With the development of artificial intelligence technology,AI computer-aided diagnosis has found certain applications in the field of dermatology.However,due to the vast variety and complex manifestations of skin dise...With the development of artificial intelligence technology,AI computer-aided diagnosis has found certain applications in the field of dermatology.However,due to the vast variety and complex manifestations of skin diseases,the specific mechanisms underlying AI computer-aided diagnosis in this context still require further exploration.Therefore,this paper,based on the imaging characteristics of skin diseases,elucidates the technical principles of AI computer-aided diagnosis and analyzes the practical application effects of AI in the diagnostic process of skin diseases.This provides new data support and methodological foundations for clinical teaching and research on skin diseases.展开更多
The aim of this study was to prospectively assess the accuracy gain of Bayesian analysis-based computeraided diagnosis(CAD) vs human judgment alone in characterizing solitary pulmonary nodules(SPNs) at computed tomogr...The aim of this study was to prospectively assess the accuracy gain of Bayesian analysis-based computeraided diagnosis(CAD) vs human judgment alone in characterizing solitary pulmonary nodules(SPNs) at computed tomography(CT). The study included 100 randomly selected SPNs with a definitive diagnosis. Nodule features at first and follow-up CT scans as well as clinical data were evaluated individually on a 1 to 5 points risk chart by 7 radiologists, firstly blinded then aware of Bayesian Inference Malignancy Calculator(BIMC) model predictions. Raters' predictions were evaluated by means of receiver operating characteristic(ROC) curve analysis and decision analysis. Overall ROC area under the curve was 0.758 before and 0.803 after the disclosure of CAD predictions(P = 0.003). A net gain in diagnostic accuracy was found in 6 out of 7 readers. Mean risk class of benign nodules dropped from 2.48 to 2.29, while mean risk class of malignancies rose from 3.66 to 3.92. Awareness of CAD predictions also determined a significant drop on mean indeterminate SPNs(15 vs 23.86 SPNs) and raised the mean number of correct and confident diagnoses(mean 39.57 vs 25.71 SPNs). This study provides evidence supporting the integration of the Bayesian analysis-based BIMC model in SPN characterization.展开更多
Age-related Macular Degeneration(AMD)and Diabetic Macular Edema(DME)are two com-mon retinal diseases for elder people that may ultimately cause irreversible blindness.Timely and accurate diagnosis is essential for the...Age-related Macular Degeneration(AMD)and Diabetic Macular Edema(DME)are two com-mon retinal diseases for elder people that may ultimately cause irreversible blindness.Timely and accurate diagnosis is essential for the treatment of these diseases.In recent years,computer-aided diagnosis(CAD)has been deeply investigated and effectively used for rapid and early diagnosis.In this paper,we proposed a method of CAD using vision transformer to analyze optical co-herence tomography(OCT)images and to automatically discriminate AMD,DME,and normal eyes.A classification accuracy of 99.69%was achieved.After the model pruning,the recognition time reached 0.010 s and the classification accuracy did not drop.Compared with the Con-volutional Neural Network(CNN)image classification models(VGG16,Resnet50,Densenet121,and EfficientNet),vision transformer after pruning exhibited better recognition ability.Results show that vision transformer is an improved alternative to diagnose retinal diseases more accurately.展开更多
AIM: To further improve the endoscopic detection of intestinal mucosa alterations due to celiac disease(CD).METHODS: We assessed a hybrid approach based on the integration of expert knowledge into the computerbased cl...AIM: To further improve the endoscopic detection of intestinal mucosa alterations due to celiac disease(CD).METHODS: We assessed a hybrid approach based on the integration of expert knowledge into the computerbased classification pipeline. A total of 2835 endoscopic images from the duodenum were recorded in 290 children using the modified immersion technique(MIT). These children underwent routine upper endoscopy for suspected CD or non-celiac upper abdominal symptoms between August 2008 and December 2014. Blinded to the clinical data and biopsy results, three medical experts visually classified each image as normal mucosa(Marsh-0) or villous atrophy(Marsh-3). The experts' decisions were further integrated into state-of-the-arttexture recognition systems. Using the biopsy results as the reference standard, the classification accuracies of this hybrid approach were compared to the experts' diagnoses in 27 different settings.RESULTS: Compared to the experts' diagnoses, in 24 of 27 classification settings(consisting of three imaging modalities, three endoscopists and three classification approaches), the best overall classification accuracies were obtained with the new hybrid approach. In 17 of 24 classification settings, the improvements achieved with the hybrid approach were statistically significant(P < 0.05). Using the hybrid approach classification accuracies between 94% and 100% were obtained. Whereas the improvements are only moderate in the case of the most experienced expert, the results of the less experienced expert could be improved significantly in 17 out of 18 classification settings. Furthermore, the lowest classification accuracy, based on the combination of one database and one specific expert, could be improved from 80% to 95%(P < 0.001).CONCLUSION: The overall classification performance of medical experts, especially less experienced experts, can be boosted significantly by integrating expert knowledge into computer-aided diagnosis systems.展开更多
Computer-aided diagnosis(CAD) has become one of the major research subjects in medical imaging and diagnostic radiology.The basic concept of CAD is to provide computer output as a second opinion to assist radiologists...Computer-aided diagnosis(CAD) has become one of the major research subjects in medical imaging and diagnostic radiology.The basic concept of CAD is to provide computer output as a second opinion to assist radiologists' image interpretations by improving the accuracy and consistency of radiologic diagnosis and also by reducing the image-reading time.To date,research on CAD in ultrasound(US)-based diagnosis has been carried out mostly for breast lesions and has been limited in the fields of gastroenterology and hepatology,with most studies being conducted using B-mode US images.Two CAD schemes with contrast-enhanced US(CEUS) that are used in classifying focal liver lesions(FLLs) as liver metastasis,hemangioma,or three histologically differentiated types of hepatocellular carcinoma(HCC) are introduced in this article:one is based on physicians' subjective pattern classifications(subjective analysis) and the other is a computerized scheme for classification of FLLs(quantitative analysis).Classification accuracies for FLLs for each CAD scheme were 84.8% and 88.5% for metastasis,93.3% and 93.8% for hemangioma,and 98.6% and 86.9% for all HCCs,respectively.In addition,the classification accuracies for histologic differentiation of HCCs were 65.2% and 79.2% for well-differentiated HCCs,41.7% and 50.0% for moderately differentiated HCCs,and 80.0% and 77.8% for poorly differentiated HCCs,respectively.There are a number of issues concerning the clinical application of CAD for CEUS,however,it is likely that CAD for CEUS of the liver will make great progress in the future.展开更多
The Brain Tumor(BT)is created by an uncontrollable rise of anomalous cells in brain tissue,and it consists of 2 types of cancers they are malignant and benign tumors.The benevolent BT does not affect the neighbouring ...The Brain Tumor(BT)is created by an uncontrollable rise of anomalous cells in brain tissue,and it consists of 2 types of cancers they are malignant and benign tumors.The benevolent BT does not affect the neighbouring healthy and normal tissue;however,the malignant could affect the adjacent brain tissues,which results in death.Initial recognition of BT is highly significant to protecting the patient’s life.Generally,the BT can be identified through the magnetic resonance imaging(MRI)scanning technique.But the radiotherapists are not offering effective tumor segmentation in MRI images because of the position and unequal shape of the tumor in the brain.Recently,ML has prevailed against standard image processing techniques.Several studies denote the superiority of machine learning(ML)techniques over standard techniques.Therefore,this study develops novel brain tumor detection and classification model using met heuristic optimization with machine learning(BTDC-MOML)model.To accomplish the detection of brain tumor effectively,a Computer-Aided Design(CAD)model using Machine Learning(ML)technique is proposed in this research manuscript.Initially,the input image pre-processing is performed using Gaborfiltering(GF)based noise removal,contrast enhancement,and skull stripping.Next,mayfly optimization with the Kapur’s thresholding based segmentation process takes place.For feature extraction proposes,local diagonal extreme patterns(LDEP)are exploited.At last,the Extreme Gradient Boosting(XGBoost)model can be used for the BT classification process.The accuracy analysis is performed in terms of Learning accuracy,and the validation accuracy is performed to determine the efficiency of the proposed research work.The experimental validation of the proposed model demonstrates its promising performance over other existing methods.展开更多
Computer-aided diagnosis(CAD)models exploit artificial intelligence(AI)for chest X-ray(CXR)examination to identify the presence of tuberculosis(TB)and can improve the feasibility and performance of CXR for TB screenin...Computer-aided diagnosis(CAD)models exploit artificial intelligence(AI)for chest X-ray(CXR)examination to identify the presence of tuberculosis(TB)and can improve the feasibility and performance of CXR for TB screening and triage.At the same time,CXR interpretation is a time-consuming and subjective process.Furthermore,high resemblance among the radiological patterns of TB and other lung diseases can result in misdiagnosis.Therefore,computer-aided diagnosis(CAD)models using machine learning(ML)and deep learning(DL)can be designed for screening TB accurately.With this motivation,this article develops a Water Strider Optimization with Deep Transfer Learning Enabled Tuberculosis Classification(WSODTL-TBC)model on Chest X-rays(CXR).The presented WSODTL-TBC model aims to detect and classify TB on CXR images.Primarily,the WSODTL-TBC model undergoes image filtering techniques to discard the noise content and U-Net-based image segmentation.Besides,a pre-trained residual network with a two-dimensional convolutional neural network(2D-CNN)model is applied to extract feature vectors.In addition,the WSO algorithm with long short-term memory(LSTM)model was employed for identifying and classifying TB,where the WSO algorithm is applied as a hyperparameter optimizer of the LSTM methodology,showing the novelty of the work.The performance validation of the presented WSODTL-TBC model is carried out on the benchmark dataset,and the outcomes were investigated in many aspects.The experimental development pointed out the betterment of the WSODTL-TBC model over existing algorithms.展开更多
Objective: Computer classification of sonographic BI-RADS features can aid differentiation of the malignant and benign masses. However, the variability in the diagnosis due to the differences in the observed features ...Objective: Computer classification of sonographic BI-RADS features can aid differentiation of the malignant and benign masses. However, the variability in the diagnosis due to the differences in the observed features between the observations is not known. The goal of this study is to measure the variation in sonographic features between multiple observations and determine the effect of features variation on computer-aided diagnosis of the breast masses. Materials and Methods: Ultrasound images of biopsy proven solid breast masses were analyzed in three independent observations for BI-RADS sonographic features. The BI-RADS features from each observation were used with Bayes classifier to determine probability of malignancy. The observer agreement in the sonographic features was measured by kappa coefficient and the difference in the diagnostic performances between observations was determined by the area under the ROC curve, Az, and interclass correlation coefficient. Results: While some features were repeatedly observed, κ = 0.95, other showed a significant variation, κ = 0.16. For all features, combined intra-observer agreement was substantial, κ = 0.77. The agreement, however, decreased steadily to 0.66 and 0.56 as time between the observations increased from 1 to 2 and 3 months, respectively. Despite the variation in features between observations the probabilities of malignancy estimates from Bayes classifier were robust and consistently yielded same level of diagnostic performance, Az was 0.772-0.817 for sonographic features alone and 0.828-0.849 for sonographic features and age combined. The difference in the performance, ΔAz, between the observations for the two groups was small (0.003-0.044) and was not statistically significant (p < 0.05). Interclass correlation coefficient for the observations was 0.822 (CI: 0.787-0.853) for BI-RADS sonographic features alone and for those combined with age was 0.833 (CI: 0.800-0.862). Conclusion: Despite the differences in the BI-RADS sonographic features between different observations, the diagnostic performance of computer-aided analysis for differentiating breast masses did not change. Through continual retraining, the computer-aided analysis provides consistent diagnostic performance independent of the variations in the observed sonographic features.展开更多
Objective To evaluate and reduce inter-observer variations in the detection and characterization of pulmonary nodules on digital radiograph （DR） chest images. Methods Two hundreds and thirty-two new posterior-anteri...Objective To evaluate and reduce inter-observer variations in the detection and characterization of pulmonary nodules on digital radiograph （DR） chest images. Methods Two hundreds and thirty-two new posterior-anterior DR chest images were collected from out-patient screening patients. Consensus was reached by two experienced radiologists on the marking, rating, and segmentation of small actionable nodules ranged from 5 to 15 mm in diameter using a computer-aided diagnosis （CAD） system. Both their own nodule findings and the computer＇s automatic nodule detection results were analyzed to make the consensus. Nodules identified together with corresponding likelihood rating and segmentation results were referred as ＂Gold Stand- ard＂. Two un-experienced radiologists were asked to first mark and characterize suspicious nodules independently, then were allowed to consult the computer nodule detection results and change their decisions. Results Large inter-observer variations in pulmonary nodule identification and characterization on DR chest images were observed between un-experienced radiologists. Un-expefienced radiologists could greatly benefit from the CAD system, including substantial decrease of inter-observer variation and improvement of nodule detection rates. Moreover, radiologists with different levels of skillfulness could achieve similar high level performance after using the CAD system. Conclusion The CAD system shows a high potential for providing a valuable assistance to the examination of DR chest images.展开更多
Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues,particularly in the field of lung disease diagnosis.One promising avenue involves the use of chest X-Rays,w...Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues,particularly in the field of lung disease diagnosis.One promising avenue involves the use of chest X-Rays,which are commonly utilized in radiology.To fully exploit their potential,researchers have suggested utilizing deep learning methods to construct computer-aided diagnostic systems.However,constructing and compressing these systems presents a significant challenge,as it relies heavily on the expertise of data scientists.To tackle this issue,we propose an automated approach that utilizes an evolutionary algorithm(EA)to optimize the design and compression of a convolutional neural network(CNN)for X-Ray image classification.Our approach accurately classifies radiography images and detects potential chest abnormalities and infections,including COVID-19.Furthermore,our approach incorporates transfer learning,where a pre-trainedCNNmodel on a vast dataset of chest X-Ray images is fine-tuned for the specific task of detecting COVID-19.This method can help reduce the amount of labeled data required for the task and enhance the overall performance of the model.We have validated our method via a series of experiments against state-of-the-art architectures.展开更多
Diabetic Retinopathy(DR)is a significant blinding disease that poses serious threat to human vision rapidly.Classification and severity grading of DR are difficult processes to accomplish.Traditionally,it depends on o...Diabetic Retinopathy(DR)is a significant blinding disease that poses serious threat to human vision rapidly.Classification and severity grading of DR are difficult processes to accomplish.Traditionally,it depends on ophthalmoscopically-visible symptoms of growing severity,which is then ranked in a stepwise scale from no retinopathy to various levels of DR severity.This paper presents an ensemble of Orthogonal Learning Particle Swarm Optimization(OPSO)algorithm-based Convolutional Neural Network(CNN)Model EOPSO-CNN in order to perform DR detection and grading.The proposed EOPSO-CNN model involves three main processes such as preprocessing,feature extraction,and classification.The proposed model initially involves preprocessing stage which removes the presence of noise in the input image.Then,the watershed algorithm is applied to segment the preprocessed images.Followed by,feature extraction takes place by leveraging EOPSO-CNN model.Finally,the extracted feature vectors are provided to a Decision Tree(DT)classifier to classify the DR images.The study experiments were carried out using Messidor DR Dataset and the results showed an extraordinary performance by the proposed method over compared methods in a considerable way.The simulation outcome offered the maximum classification with accuracy,sensitivity,and specificity values being 98.47%,96.43%,and 99.02%respectively.展开更多
Diabetic retinopathy(DR)diagnosis through digital fundus images requires clinical experts to recognize the presence and importance of many intricate features.This task is very difficult for ophthalmologists and timeco...Diabetic retinopathy(DR)diagnosis through digital fundus images requires clinical experts to recognize the presence and importance of many intricate features.This task is very difficult for ophthalmologists and timeconsuming.Therefore,many computer-aided diagnosis(CAD)systems were developed to automate this screening process ofDR.In this paper,aCAD-DR system is proposed based on preprocessing and a pre-train transfer learningbased convolutional neural network(PCNN)to recognize the five stages of DR through retinal fundus images.To develop this CAD-DR system,a preprocessing step is performed in a perceptual-oriented color space to enhance the DR-related lesions and then a standard pre-train PCNN model is improved to get high classification results.The architecture of the PCNN model is based on three main phases.Firstly,the training process of the proposed PCNN is accomplished by using the expected gradient length(EGL)to decrease the image labeling efforts during the training of the CNN model.Secondly,themost informative patches and images were automatically selected using a few pieces of training labeled samples.Thirdly,the PCNN method generated useful masks for prognostication and identified regions of interest.Fourthly,the DR-related lesions involved in the classification task such as micro-aneurysms,hemorrhages,and exudates were detected and then used for recognition of DR.The PCNN model is pre-trained using a high-end graphical processor unit(GPU)on the publicly available Kaggle benchmark.The obtained results demonstrate that the CAD-DR system outperforms compared to other state-of-the-art in terms of sensitivity(SE),specificity(SP),and accuracy(ACC).On the test set of 30,000 images,the CAD-DR system achieved an average SE of 93.20%,SP of 96.10%,and ACC of 98%.This result indicates that the proposed CAD-DR system is appropriate for the screening of the severity-level of DR.展开更多
Aging is a natural process that leads to debility,disease,and dependency.Alzheimer’s disease(AD)causes degeneration of the brain cells leading to cognitive decline and memory loss,as well as dependence on others to f...Aging is a natural process that leads to debility,disease,and dependency.Alzheimer’s disease(AD)causes degeneration of the brain cells leading to cognitive decline and memory loss,as well as dependence on others to fulfill basic daily needs.AD is the major cause of dementia.Computer-aided diagnosis(CADx)tools aid medical practitioners in accurately identifying diseases such as AD in patients.This study aimed to develop a CADx tool for the early detection of AD using the Intelligent Water Drop(IWD)algorithm and the Random Forest(RF)classifier.The IWD algorithm an efficient feature selection method,was used to identify the most deterministic features of AD in the dataset.RF is an ensemble method that leverages multiple weak learners to classify a patient’s disease as either demented(DN)or cognitively normal(CN).The proposed tool also classifies patients as mild cognitive impairment(MCI)or CN.The dataset on which the performance of the proposed CADx was evaluated was sourced from the Alzheimer’s Disease Neuroimaging Initiative(ADNI).The RF ensemble method achieves 100%accuracy in identifying DN patients from CN patients.The classification accuracy for classifying patients as MCI or CN is 92%.This study emphasizes the significance of pre-processing prior to classification to improve the classification results of the proposed CADx tool.展开更多
Deep neural network(DNN)based computer-aided breast tumor diagnosis(CABTD)method plays a vital role in the early detection and diagnosis of breast tumors.However,a Brightness mode(B-mode)ultrasound image derives train...Deep neural network(DNN)based computer-aided breast tumor diagnosis(CABTD)method plays a vital role in the early detection and diagnosis of breast tumors.However,a Brightness mode(B-mode)ultrasound image derives training feature samples that make closer isolation toward the infection part.Hence,it is expensive due to a metaheuristic search of features occupying the global region of interest(ROI)structures of input images.Thus,it may lead to the high computational complexity of the pre-trained DNN-based CABTD method.This paper proposes a novel ensemble pretrained DNN-based CABTD method using global-and local-ROI-structures of B-mode ultrasound images.It conveys the additional consideration of a local-ROI-structures for further enhan-cing the pretrained DNN-based CABTD method’s breast tumor diagnostic performance without degrading its visual quality.The features are extracted at various depths(18,50,and 101)from the global and local ROI structures and feed to support vector machine for better classification.From the experimental results,it has been observed that the combined local and global ROI structure of small depth residual network ResNet18(0.8 in%)has produced significant improve-ment in pixel ratio as compared to ResNet50(0.5 in%)and ResNet101(0.3 in%),respectively.Subsequently,the pretrained DNN-based CABTD methods have been tested by influencing local and global ROI structures to diagnose two specific breast tumors(Benign and Malignant)and improve the diagnostic accuracy(86%)compared to Dense Net,Alex Net,VGG Net,and Google Net.Moreover,it reduces the computational complexity due to the small depth residual network ResNet18,respectively.展开更多
BACKGROUND Computer-aided diagnosis(CAD)may assist endoscopists in identifying and classifying polyps during colonoscopy for detecting colorectal cancer.AIM To build a system using CAD to detect and classify polyps ba...BACKGROUND Computer-aided diagnosis(CAD)may assist endoscopists in identifying and classifying polyps during colonoscopy for detecting colorectal cancer.AIM To build a system using CAD to detect and classify polyps based on the Yamada classification.METHODS A total of 24045 polyp and 72367 nonpolyp images were obtained.We established a computer-aided detection and Yamada classification model based on the YOLOv7 neural network algorithm.Frame-based and image-based evaluation metrics were employed to assess the performance.RESULTS Computer-aided detection and Yamada classification screened polyps with a precision of 96.7%,a recall of 95.8%,and an F1-score of 96.2%,outperforming those of all groups of endoscopists.In regard to the Yamada classification of polyps,the CAD system displayed a precision of 82.3%,a recall of 78.5%,and an F1-score of 80.2%,outper-forming all levels of endoscopists.In addition,according to the image-based method,the CAD had an accuracy of 99.2%,a specificity of 99.5%,a sensitivity of 98.5%,a positive predictive value of 99.0%,a negative predictive value of 99.2%for polyp detection and an accuracy of 97.2%,a specificity of 98.4%,a sensitivity of 79.2%,a positive predictive value of 83.0%,and a negative predictive value of 98.4%for poly Yamada classification.CONCLUSION We developed a novel CAD system based on a deep neural network for polyp detection,and the Yamada classi-fication outperformed that of nonexpert endoscopists.This CAD system could help community-based hospitals enhance their effectiveness in polyp detection and classification.展开更多
Objective To investigate morphological differences between obstructive and non-obstructive coronary artery disease(CAD)patients using computer-aided image analysis,and identify color and texture features for tradition...Objective To investigate morphological differences between obstructive and non-obstructive coronary artery disease(CAD)patients using computer-aided image analysis,and identify color and texture features for traditional Chinese medicine(TCM)syndrome differentiation.Methods This prospective study enrolled patients undergoing coronary computed tomography angiography(CTA)at the Affiliated Hospital of Liaoning University of Traditional Chinese Medicine between May 1,2024 and August 7,2025.Based on CTA results,patients were categorized into obstructive CAD and non-obstructive CAD groups.Standardized tongue images were acquired using a dedicated mobile application(Traditional Chinese Medicine Tongue Image-Assisted Diagnosis System)and analyzed for the overall tongue surface and three macroscopic features(tooth marks,fissures,and red dots)from which high-dimensional color and texture parameters were extracted.Multi-scale texture features were derived using spatial-domain Laplacian pyramid and frequency-domain wavelet transform methods.Dimensionality reduction and feature selection were performed using principal component analysis(PCA)and random forest with 5-fold cross-validation.Feature stability was assessed using Hodges-Lehmann estimator and Cliff’s δ.A multi-view XGBoost model was developed to differentiate the two groups and evaluated on a temporally independent validation set using accuracy and the area under the receiver operating characteristic curve(AUC).SHapley Additive exPlanations(SHAP)analysis was applied to interpret model decisions.Results This study analyzed 373 CAD patients,including 167 with obstructive CAD and 206 with non-obstructive CAD according to CTA results.The whole cohort was divided into training set(n=316,obstructive:non-obstructive=142:174)and validation set(n=57,obstructive:non-obstructive=25:32),with balanced baseline characteristics(P>0.05).Macroscopic tongue analysis revealed that patients with obstructive CAD had fewer tooth marks[odds ratio(OR)=0.43,P<0.05]and red dots(OR=0.46,P<0.05).High-dimensional color analysis identified pronounced intergroup differences,most notably a reduction in hue values in the hue-saturation-intensity(HSI)color space among obstructive CAD patients(Cliff’s δ=-0.31,P=2.72×10^(-6);Hodges-Lehmann estimator:-0.31).PCA results suggested that tongue surface features explained the highest proportion of variance(48.2%).Random forest screening identified 77 stable features across all tongue regions,with wavelet-transformed texture features demonstrating the highest importance.The multi-view XGBoost fusion model achieved an accuracy of 75%and an AUC of 0.779 in the independent validation set.SHAP analysis identified the wavelet-based feature-left-handed lower-level gray-level size zone matrix zone variance(LHL_glszm_ZoneVariance)as the top predictor,accounting for 40.6%of the model's decision variance,and indicated that 85.3%of the predictive power came from wavelet-based texture features.Conclusion This study has provided objective evidence for the TCM concept that“the tongue reflects the heart”by identifying distinct morphological and colorimetric tongue patterns in patients with obstructive CAD through artificial intelligence(AI)-driven image analysis,and the promising performance of the computational model suggests its potential as a non-invasive adjunctive tool for CAD assessment.展开更多
Jaundice,common condition in newborns,is characterized by yellowing of the skin and eyes due to elevated levels of bilirubin in the blood.Timely detection and management of jaundice are crucial to prevent potential co...Jaundice,common condition in newborns,is characterized by yellowing of the skin and eyes due to elevated levels of bilirubin in the blood.Timely detection and management of jaundice are crucial to prevent potential complications.Traditional jaundice assessment methods rely on visual inspection or invasive blood tests that are subjective and painful for infants,respectively.Although several automated methods for jaundice detection have been developed during the past few years,a limited number of reviews consolidating these developments have been presented till date,making it essential to systematically evaluate and present the existing advancements.This paper fills this gap by providing a thorough survey of automated methods for jaundice detection in neonates.The primary focus of the survey is to review the existing methodologies,techniques,and technologies used for neonatal jaundice detection.The key findings from the review indicate that image-based bilirubinometers and transcutaneous bilirubinometers are promising non-invasive alternatives,and provide a good trade-off between accuracy and ease of use.However,their effectiveness varies with factors like skin pigmentation,gestational age,and measurement site.Spectroscopic and biosensor-based techniques show high sensitivity but need further clinical validation.Despite advancements,several challenges including device calibration,large-scale validation,and regulatory barriers still haunt the researchers.Standardization,regulatory compliances,and seamless integration into healthcare workflows are the key hurdles to be addressed.By consolidating the current knowledge and discussing the challenges and opportunities in this field,this survey aims to contribute to the advancement of automatic jaundice detection and ultimately improve neonatal care.展开更多
BACKGROUND Dual-phenotype hepatocellular carcinoma(HCC)is a relatively new subtype of HCC.Studies have shown that in the context of chronic hepatitis,liver cirrhosis,and other liver conditions,some intrahepatic cholan...BACKGROUND Dual-phenotype hepatocellular carcinoma(HCC)is a relatively new subtype of HCC.Studies have shown that in the context of chronic hepatitis,liver cirrhosis,and other liver conditions,some intrahepatic cholangiocarcinomas(ICCs)exhibit an enhancement pattern similar to that of HCC.Both dual-phenotype HCC(DPHCC)and ICC can express biliary markers,making imaging and pathology differentiation difficult.Currently,radiomics is widely used in the differentiation,clinical staging,and prognosis assessment of various diseases.Radiomics can effectively differentiate DPHCC and ICC preoperatively.AIM To evaluate the value of radiomics in the differential diagnosis of DPHCC and ICC and to validate its clinical applicability METHODS In this retrospective study,the data of 53 DPHCC patients and 124 ICC patients were collected retrospectively and randomly divided into training and testing sets at a ratio of 7:3.After delineation of regions of interest and feature extraction and selection,radiomics models were constructed.Receiver operating characteristic curve analysis was conducted to calculate the area under the curve(AUC)for each model.The AUC values of radiologists with and without assistance from the model were also assessed.RESULTS In the training set,the AUC value of the radiomic model was the highest,and the combined model and the radiomic model had similar AUC(P>0.05);the differences in the AUC values between the combined model and the clinical-sign model was statistically significant(P<0.05).In the testing set,the AUC value of the combined model was the highest,and the differences in the AUC values between the combined model and the clinical-sign model was statistically significant(P<0.05).With model assistance,the AUC values of Doctor D(10 years of experience in abdominal imaging diagnosis)and Doctor E(5 years of experience in abdominal imaging diagnosis)both increased.CONCLUSION Radiomics can differentiate DPHCC and ICC,and with assistance from the developed model,the accuracy of less experienced doctors in the differential diagnosis of these two diseases can be improved.展开更多
Coronary artery disease is a highly lethal cardiovascular condition,making early diagnosis crucial for patients.Echocardiograph is employed to identify coronary heart disease(CHD).However,due to issues such as fuzzy o...Coronary artery disease is a highly lethal cardiovascular condition,making early diagnosis crucial for patients.Echocardiograph is employed to identify coronary heart disease(CHD).However,due to issues such as fuzzy object boundaries,complex tissue structures,and motion artifacts in ultrasound images,it is challenging to detect CHD accurately.This paper proposes an improved Transformer model based on the Feedback Self-Attention Mechanism(FSAM)for classification of ultrasound images.The model enhances attention weights,making it easier to capture complex features.Experimental results show that the proposed method achieves high levels of accuracy,recall,precision,F1 score,and area under the receiver operating characteristic curve(72.3%,79.5%,82.0%,81.0%,and 0.73%,respectively).The proposed model was compared with widely used models,including convolutional neural network and visual Transformer model,and the results show that our model outperforms others in the above evaluation metrics.In conclusion,the proposed model provides a promising approach for diagnosing CHD using echocardiogram.展开更多
BACKGROUND Upper gastrointestinal endoscopy is critical for esophageal squamous cell carcinoma(ESCC)detection;however,endoscopists require long-term training to avoid missing superficial lesions.AIM To develop a deep ...BACKGROUND Upper gastrointestinal endoscopy is critical for esophageal squamous cell carcinoma(ESCC)detection;however,endoscopists require long-term training to avoid missing superficial lesions.AIM To develop a deep learning computer-assisted diagnosis(CAD)system for endoscopic detection of superficial ESCC and investigate its application value.METHODS We configured the CAD system for white-light and narrow-band imaging modes based on the YOLO v5 algorithm.A total of 4447 images from 837 patients and 1695 images from 323 patients were included in the training and testing datasets,respectively.Two experts and two non-expert endoscopists reviewed the testing dataset independently and with computer assistance.The diagnostic performance was evaluated in terms of the area under the receiver operating characteristic curve,accuracy,sensitivity,and specificity.RESULTS The area under the receiver operating characteristics curve,accuracy,sensitivity,and specificity of the CAD system were 0.982[95%confidence interval(CI):0.969-0.994],92.9%(95%CI:89.5%-95.2%),91.9%(95%CI:87.4%-94.9%),and 94.7%(95%CI:89.0%-97.6%),respectively.The accuracy of CAD was significantly higher than that of non-expert endoscopists(78.3%,P<0.001 compared with CAD)and comparable to that of expert endoscopists(91.0%,P=0.129 compared with CAD).After referring to the CAD results,the accuracy of the non-expert endoscopists significantly improved(88.2%vs 78.3%,P<0.001).Lesions with Paris classification type 0-IIb were more likely to be inaccurately identified by the CAD system.CONCLUSION The diagnostic performance of the CAD system is promising and may assist in improving detectability,particularly for inexperienced endoscopists.展开更多
文摘With the development of artificial intelligence technology,AI computer-aided diagnosis has found certain applications in the field of dermatology.However,due to the vast variety and complex manifestations of skin diseases,the specific mechanisms underlying AI computer-aided diagnosis in this context still require further exploration.Therefore,this paper,based on the imaging characteristics of skin diseases,elucidates the technical principles of AI computer-aided diagnosis and analyzes the practical application effects of AI in the diagnostic process of skin diseases.This provides new data support and methodological foundations for clinical teaching and research on skin diseases.
文摘The aim of this study was to prospectively assess the accuracy gain of Bayesian analysis-based computeraided diagnosis(CAD) vs human judgment alone in characterizing solitary pulmonary nodules(SPNs) at computed tomography(CT). The study included 100 randomly selected SPNs with a definitive diagnosis. Nodule features at first and follow-up CT scans as well as clinical data were evaluated individually on a 1 to 5 points risk chart by 7 radiologists, firstly blinded then aware of Bayesian Inference Malignancy Calculator(BIMC) model predictions. Raters' predictions were evaluated by means of receiver operating characteristic(ROC) curve analysis and decision analysis. Overall ROC area under the curve was 0.758 before and 0.803 after the disclosure of CAD predictions(P = 0.003). A net gain in diagnostic accuracy was found in 6 out of 7 readers. Mean risk class of benign nodules dropped from 2.48 to 2.29, while mean risk class of malignancies rose from 3.66 to 3.92. Awareness of CAD predictions also determined a significant drop on mean indeterminate SPNs(15 vs 23.86 SPNs) and raised the mean number of correct and confident diagnoses(mean 39.57 vs 25.71 SPNs). This study provides evidence supporting the integration of the Bayesian analysis-based BIMC model in SPN characterization.
基金This work was supported by the Science and Technology innovation project of Shanghai Science and Technology Commission(19441905800)the Natural National Science Foundation of China(62175156,81827807,8210041176,82101177,61675134)+1 种基金the Project of State Key Laboratory of Ophthalmology,Optometry and Visual Science,Wenzhou Medical University(K181002)the Key R&D Program Projects in Zhejiang Province(2019C03045).
文摘Age-related Macular Degeneration(AMD)and Diabetic Macular Edema(DME)are two com-mon retinal diseases for elder people that may ultimately cause irreversible blindness.Timely and accurate diagnosis is essential for the treatment of these diseases.In recent years,computer-aided diagnosis(CAD)has been deeply investigated and effectively used for rapid and early diagnosis.In this paper,we proposed a method of CAD using vision transformer to analyze optical co-herence tomography(OCT)images and to automatically discriminate AMD,DME,and normal eyes.A classification accuracy of 99.69%was achieved.After the model pruning,the recognition time reached 0.010 s and the classification accuracy did not drop.Compared with the Con-volutional Neural Network(CNN)image classification models(VGG16,Resnet50,Densenet121,and EfficientNet),vision transformer after pruning exhibited better recognition ability.Results show that vision transformer is an improved alternative to diagnose retinal diseases more accurately.
基金Supported by the Austrian Science Fund(FWF),No.KLI 429-B13 to Vécsei A
文摘AIM: To further improve the endoscopic detection of intestinal mucosa alterations due to celiac disease(CD).METHODS: We assessed a hybrid approach based on the integration of expert knowledge into the computerbased classification pipeline. A total of 2835 endoscopic images from the duodenum were recorded in 290 children using the modified immersion technique(MIT). These children underwent routine upper endoscopy for suspected CD or non-celiac upper abdominal symptoms between August 2008 and December 2014. Blinded to the clinical data and biopsy results, three medical experts visually classified each image as normal mucosa(Marsh-0) or villous atrophy(Marsh-3). The experts' decisions were further integrated into state-of-the-arttexture recognition systems. Using the biopsy results as the reference standard, the classification accuracies of this hybrid approach were compared to the experts' diagnoses in 27 different settings.RESULTS: Compared to the experts' diagnoses, in 24 of 27 classification settings(consisting of three imaging modalities, three endoscopists and three classification approaches), the best overall classification accuracies were obtained with the new hybrid approach. In 17 of 24 classification settings, the improvements achieved with the hybrid approach were statistically significant(P < 0.05). Using the hybrid approach classification accuracies between 94% and 100% were obtained. Whereas the improvements are only moderate in the case of the most experienced expert, the results of the less experienced expert could be improved significantly in 17 out of 18 classification settings. Furthermore, the lowest classification accuracy, based on the combination of one database and one specific expert, could be improved from 80% to 95%(P < 0.001).CONCLUSION: The overall classification performance of medical experts, especially less experienced experts, can be boosted significantly by integrating expert knowledge into computer-aided diagnosis systems.
文摘Computer-aided diagnosis(CAD) has become one of the major research subjects in medical imaging and diagnostic radiology.The basic concept of CAD is to provide computer output as a second opinion to assist radiologists' image interpretations by improving the accuracy and consistency of radiologic diagnosis and also by reducing the image-reading time.To date,research on CAD in ultrasound(US)-based diagnosis has been carried out mostly for breast lesions and has been limited in the fields of gastroenterology and hepatology,with most studies being conducted using B-mode US images.Two CAD schemes with contrast-enhanced US(CEUS) that are used in classifying focal liver lesions(FLLs) as liver metastasis,hemangioma,or three histologically differentiated types of hepatocellular carcinoma(HCC) are introduced in this article:one is based on physicians' subjective pattern classifications(subjective analysis) and the other is a computerized scheme for classification of FLLs(quantitative analysis).Classification accuracies for FLLs for each CAD scheme were 84.8% and 88.5% for metastasis,93.3% and 93.8% for hemangioma,and 98.6% and 86.9% for all HCCs,respectively.In addition,the classification accuracies for histologic differentiation of HCCs were 65.2% and 79.2% for well-differentiated HCCs,41.7% and 50.0% for moderately differentiated HCCs,and 80.0% and 77.8% for poorly differentiated HCCs,respectively.There are a number of issues concerning the clinical application of CAD for CEUS,however,it is likely that CAD for CEUS of the liver will make great progress in the future.
文摘The Brain Tumor(BT)is created by an uncontrollable rise of anomalous cells in brain tissue,and it consists of 2 types of cancers they are malignant and benign tumors.The benevolent BT does not affect the neighbouring healthy and normal tissue;however,the malignant could affect the adjacent brain tissues,which results in death.Initial recognition of BT is highly significant to protecting the patient’s life.Generally,the BT can be identified through the magnetic resonance imaging(MRI)scanning technique.But the radiotherapists are not offering effective tumor segmentation in MRI images because of the position and unequal shape of the tumor in the brain.Recently,ML has prevailed against standard image processing techniques.Several studies denote the superiority of machine learning(ML)techniques over standard techniques.Therefore,this study develops novel brain tumor detection and classification model using met heuristic optimization with machine learning(BTDC-MOML)model.To accomplish the detection of brain tumor effectively,a Computer-Aided Design(CAD)model using Machine Learning(ML)technique is proposed in this research manuscript.Initially,the input image pre-processing is performed using Gaborfiltering(GF)based noise removal,contrast enhancement,and skull stripping.Next,mayfly optimization with the Kapur’s thresholding based segmentation process takes place.For feature extraction proposes,local diagonal extreme patterns(LDEP)are exploited.At last,the Extreme Gradient Boosting(XGBoost)model can be used for the BT classification process.The accuracy analysis is performed in terms of Learning accuracy,and the validation accuracy is performed to determine the efficiency of the proposed research work.The experimental validation of the proposed model demonstrates its promising performance over other existing methods.
文摘Computer-aided diagnosis(CAD)models exploit artificial intelligence(AI)for chest X-ray(CXR)examination to identify the presence of tuberculosis(TB)and can improve the feasibility and performance of CXR for TB screening and triage.At the same time,CXR interpretation is a time-consuming and subjective process.Furthermore,high resemblance among the radiological patterns of TB and other lung diseases can result in misdiagnosis.Therefore,computer-aided diagnosis(CAD)models using machine learning(ML)and deep learning(DL)can be designed for screening TB accurately.With this motivation,this article develops a Water Strider Optimization with Deep Transfer Learning Enabled Tuberculosis Classification(WSODTL-TBC)model on Chest X-rays(CXR).The presented WSODTL-TBC model aims to detect and classify TB on CXR images.Primarily,the WSODTL-TBC model undergoes image filtering techniques to discard the noise content and U-Net-based image segmentation.Besides,a pre-trained residual network with a two-dimensional convolutional neural network(2D-CNN)model is applied to extract feature vectors.In addition,the WSO algorithm with long short-term memory(LSTM)model was employed for identifying and classifying TB,where the WSO algorithm is applied as a hyperparameter optimizer of the LSTM methodology,showing the novelty of the work.The performance validation of the presented WSODTL-TBC model is carried out on the benchmark dataset,and the outcomes were investigated in many aspects.The experimental development pointed out the betterment of the WSODTL-TBC model over existing algorithms.
文摘Objective: Computer classification of sonographic BI-RADS features can aid differentiation of the malignant and benign masses. However, the variability in the diagnosis due to the differences in the observed features between the observations is not known. The goal of this study is to measure the variation in sonographic features between multiple observations and determine the effect of features variation on computer-aided diagnosis of the breast masses. Materials and Methods: Ultrasound images of biopsy proven solid breast masses were analyzed in three independent observations for BI-RADS sonographic features. The BI-RADS features from each observation were used with Bayes classifier to determine probability of malignancy. The observer agreement in the sonographic features was measured by kappa coefficient and the difference in the diagnostic performances between observations was determined by the area under the ROC curve, Az, and interclass correlation coefficient. Results: While some features were repeatedly observed, κ = 0.95, other showed a significant variation, κ = 0.16. For all features, combined intra-observer agreement was substantial, κ = 0.77. The agreement, however, decreased steadily to 0.66 and 0.56 as time between the observations increased from 1 to 2 and 3 months, respectively. Despite the variation in features between observations the probabilities of malignancy estimates from Bayes classifier were robust and consistently yielded same level of diagnostic performance, Az was 0.772-0.817 for sonographic features alone and 0.828-0.849 for sonographic features and age combined. The difference in the performance, ΔAz, between the observations for the two groups was small (0.003-0.044) and was not statistically significant (p < 0.05). Interclass correlation coefficient for the observations was 0.822 (CI: 0.787-0.853) for BI-RADS sonographic features alone and for those combined with age was 0.833 (CI: 0.800-0.862). Conclusion: Despite the differences in the BI-RADS sonographic features between different observations, the diagnostic performance of computer-aided analysis for differentiating breast masses did not change. Through continual retraining, the computer-aided analysis provides consistent diagnostic performance independent of the variations in the observed sonographic features.
文摘Objective To evaluate and reduce inter-observer variations in the detection and characterization of pulmonary nodules on digital radiograph （DR） chest images. Methods Two hundreds and thirty-two new posterior-anterior DR chest images were collected from out-patient screening patients. Consensus was reached by two experienced radiologists on the marking, rating, and segmentation of small actionable nodules ranged from 5 to 15 mm in diameter using a computer-aided diagnosis （CAD） system. Both their own nodule findings and the computer＇s automatic nodule detection results were analyzed to make the consensus. Nodules identified together with corresponding likelihood rating and segmentation results were referred as ＂Gold Stand- ard＂. Two un-experienced radiologists were asked to first mark and characterize suspicious nodules independently, then were allowed to consult the computer nodule detection results and change their decisions. Results Large inter-observer variations in pulmonary nodule identification and characterization on DR chest images were observed between un-experienced radiologists. Un-expefienced radiologists could greatly benefit from the CAD system, including substantial decrease of inter-observer variation and improvement of nodule detection rates. Moreover, radiologists with different levels of skillfulness could achieve similar high level performance after using the CAD system. Conclusion The CAD system shows a high potential for providing a valuable assistance to the examination of DR chest images.
基金via funding from Prince Sattam bin Abdulaziz University Project Number(PSAU/2023/R/1444).
文摘Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues,particularly in the field of lung disease diagnosis.One promising avenue involves the use of chest X-Rays,which are commonly utilized in radiology.To fully exploit their potential,researchers have suggested utilizing deep learning methods to construct computer-aided diagnostic systems.However,constructing and compressing these systems presents a significant challenge,as it relies heavily on the expertise of data scientists.To tackle this issue,we propose an automated approach that utilizes an evolutionary algorithm(EA)to optimize the design and compression of a convolutional neural network(CNN)for X-Ray image classification.Our approach accurately classifies radiography images and detects potential chest abnormalities and infections,including COVID-19.Furthermore,our approach incorporates transfer learning,where a pre-trainedCNNmodel on a vast dataset of chest X-Ray images is fine-tuned for the specific task of detecting COVID-19.This method can help reduce the amount of labeled data required for the task and enhance the overall performance of the model.We have validated our method via a series of experiments against state-of-the-art architectures.
文摘Diabetic Retinopathy(DR)is a significant blinding disease that poses serious threat to human vision rapidly.Classification and severity grading of DR are difficult processes to accomplish.Traditionally,it depends on ophthalmoscopically-visible symptoms of growing severity,which is then ranked in a stepwise scale from no retinopathy to various levels of DR severity.This paper presents an ensemble of Orthogonal Learning Particle Swarm Optimization(OPSO)algorithm-based Convolutional Neural Network(CNN)Model EOPSO-CNN in order to perform DR detection and grading.The proposed EOPSO-CNN model involves three main processes such as preprocessing,feature extraction,and classification.The proposed model initially involves preprocessing stage which removes the presence of noise in the input image.Then,the watershed algorithm is applied to segment the preprocessed images.Followed by,feature extraction takes place by leveraging EOPSO-CNN model.Finally,the extracted feature vectors are provided to a Decision Tree(DT)classifier to classify the DR images.The study experiments were carried out using Messidor DR Dataset and the results showed an extraordinary performance by the proposed method over compared methods in a considerable way.The simulation outcome offered the maximum classification with accuracy,sensitivity,and specificity values being 98.47%,96.43%,and 99.02%respectively.
基金Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University for funding this work through Research Group no.RG-21-07-01.
文摘Diabetic retinopathy(DR)diagnosis through digital fundus images requires clinical experts to recognize the presence and importance of many intricate features.This task is very difficult for ophthalmologists and timeconsuming.Therefore,many computer-aided diagnosis(CAD)systems were developed to automate this screening process ofDR.In this paper,aCAD-DR system is proposed based on preprocessing and a pre-train transfer learningbased convolutional neural network(PCNN)to recognize the five stages of DR through retinal fundus images.To develop this CAD-DR system,a preprocessing step is performed in a perceptual-oriented color space to enhance the DR-related lesions and then a standard pre-train PCNN model is improved to get high classification results.The architecture of the PCNN model is based on three main phases.Firstly,the training process of the proposed PCNN is accomplished by using the expected gradient length(EGL)to decrease the image labeling efforts during the training of the CNN model.Secondly,themost informative patches and images were automatically selected using a few pieces of training labeled samples.Thirdly,the PCNN method generated useful masks for prognostication and identified regions of interest.Fourthly,the DR-related lesions involved in the classification task such as micro-aneurysms,hemorrhages,and exudates were detected and then used for recognition of DR.The PCNN model is pre-trained using a high-end graphical processor unit(GPU)on the publicly available Kaggle benchmark.The obtained results demonstrate that the CAD-DR system outperforms compared to other state-of-the-art in terms of sensitivity(SE),specificity(SP),and accuracy(ACC).On the test set of 30,000 images,the CAD-DR system achieved an average SE of 93.20%,SP of 96.10%,and ACC of 98%.This result indicates that the proposed CAD-DR system is appropriate for the screening of the severity-level of DR.
基金The authors extend their appreciation to the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the project number(IF-PSAU-2021/01/18596).
文摘Aging is a natural process that leads to debility,disease,and dependency.Alzheimer’s disease(AD)causes degeneration of the brain cells leading to cognitive decline and memory loss,as well as dependence on others to fulfill basic daily needs.AD is the major cause of dementia.Computer-aided diagnosis(CADx)tools aid medical practitioners in accurately identifying diseases such as AD in patients.This study aimed to develop a CADx tool for the early detection of AD using the Intelligent Water Drop(IWD)algorithm and the Random Forest(RF)classifier.The IWD algorithm an efficient feature selection method,was used to identify the most deterministic features of AD in the dataset.RF is an ensemble method that leverages multiple weak learners to classify a patient’s disease as either demented(DN)or cognitively normal(CN).The proposed tool also classifies patients as mild cognitive impairment(MCI)or CN.The dataset on which the performance of the proposed CADx was evaluated was sourced from the Alzheimer’s Disease Neuroimaging Initiative(ADNI).The RF ensemble method achieves 100%accuracy in identifying DN patients from CN patients.The classification accuracy for classifying patients as MCI or CN is 92%.This study emphasizes the significance of pre-processing prior to classification to improve the classification results of the proposed CADx tool.
文摘Deep neural network(DNN)based computer-aided breast tumor diagnosis(CABTD)method plays a vital role in the early detection and diagnosis of breast tumors.However,a Brightness mode(B-mode)ultrasound image derives training feature samples that make closer isolation toward the infection part.Hence,it is expensive due to a metaheuristic search of features occupying the global region of interest(ROI)structures of input images.Thus,it may lead to the high computational complexity of the pre-trained DNN-based CABTD method.This paper proposes a novel ensemble pretrained DNN-based CABTD method using global-and local-ROI-structures of B-mode ultrasound images.It conveys the additional consideration of a local-ROI-structures for further enhan-cing the pretrained DNN-based CABTD method’s breast tumor diagnostic performance without degrading its visual quality.The features are extracted at various depths(18,50,and 101)from the global and local ROI structures and feed to support vector machine for better classification.From the experimental results,it has been observed that the combined local and global ROI structure of small depth residual network ResNet18(0.8 in%)has produced significant improve-ment in pixel ratio as compared to ResNet50(0.5 in%)and ResNet101(0.3 in%),respectively.Subsequently,the pretrained DNN-based CABTD methods have been tested by influencing local and global ROI structures to diagnose two specific breast tumors(Benign and Malignant)and improve the diagnostic accuracy(86%)compared to Dense Net,Alex Net,VGG Net,and Google Net.Moreover,it reduces the computational complexity due to the small depth residual network ResNet18,respectively.
基金Supported by Science and Technology Projects in Guangzhou,No.2023A04J2282。
文摘BACKGROUND Computer-aided diagnosis(CAD)may assist endoscopists in identifying and classifying polyps during colonoscopy for detecting colorectal cancer.AIM To build a system using CAD to detect and classify polyps based on the Yamada classification.METHODS A total of 24045 polyp and 72367 nonpolyp images were obtained.We established a computer-aided detection and Yamada classification model based on the YOLOv7 neural network algorithm.Frame-based and image-based evaluation metrics were employed to assess the performance.RESULTS Computer-aided detection and Yamada classification screened polyps with a precision of 96.7%,a recall of 95.8%,and an F1-score of 96.2%,outperforming those of all groups of endoscopists.In regard to the Yamada classification of polyps,the CAD system displayed a precision of 82.3%,a recall of 78.5%,and an F1-score of 80.2%,outper-forming all levels of endoscopists.In addition,according to the image-based method,the CAD had an accuracy of 99.2%,a specificity of 99.5%,a sensitivity of 98.5%,a positive predictive value of 99.0%,a negative predictive value of 99.2%for polyp detection and an accuracy of 97.2%,a specificity of 98.4%,a sensitivity of 79.2%,a positive predictive value of 83.0%,and a negative predictive value of 98.4%for poly Yamada classification.CONCLUSION We developed a novel CAD system based on a deep neural network for polyp detection,and the Yamada classi-fication outperformed that of nonexpert endoscopists.This CAD system could help community-based hospitals enhance their effectiveness in polyp detection and classification.
基金Youth Qihuang Scholar Support Project(20201A2180)。
文摘Objective To investigate morphological differences between obstructive and non-obstructive coronary artery disease(CAD)patients using computer-aided image analysis,and identify color and texture features for traditional Chinese medicine(TCM)syndrome differentiation.Methods This prospective study enrolled patients undergoing coronary computed tomography angiography(CTA)at the Affiliated Hospital of Liaoning University of Traditional Chinese Medicine between May 1,2024 and August 7,2025.Based on CTA results,patients were categorized into obstructive CAD and non-obstructive CAD groups.Standardized tongue images were acquired using a dedicated mobile application(Traditional Chinese Medicine Tongue Image-Assisted Diagnosis System)and analyzed for the overall tongue surface and three macroscopic features(tooth marks,fissures,and red dots)from which high-dimensional color and texture parameters were extracted.Multi-scale texture features were derived using spatial-domain Laplacian pyramid and frequency-domain wavelet transform methods.Dimensionality reduction and feature selection were performed using principal component analysis(PCA)and random forest with 5-fold cross-validation.Feature stability was assessed using Hodges-Lehmann estimator and Cliff’s δ.A multi-view XGBoost model was developed to differentiate the two groups and evaluated on a temporally independent validation set using accuracy and the area under the receiver operating characteristic curve(AUC).SHapley Additive exPlanations(SHAP)analysis was applied to interpret model decisions.Results This study analyzed 373 CAD patients,including 167 with obstructive CAD and 206 with non-obstructive CAD according to CTA results.The whole cohort was divided into training set(n=316,obstructive:non-obstructive=142:174)and validation set(n=57,obstructive:non-obstructive=25:32),with balanced baseline characteristics(P>0.05).Macroscopic tongue analysis revealed that patients with obstructive CAD had fewer tooth marks[odds ratio(OR)=0.43,P<0.05]and red dots(OR=0.46,P<0.05).High-dimensional color analysis identified pronounced intergroup differences,most notably a reduction in hue values in the hue-saturation-intensity(HSI)color space among obstructive CAD patients(Cliff’s δ=-0.31,P=2.72×10^(-6);Hodges-Lehmann estimator:-0.31).PCA results suggested that tongue surface features explained the highest proportion of variance(48.2%).Random forest screening identified 77 stable features across all tongue regions,with wavelet-transformed texture features demonstrating the highest importance.The multi-view XGBoost fusion model achieved an accuracy of 75%and an AUC of 0.779 in the independent validation set.SHAP analysis identified the wavelet-based feature-left-handed lower-level gray-level size zone matrix zone variance(LHL_glszm_ZoneVariance)as the top predictor,accounting for 40.6%of the model's decision variance,and indicated that 85.3%of the predictive power came from wavelet-based texture features.Conclusion This study has provided objective evidence for the TCM concept that“the tongue reflects the heart”by identifying distinct morphological and colorimetric tongue patterns in patients with obstructive CAD through artificial intelligence(AI)-driven image analysis,and the promising performance of the computational model suggests its potential as a non-invasive adjunctive tool for CAD assessment.
基金funded by the Indian Council of Medical Research(ICMR),New Delhi,Government of India under Grant No.EM/SG/Dev.Res/124/0812-2023.
文摘Jaundice,common condition in newborns,is characterized by yellowing of the skin and eyes due to elevated levels of bilirubin in the blood.Timely detection and management of jaundice are crucial to prevent potential complications.Traditional jaundice assessment methods rely on visual inspection or invasive blood tests that are subjective and painful for infants,respectively.Although several automated methods for jaundice detection have been developed during the past few years,a limited number of reviews consolidating these developments have been presented till date,making it essential to systematically evaluate and present the existing advancements.This paper fills this gap by providing a thorough survey of automated methods for jaundice detection in neonates.The primary focus of the survey is to review the existing methodologies,techniques,and technologies used for neonatal jaundice detection.The key findings from the review indicate that image-based bilirubinometers and transcutaneous bilirubinometers are promising non-invasive alternatives,and provide a good trade-off between accuracy and ease of use.However,their effectiveness varies with factors like skin pigmentation,gestational age,and measurement site.Spectroscopic and biosensor-based techniques show high sensitivity but need further clinical validation.Despite advancements,several challenges including device calibration,large-scale validation,and regulatory barriers still haunt the researchers.Standardization,regulatory compliances,and seamless integration into healthcare workflows are the key hurdles to be addressed.By consolidating the current knowledge and discussing the challenges and opportunities in this field,this survey aims to contribute to the advancement of automatic jaundice detection and ultimately improve neonatal care.
文摘BACKGROUND Dual-phenotype hepatocellular carcinoma(HCC)is a relatively new subtype of HCC.Studies have shown that in the context of chronic hepatitis,liver cirrhosis,and other liver conditions,some intrahepatic cholangiocarcinomas(ICCs)exhibit an enhancement pattern similar to that of HCC.Both dual-phenotype HCC(DPHCC)and ICC can express biliary markers,making imaging and pathology differentiation difficult.Currently,radiomics is widely used in the differentiation,clinical staging,and prognosis assessment of various diseases.Radiomics can effectively differentiate DPHCC and ICC preoperatively.AIM To evaluate the value of radiomics in the differential diagnosis of DPHCC and ICC and to validate its clinical applicability METHODS In this retrospective study,the data of 53 DPHCC patients and 124 ICC patients were collected retrospectively and randomly divided into training and testing sets at a ratio of 7:3.After delineation of regions of interest and feature extraction and selection,radiomics models were constructed.Receiver operating characteristic curve analysis was conducted to calculate the area under the curve(AUC)for each model.The AUC values of radiologists with and without assistance from the model were also assessed.RESULTS In the training set,the AUC value of the radiomic model was the highest,and the combined model and the radiomic model had similar AUC(P>0.05);the differences in the AUC values between the combined model and the clinical-sign model was statistically significant(P<0.05).In the testing set,the AUC value of the combined model was the highest,and the differences in the AUC values between the combined model and the clinical-sign model was statistically significant(P<0.05).With model assistance,the AUC values of Doctor D(10 years of experience in abdominal imaging diagnosis)and Doctor E(5 years of experience in abdominal imaging diagnosis)both increased.CONCLUSION Radiomics can differentiate DPHCC and ICC,and with assistance from the developed model,the accuracy of less experienced doctors in the differential diagnosis of these two diseases can be improved.
基金supported by the National Natural Science Foundation of China(82071948,82472003)Discovery Partners Institute and Shield of Illinois,Guangdong Natural Science Foundation(2022A1515011675)the Scientific Research Fund for Hundred Talents Program Talent Introduction of Sun Yat-sen University(1320323001).
文摘Coronary artery disease is a highly lethal cardiovascular condition,making early diagnosis crucial for patients.Echocardiograph is employed to identify coronary heart disease(CHD).However,due to issues such as fuzzy object boundaries,complex tissue structures,and motion artifacts in ultrasound images,it is challenging to detect CHD accurately.This paper proposes an improved Transformer model based on the Feedback Self-Attention Mechanism(FSAM)for classification of ultrasound images.The model enhances attention weights,making it easier to capture complex features.Experimental results show that the proposed method achieves high levels of accuracy,recall,precision,F1 score,and area under the receiver operating characteristic curve(72.3%,79.5%,82.0%,81.0%,and 0.73%,respectively).The proposed model was compared with widely used models,including convolutional neural network and visual Transformer model,and the results show that our model outperforms others in the above evaluation metrics.In conclusion,the proposed model provides a promising approach for diagnosing CHD using echocardiogram.
基金Supported by Shanghai Science and Technology Innovation Action Program, No. 21Y31900100234 Clinical Research Fund of Changhai Hospital, No. 2019YXK006
文摘BACKGROUND Upper gastrointestinal endoscopy is critical for esophageal squamous cell carcinoma(ESCC)detection;however,endoscopists require long-term training to avoid missing superficial lesions.AIM To develop a deep learning computer-assisted diagnosis(CAD)system for endoscopic detection of superficial ESCC and investigate its application value.METHODS We configured the CAD system for white-light and narrow-band imaging modes based on the YOLO v5 algorithm.A total of 4447 images from 837 patients and 1695 images from 323 patients were included in the training and testing datasets,respectively.Two experts and two non-expert endoscopists reviewed the testing dataset independently and with computer assistance.The diagnostic performance was evaluated in terms of the area under the receiver operating characteristic curve,accuracy,sensitivity,and specificity.RESULTS The area under the receiver operating characteristics curve,accuracy,sensitivity,and specificity of the CAD system were 0.982[95%confidence interval(CI):0.969-0.994],92.9%(95%CI:89.5%-95.2%),91.9%(95%CI:87.4%-94.9%),and 94.7%(95%CI:89.0%-97.6%),respectively.The accuracy of CAD was significantly higher than that of non-expert endoscopists(78.3%,P<0.001 compared with CAD)and comparable to that of expert endoscopists(91.0%,P=0.129 compared with CAD).After referring to the CAD results,the accuracy of the non-expert endoscopists significantly improved(88.2%vs 78.3%,P<0.001).Lesions with Paris classification type 0-IIb were more likely to be inaccurately identified by the CAD system.CONCLUSION The diagnostic performance of the CAD system is promising and may assist in improving detectability,particularly for inexperienced endoscopists.
