Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status...Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status.Each of these methods contributes unique diagnostic insights,enhancing the overall assessment of patient condition.Nevertheless,the amalgamation of data from multiple modalities presents difficulties due to disparities in resolution,data collection methods,and noise levels.While traditional models like Convolutional Neural Networks(CNNs)excel in single-modality tasks,they struggle to handle multi-modal complexities,lacking the capacity to model global relationships.This research presents a novel approach for examining multi-modal medical imagery using a transformer-based system.The framework employs self-attention and cross-attention mechanisms to synchronize and integrate features across various modalities.Additionally,it shows resilience to variations in noise and image quality,making it adaptable for real-time clinical use.To address the computational hurdles linked to transformer models,particularly in real-time clinical applications in resource-constrained environments,several optimization techniques have been integrated to boost scalability and efficiency.Initially,a streamlined transformer architecture was adopted to minimize the computational load while maintaining model effectiveness.Methods such as model pruning,quantization,and knowledge distillation have been applied to reduce the parameter count and enhance the inference speed.Furthermore,efficient attention mechanisms such as linear or sparse attention were employed to alleviate the substantial memory and processing requirements of traditional self-attention operations.For further deployment optimization,researchers have implemented hardware-aware acceleration strategies,including the use of TensorRT and ONNX-based model compression,to ensure efficient execution on edge devices.These optimizations allow the approach to function effectively in real-time clinical settings,ensuring viability even in environments with limited resources.Future research directions include integrating non-imaging data to facilitate personalized treatment and enhancing computational efficiency for implementation in resource-limited environments.This study highlights the transformative potential of transformer models in multi-modal medical imaging,offering improvements in diagnostic accuracy and patient care outcomes.展开更多
BACKGROUND Congestive hepatopathy,also known as nutmeg liver,is liver damage secondary to chronic heart failure(HF).Its morphological characteristics in terms of medical imaging are not defined and remain unclear.AIM ...BACKGROUND Congestive hepatopathy,also known as nutmeg liver,is liver damage secondary to chronic heart failure(HF).Its morphological characteristics in terms of medical imaging are not defined and remain unclear.AIM To leverage machine learning to capture imaging features of congestive hepatopathy using incidentally acquired computed tomography(CT)scans.METHODS We retrospectively analyzed 179 chronic HF patients who underwent echocardiography and CT within one year.Right HF severity was classified into three grades.Liver CT images at the paraumbilical vein level were used to develop a ResNet-based machine learning model to predict tricuspid regurgitation(TR)severity.Model accuracy was compared with that of six gastroenterology and four radiology experts.RESULTS In the included patients,120 were male(mean age:73.1±14.4 years).The accuracy of the results predicting TR severity from a single CT image for the machine learning model was significantly higher than the average accuracy of the experts.The model was found to be exceptionally reliable for predicting severe TR.CONCLUSION Deep learning models,particularly those using ResNet architectures,can help identify morphological changes associated with TR severity,aiding in early liver dysfunction detection in patients with HF,thereby improving outcomes.展开更多
Corporate image is the external manifestation of a company’s cultural and spiritual essence,as well as the overall impression formed through its interactions with the public.Huawei,as a successful multinational enter...Corporate image is the external manifestation of a company’s cultural and spiritual essence,as well as the overall impression formed through its interactions with the public.Huawei,as a successful multinational enterprise,has established a robust corporate image in the international market through technological innovation and brand building.Moreover,Huawei’s development is closely aligned with national policies and strategies,making it a representative enterprise for showcasing China’s technological independence and national image.This study examines Huawei’s English press releases on product launches published between 2022 and 2024 and conducts a comparative analysis with similar materials from Apple’s official website.Based on Fairclough’s three-dimensional discourse analysis model,this research explores the linguistic features of Huawei’s corporate image construction from the perspectives of text,discourse practice,and social practice.The findings reveal that Huawei has successfully constructed a corporate image that emphasizes technological innovation,prioritizes user needs,and underscores its identity as a national enterprise.This study not only sheds light on Huawei’s strategies for image construction in international competition but also provides a valuable reference for Chinese enterprises in their cultural communication and brand building during the globalization process.展开更多
Given the importance of sentiment analysis in diverse environments,various methods are used for image sentiment analysis,including contextual sentiment analysis that utilizes character and scene relationships.However,...Given the importance of sentiment analysis in diverse environments,various methods are used for image sentiment analysis,including contextual sentiment analysis that utilizes character and scene relationships.However,most existing works employ character faces in conjunction with context,yet lack the capacity to analyze the emotions of characters in unconstrained environments,such as when their faces are obscured or blurred.Accordingly,this article presents the Adaptive Multi-Channel Sentiment Analysis Network(AMSA),a contextual image sentiment analysis framework,which consists of three channels:body,face,and context.AMSA employs Multi-task Cascaded Convolutional Networks(MTCNN)to detect faces within body frames;if detected,facial features are extracted and fused with body and context information for emotion recognition.If not,the model leverages body and context features alone.Meanwhile,to address class imbalance in the EMOTIC dataset,Focal Loss is introduced to improve classification performance,especially for minority emotion categories.Experimental results have shown that certain sentiment categories with lower representation in the dataset demonstrate leading classification accuracy,the AMSA yields a 2.53%increase compared with state-of-the-art methods.展开更多
Applying visual grammar theory,this study examines representational,interactive,and compositional meanings of the giant panda in Western media cartoons related to China from 1999 to the present.Distinct phases in the ...Applying visual grammar theory,this study examines representational,interactive,and compositional meanings of the giant panda in Western media cartoons related to China from 1999 to the present.Distinct phases in the panda’s representation were identified and illustrated by cases of cartoons in major Western media.These phases trace shift of panda cartoon image from a symbol of peace and friendliness to a politicized emblem of China’s international stance.Key visual trends,such as transitivity,color symbolism,scale enlargement,and increasing compositional complexity,embody the panda’s role in shaping China’s global image and its function in international discourse.These trends reflect the panda’s transformation into a contested symbol,which mediates between China’s self-representation and Western perceptions of its geopolitical rise.By situating the analysis within the context of China’s growing global influence,this study contributes to visual and media studies,demonstrating how cultural symbols are recontextualized to reflect and shape geopolitical narratives.展开更多
Deep learning models have become a core technological tool in the field of medical image analysis.However,these models often suffer from a lack of transparency in their decision-making processes,leading to challenges ...Deep learning models have become a core technological tool in the field of medical image analysis.However,these models often suffer from a lack of transparency in their decision-making processes,leading to challenges related to trust and interpret ability in clinical applications.To address this issue,explainable artificial intelligence(XAI)techniques have been applied to medical image analysis.While showing promising potential,XAI also brings significant ethical risks in practice—most notably,the problem of spurious explanations.Such explanations may rise further concerns regarding patient privacy,data security,and the attribution of decisionmaking authority in medical contexts.This paper analyzes the application of XAI methods—particularly saliency aps—in medical image interpretation,identifies the underlying causes of spurious explanations,and proposes possible mitigation strategies.The aim is to contribute to the responsible and sustainable integration of explainable AI into clinical practice.展开更多
Medical image analysis has become a cornerstone of modern healthcare,driven by the exponential growth of data from imaging modalities such as MRI,CT,PET,ultrasound,and X-ray.Traditional machine learning methods have m...Medical image analysis has become a cornerstone of modern healthcare,driven by the exponential growth of data from imaging modalities such as MRI,CT,PET,ultrasound,and X-ray.Traditional machine learning methods have made early contributions;however,recent advancements in deep learning(DL)have revolutionized the field,offering state-of-the-art performance in image classification,segmentation,detection,fusion,registration,and enhancement.This comprehensive review presents an in-depth analysis of deep learning methodologies applied across medical image analysis tasks,highlighting both foundational models and recent innovations.The article begins by introducing conventional techniques and their limitations,setting the stage for DL-based solutions.Core DL architectures,including Convolutional Neural Networks(CNNs),Recurrent Neural Networks(RNNs),Generative Adversarial Networks(GANs),Vision Transformers(ViTs),and hybrid models,are discussed in detail,including their advantages and domain-specific adaptations.Advanced learning paradigms such as semi-supervised learning,selfsupervised learning,and few-shot learning are explored for their potential to mitigate data annotation challenges in clinical datasets.This review further categorizes major tasks in medical image analysis,elaborating on how DL techniques have enabled precise tumor segmentation,lesion detection,modality fusion,super-resolution,and robust classification across diverse clinical settings.Emphasis is placed on applications in oncology,cardiology,neurology,and infectious diseases,including COVID-19.Challenges such as data scarcity,label imbalance,model generalizability,interpretability,and integration into clinical workflows are critically examined.Ethical considerations,explainable AI(XAI),federated learning,and regulatory compliance are discussed as essential components of real-world deployment.Benchmark datasets,evaluation metrics,and comparative performance analyses are presented to support future research.The article concludes with a forward-looking perspective on the role of foundation models,multimodal learning,edge AI,and bio-inspired computing in the future of medical imaging.Overall,this review serves as a valuable resource for researchers,clinicians,and developers aiming to harness deep learning for intelligent,efficient,and clinically viable medical image analysis.展开更多
Methods and procedures of three-dimensional （3D） characterization of the pore structure features in the packed ore particle bed are focused. X-ray computed tomography was applied to deriving the cross-sectional imag...Methods and procedures of three-dimensional （3D） characterization of the pore structure features in the packed ore particle bed are focused. X-ray computed tomography was applied to deriving the cross-sectional images of specimens with single particle size of 1-2, 2-3, 3-4, 4-5, 5-6, 6-7, 7-8, 8-9, 9-10 ram. Based on the in-house developed 3D image analysis programs using Matlab, the volume porosity, pore size distribution and degree of connectivity were calculated and analyzed in detail. The results indicate that the volume porosity, the mean diameter of pores and the effective pore size （d50） increase with the increasing of particle size. Lognormal distribution or Gauss distribution is mostly suitable to model the pore size distribution. The degree of connectivity investigated on the basis of cluster-labeling algorithm also increases with increasing the particle size approximately.展开更多
To develop a quick, accurate and antinoise automated image registration technique for infrared images, the wavelet analysis technique was used to extract the feature points in two images followed by the compensation f...To develop a quick, accurate and antinoise automated image registration technique for infrared images, the wavelet analysis technique was used to extract the feature points in two images followed by the compensation for input image with angle difference between them. A hi erarchical feature matching algorithm was adopted to get the final transform parameters between the two images. The simulation results for two infrared images show that the method can effectively, quickly and accurately register images and be antinoise to some extent.展开更多
Aim： To establish a rat and mouse epididymal map based on the use of the Epiquatre automatic software for histologic image analysis. Methods： Epididymides from five adult rats and five adult mice were fixed in alcoh...Aim： To establish a rat and mouse epididymal map based on the use of the Epiquatre automatic software for histologic image analysis. Methods： Epididymides from five adult rats and five adult mice were fixed in alcoholic Bouin＇s fixative and embedded in paraffin. Serial longitudinal sections through the medial aspect of the organ were cut at 10 jam and stained with hematoxylin and eosin. As determined from major connective tissue septa, nine subdivisions of the rat epididymis and seven for the mouse were determined, consisting of five sub-regions in the caput （rat and mouse）, one （mouse） or three （rat） in the corpus and one in the cauda （rat and mouse）. Using the Epiquatre software, several tubular, luminal and epithelial morphometric parameters were evaluated. Results： Statistical comparison of the quantitative parameters revealed regional differences （2-5 in the rat, 3-6 in the mouse, dependent on parameters） with caput regions 1 and 2 being largely distinguishable from the similar remaining caput and corpus, which were in turn recognizable from the cauda regions in both species. Conclusion： The use of the Epiquatre software allowed us to establish regression curves for different morphometric parameters that can permit the detection of changes in their values under different pathological or experimental conditions. （Asian J Androl 2005 Sep; 7： 267-275）展开更多
Early correction of childhood malocclusion is timely managing morphological,structural,and functional abnormalities at different dentomaxillofacial developmental stages.The selection of appropriate imaging examination...Early correction of childhood malocclusion is timely managing morphological,structural,and functional abnormalities at different dentomaxillofacial developmental stages.The selection of appropriate imaging examination and comprehensive radiological diagnosis and analysis play an important role in early correction of childhood malocclusion.This expert consensus is a collaborative effort by multidisciplinary experts in dentistry across the nation based on the current clinical evidence,aiming to provide general guidance on appropriate imaging examination selection,comprehensive and accurate imaging assessment for early orthodontic treatment patients.展开更多
The detection and characterization of non-metallic inclusions are essential for clean steel production.Recently,imaging analysis combined with high-dimensional data processing of metallic materials using artificial in...The detection and characterization of non-metallic inclusions are essential for clean steel production.Recently,imaging analysis combined with high-dimensional data processing of metallic materials using artificial intelligence(AI)-based machine learning(ML)has developed rapidly.This technique has achieved impressive results in the field of inclusion classification in process metallurgy.The present study surveys the ML modeling of inclusion prediction in advanced steels,including the detection,classification,and feature prediction of inclusions in different steel grades.Studies on clean steel with different features based on data and image analysis via ML are summarized.Regarding the data analysis,the inclusion prediction methodology based on ML establishes a connection between the experimental parameters and inclusion characteristics and analyzes the importance of the experimental parameters.Regarding the image analysis,the focus is placed on the classification of different types of inclusions via deep learning,in comparison with data analysis.Finally,further development of inclusion analyses using ML-based methods is recommended.This work paves the way for the application of AIbased methodologies for ultraclean-steel studies from a sustainable metallurgy perspective.展开更多
Underground hydrogen storage has gained interest in recent years due to the enormous demand for clean energy.Hydrogen is more diffusive than air,with a smaller density and lower viscosity.These unique properties intro...Underground hydrogen storage has gained interest in recent years due to the enormous demand for clean energy.Hydrogen is more diffusive than air,with a smaller density and lower viscosity.These unique properties introduce distinctive hydrodynamic phenomena in hydrogen storage,one of which is fingering.Fingering could induce the fluid trapped in small clusters of pores,leading to a dramatic decrease in hydrogen saturation and a lower recovery rate.In this study,numerical simulations are performed at the microscopic scale to understand the evolution of hydrogen saturation and the impacts of injection and withdrawal cycles.Two sets of micromodels with different porosity(0.362 and 0.426)and minimum sizes of pore throats(0.362 mm and 0.181 mm)are developed in the numerical model.A parameter analysis is then conducted to understand the influence of injection velocity(in the range of 10^(-2)m/s to 10^(-5)m/s)and porous structure on the fingering pattern,followed by an image analysis to capture the evolution of the fingering pattern.Viscous fingering,capillary fingering,and crossover fingering are observed and identified under different boundary conditions.The fractal dimension,specific area,mean angle,and entropy of fingers are proposed as geometric descriptors to characterize the shape of the fingering pattern.When porosity increases from 0.362 to 0.426,the saturation of hydrogen increases by 26.2%.Narrower pore throats elevate capillary resistance,which hinders fluid invasion.These results underscore the importance of pore structures and the interaction between viscous and capillary forces for hydrogen recovery efficiency.This work illuminates the influence of the pore structures and the fluid properties on the immiscible displacement of hydrogen and can be further extended to optimize the injection strategy of hydrogen in underground hydrogen storage.展开更多
The advent of 5G technology has significantly enhanced the transmission of images over networks,expanding data accessibility and exposure across various applications in digital technology and social media.Consequently...The advent of 5G technology has significantly enhanced the transmission of images over networks,expanding data accessibility and exposure across various applications in digital technology and social media.Consequently,the protection of sensitive data has become increasingly critical.Regardless of the complexity of the encryption algorithm used,a robust and highly secure encryption key is essential,with randomness and key space being crucial factors.This paper proposes a new Robust Deoxyribonucleic Acid(RDNA)nucleotide-based encryption method.The RDNA encryption method leverages the unique properties of DNA nucleotides,including their inherent randomness and extensive key space,to generate a highly secure encryption key.By employing transposition and substitution operations,the RDNA method ensures significant diffusion and confusion in the encrypted images.Additionally,it utilises a pseudorandom generation technique based on the random sequence of nucleotides in the DNA secret key.The performance of the RDNA encryption method is evaluated through various statistical and visual tests,and compared against established encryption methods such as 3DES,AES,and a DNA-based method.Experimental results demonstrate that the RDNA encryption method outperforms its rivals in the literature,and achieves superior performance in terms of information entropy,avalanche effect,encryption execution time,and correlation reduction,while maintaining competitive values for NMAE,PSNR,NPCR,and UACI.The high degree of randomness and sensitivity to key changes inherent in the RDNA method offers enhanced security,making it highly resistant to brute force and differential attacks.展开更多
AIM:To summarize publication trends in the field of strabismus over the past 30y and predict future research hotspots.METHODS:A total of 2915 English-language articles and reviews on strabismus,published between 1993 ...AIM:To summarize publication trends in the field of strabismus over the past 30y and predict future research hotspots.METHODS:A total of 2915 English-language articles and reviews on strabismus,published between 1993 and 2022,were retrieved from the Web of Science Core Collection.Bibliometric analyses were performed using VOSviewer and CiteSpace software to explore publication trends,as well as the contributions and collaborative networks of countries/regions,authors,institutions,and journals.RESULTS:The annual number of publications on strabismus showed a consistent upward trend.The United States(USA)maintained a leading position in this research field while Republic of Korea and China emerged as rapidly advancing contributors over the last decade.The University of California,Los Angeles ranked as the most productive institution,and Jonathan M.Holmes from USA was the most productive author.Journal of AAPOS was the leading journal with the most strabismus publications,whereas the two most highly cited articles were both published in Ophthalmology.Co-occurrence analysis identified pivotal keywords and burst terms,including intermittent exotropia(IXT),acute acquired comitant esotropia(AACE),functional magnetic resonance imaging(fMRI),and surgical treatment,which were confirmed as predominant and frontier topics.CONCLUSION:This study provides a comprehensive bibliometric analysis of strabismus research,revealing the evolution of research hotspots over the past 30y and outlining several cutting-edge directions for future investigation.展开更多
Recent studies indicate that millions of individuals suffer from renal diseases,with renal carcinoma,a type of kidney cancer,emerging as both a chronic illness and a significant cause of mortality.Magnetic Resonance I...Recent studies indicate that millions of individuals suffer from renal diseases,with renal carcinoma,a type of kidney cancer,emerging as both a chronic illness and a significant cause of mortality.Magnetic Resonance Imaging(MRI)and Computed Tomography(CT)have become essential tools for diagnosing and assessing kidney disorders.However,accurate analysis of thesemedical images is critical for detecting and evaluating tumor severity.This study introduces an integrated hybrid framework that combines three complementary deep learning models for kidney tumor segmentation from MRI images.The proposed framework fuses a customized U-Net and Mask R-CNN using a weighted scheme to achieve semantic and instance-level segmentation.The fused outputs are further refined through edge detection using Stochastic FeatureMapping Neural Networks(SFMNN),while volumetric consistency is ensured through Improved Mini-Batch K-Means(IMBKM)clustering integrated with an Encoder-Decoder Convolutional Neural Network(EDCNN).The outputs of these three stages are combined through a weighted fusion mechanism,with optimal weights determined empirically.Experiments on MRI scans from the TCGA-KIRC dataset demonstrate that the proposed hybrid framework significantly outperforms standalone models,achieving a Dice Score of 92.5%,an IoU of 87.8%,a Precision of 93.1%,a Recall of 90.8%,and a Hausdorff Distance of 2.8 mm.These findings validate that the weighted integration of complementary architectures effectively overcomes key limitations in kidney tumor segmentation,leading to improved diagnostic accuracy and robustness in medical image analysis.展开更多
BACKGROUND Pancreatic cancer remains one of the most lethal malignancies worldwide,with a poor prognosis often attributed to late diagnosis.Understanding the correlation between pathological type and imaging features ...BACKGROUND Pancreatic cancer remains one of the most lethal malignancies worldwide,with a poor prognosis often attributed to late diagnosis.Understanding the correlation between pathological type and imaging features is crucial for early detection and appropriate treatment planning.AIM To retrospectively analyze the relationship between different pathological types of pancreatic cancer and their corresponding imaging features.METHODS We retrospectively analyzed the data of 500 patients diagnosed with pancreatic cancer between January 2010 and December 2020 at our institution.Pathological types were determined by histopathological examination of the surgical spe-cimens or biopsy samples.The imaging features were assessed using computed tomography,magnetic resonance imaging,and endoscopic ultrasound.Statistical analyses were performed to identify significant associations between pathological types and specific imaging characteristics.RESULTS There were 320(64%)cases of pancreatic ductal adenocarcinoma,75(15%)of intraductal papillary mucinous neoplasms,50(10%)of neuroendocrine tumors,and 55(11%)of other rare types.Distinct imaging features were identified in each pathological type.Pancreatic ductal adenocarcinoma typically presents as a hypodense mass with poorly defined borders on computed tomography,whereas intraductal papillary mucinous neoplasms present as characteristic cystic lesions with mural nodules.Neuroendocrine tumors often appear as hypervascular lesions in contrast-enhanced imaging.Statistical analysis revealed significant correlations between specific imaging features and pathological types(P<0.001).CONCLUSION This study demonstrated a strong association between the pathological types of pancreatic cancer and imaging features.These findings can enhance the accuracy of noninvasive diagnosis and guide personalized treatment approaches.展开更多
Segmenting the lesion regions from the ultrasound （US） images is an important step in the intra-operative planning of some computer-aided therapies. High-Intensity Focused Ultrasound （HIFU）, as a popular computer-...Segmenting the lesion regions from the ultrasound （US） images is an important step in the intra-operative planning of some computer-aided therapies. High-Intensity Focused Ultrasound （HIFU）, as a popular computer-aided therapy, has been widely used in the treatment of uterine fibroids. However, such segmentation in HIFU remains challenge for two reasons： （1） the blurry or missing boundaries of lesion regions in the HIFU images and （2） the deformation of uterine fibroids caused by the patient＇s breathing or an external force during the US imaging process, which can lead to complex shapes of lesion regions. These factors have prevented classical active contour-based segmentation methods from yielding desired results for uterine fibroids in US images. In this paper, a novel active contour-based segmentation method is proposed, which utilizes the correlation information of target shapes among a sequence of images as prior knowledge to aid the existing active contour method. This prior knowledge can be interpreted as a unsupervised clustering of shapes prior modeling. Meanwhile, it is also proved that the shapes correlation has the low-rank property in a linear space, and the theory of matrix recovery is used as an effective tool to impose the proposed prior on an existing active contour model. Finally, an accurate method is developed to solve the proposed model by using the Augmented Lagrange Multiplier （ALM）. Experimental results from both synthetic and clinical uterine fibroids US image sequences demonstrate that the proposed method can consistently improve the performance of active contour models and increase the robustness against missing or misleading boundaries, and can greatly improve the efficiency of HIFU therapy.展开更多
BACKGROUND It is evident that an accurate evaluation of T and N stage rectal cancer is essential for treatment planning.It has not been extensively investigated whether texture features derived from diffusion-weighted...BACKGROUND It is evident that an accurate evaluation of T and N stage rectal cancer is essential for treatment planning.It has not been extensively investigated whether texture features derived from diffusion-weighted imaging(DWI)images and apparent diffusion coefficient(ADC)maps are associated with the extent of local invasion(pathological stage T1-2 vs T3-4)and nodal involvement(pathological stage N0 vs N1-2)in rectal cancer.AIM To predict different stages of rectal cancer using texture analysis based on DWI images and ADC maps.METHODS One hundred and fifteen patients with pathologically proven rectal cancer,who underwent preoperative magnetic resonance imaging,including DWI,were enrolled,retrospectively.The ADC measurements(ADCmean,ADCmin,ADCmax)as well as texture features,including the gray level co-occurrence matrix parameters,the gray level run-length matrix parameters and wavelet parameters were calculated based on DWI(b=0 and b=1000)images and the ADC maps.Independent sample t-tests or Mann-Whitney U tests were used for statistical analysis.Multivariate logistic regression analysis was conducted to establish the models.The predictive performance was validated by receiver operating characteristic curve analysis.RESULTS Dissimilarity,sum average,information correlation and run-length nonuniformity from DWIb=0 images,gray level nonuniformity,run percentage and run-length nonuniformity from DWIb=1000 images,and dissimilarity and run percentage from ADC maps were found to be independent predictors of local invasion(stage T3-4).The area under the operating characteristic curve of the model reached 0.793 with a sensitivity of 78.57%and a specificity of 74.19%.Sum average,gray level nonuniformity and the horizontal components of symlet transform(SymletH)from DWIb=0 images,sum average,information correlation,long run low gray level emphasis and SymletH from DWIb=1000 images,and ADCmax,ADCmean and information correlation from ADC maps were identified as independent predictors of nodal involvement.The area under the operating characteristic curve of the model reached 0.802 with a sensitivity of 80.77%and a specificity of 68.25%.CONCLUSION Texture features extracted from DWI images and ADC maps are useful clues for predicting pathological T and N stages in rectal cancer.展开更多
Confocal laser endomicroscopy(CLE)has become an indispensable tool in the diagnosis and detection of gastrointestinal(GI)diseases due to its high-resolution and high-contrast imaging capabilities.However,the early-sta...Confocal laser endomicroscopy(CLE)has become an indispensable tool in the diagnosis and detection of gastrointestinal(GI)diseases due to its high-resolution and high-contrast imaging capabilities.However,the early-stage imaging changes of gastrointestinal disorders are often subtle,and traditional medical image analysis methods rely heavily on manual interpretation,which is time-consuming,subject to observer variability,and inefficient for accurate lesion identification across large-scale image datasets.With the introduction of artificial intelligence(AI)technologies,AI-driven CLE image analysis systems can automatically extract pathological features and have demonstrated significant clinical value in lesion recognition,classification diagnosis,and malignancy prediction of GI diseases.These systems greatly enhance diagnostic efficiency and early detection capabilities.This review summarizes the applications of AI-assisted CLE in GI diseases,analyzes the limitations of current technologies,and explores future research directions.It is expected that the deep integration of AI and confocal imaging technologies will provide strong support for precision diagnosis and personalized treatment in the field of gastrointestinal disorders.展开更多
基金supported by the Deanship of Research and Graduate Studies at King Khalid University under Small Research Project grant number RGP1/139/45.
文摘Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status.Each of these methods contributes unique diagnostic insights,enhancing the overall assessment of patient condition.Nevertheless,the amalgamation of data from multiple modalities presents difficulties due to disparities in resolution,data collection methods,and noise levels.While traditional models like Convolutional Neural Networks(CNNs)excel in single-modality tasks,they struggle to handle multi-modal complexities,lacking the capacity to model global relationships.This research presents a novel approach for examining multi-modal medical imagery using a transformer-based system.The framework employs self-attention and cross-attention mechanisms to synchronize and integrate features across various modalities.Additionally,it shows resilience to variations in noise and image quality,making it adaptable for real-time clinical use.To address the computational hurdles linked to transformer models,particularly in real-time clinical applications in resource-constrained environments,several optimization techniques have been integrated to boost scalability and efficiency.Initially,a streamlined transformer architecture was adopted to minimize the computational load while maintaining model effectiveness.Methods such as model pruning,quantization,and knowledge distillation have been applied to reduce the parameter count and enhance the inference speed.Furthermore,efficient attention mechanisms such as linear or sparse attention were employed to alleviate the substantial memory and processing requirements of traditional self-attention operations.For further deployment optimization,researchers have implemented hardware-aware acceleration strategies,including the use of TensorRT and ONNX-based model compression,to ensure efficient execution on edge devices.These optimizations allow the approach to function effectively in real-time clinical settings,ensuring viability even in environments with limited resources.Future research directions include integrating non-imaging data to facilitate personalized treatment and enhancing computational efficiency for implementation in resource-limited environments.This study highlights the transformative potential of transformer models in multi-modal medical imaging,offering improvements in diagnostic accuracy and patient care outcomes.
基金Supported by Grant-in-Aid for Research on Hepatitis from the Japan Agency for Medical Research and Development,No.24fk0210128h0002Grant-in-Aid for Scientific Research,No.KAKENHI-23K07372.
文摘BACKGROUND Congestive hepatopathy,also known as nutmeg liver,is liver damage secondary to chronic heart failure(HF).Its morphological characteristics in terms of medical imaging are not defined and remain unclear.AIM To leverage machine learning to capture imaging features of congestive hepatopathy using incidentally acquired computed tomography(CT)scans.METHODS We retrospectively analyzed 179 chronic HF patients who underwent echocardiography and CT within one year.Right HF severity was classified into three grades.Liver CT images at the paraumbilical vein level were used to develop a ResNet-based machine learning model to predict tricuspid regurgitation(TR)severity.Model accuracy was compared with that of six gastroenterology and four radiology experts.RESULTS In the included patients,120 were male(mean age:73.1±14.4 years).The accuracy of the results predicting TR severity from a single CT image for the machine learning model was significantly higher than the average accuracy of the experts.The model was found to be exceptionally reliable for predicting severe TR.CONCLUSION Deep learning models,particularly those using ResNet architectures,can help identify morphological changes associated with TR severity,aiding in early liver dysfunction detection in patients with HF,thereby improving outcomes.
文摘Corporate image is the external manifestation of a company’s cultural and spiritual essence,as well as the overall impression formed through its interactions with the public.Huawei,as a successful multinational enterprise,has established a robust corporate image in the international market through technological innovation and brand building.Moreover,Huawei’s development is closely aligned with national policies and strategies,making it a representative enterprise for showcasing China’s technological independence and national image.This study examines Huawei’s English press releases on product launches published between 2022 and 2024 and conducts a comparative analysis with similar materials from Apple’s official website.Based on Fairclough’s three-dimensional discourse analysis model,this research explores the linguistic features of Huawei’s corporate image construction from the perspectives of text,discourse practice,and social practice.The findings reveal that Huawei has successfully constructed a corporate image that emphasizes technological innovation,prioritizes user needs,and underscores its identity as a national enterprise.This study not only sheds light on Huawei’s strategies for image construction in international competition but also provides a valuable reference for Chinese enterprises in their cultural communication and brand building during the globalization process.
文摘Given the importance of sentiment analysis in diverse environments,various methods are used for image sentiment analysis,including contextual sentiment analysis that utilizes character and scene relationships.However,most existing works employ character faces in conjunction with context,yet lack the capacity to analyze the emotions of characters in unconstrained environments,such as when their faces are obscured or blurred.Accordingly,this article presents the Adaptive Multi-Channel Sentiment Analysis Network(AMSA),a contextual image sentiment analysis framework,which consists of three channels:body,face,and context.AMSA employs Multi-task Cascaded Convolutional Networks(MTCNN)to detect faces within body frames;if detected,facial features are extracted and fused with body and context information for emotion recognition.If not,the model leverages body and context features alone.Meanwhile,to address class imbalance in the EMOTIC dataset,Focal Loss is introduced to improve classification performance,especially for minority emotion categories.Experimental results have shown that certain sentiment categories with lower representation in the dataset demonstrate leading classification accuracy,the AMSA yields a 2.53%increase compared with state-of-the-art methods.
基金supported by the Wuhan University Undergraduate Project of Innovation and Entrepreneurship Training“The Evolution of Cartoon Images of Pandas in Western Media’s China-Related News From the Perspective of Multimodal Theory”(Project Number:S202410486013).
文摘Applying visual grammar theory,this study examines representational,interactive,and compositional meanings of the giant panda in Western media cartoons related to China from 1999 to the present.Distinct phases in the panda’s representation were identified and illustrated by cases of cartoons in major Western media.These phases trace shift of panda cartoon image from a symbol of peace and friendliness to a politicized emblem of China’s international stance.Key visual trends,such as transitivity,color symbolism,scale enlargement,and increasing compositional complexity,embody the panda’s role in shaping China’s global image and its function in international discourse.These trends reflect the panda’s transformation into a contested symbol,which mediates between China’s self-representation and Western perceptions of its geopolitical rise.By situating the analysis within the context of China’s growing global influence,this study contributes to visual and media studies,demonstrating how cultural symbols are recontextualized to reflect and shape geopolitical narratives.
文摘Deep learning models have become a core technological tool in the field of medical image analysis.However,these models often suffer from a lack of transparency in their decision-making processes,leading to challenges related to trust and interpret ability in clinical applications.To address this issue,explainable artificial intelligence(XAI)techniques have been applied to medical image analysis.While showing promising potential,XAI also brings significant ethical risks in practice—most notably,the problem of spurious explanations.Such explanations may rise further concerns regarding patient privacy,data security,and the attribution of decisionmaking authority in medical contexts.This paper analyzes the application of XAI methods—particularly saliency aps—in medical image interpretation,identifies the underlying causes of spurious explanations,and proposes possible mitigation strategies.The aim is to contribute to the responsible and sustainable integration of explainable AI into clinical practice.
文摘Medical image analysis has become a cornerstone of modern healthcare,driven by the exponential growth of data from imaging modalities such as MRI,CT,PET,ultrasound,and X-ray.Traditional machine learning methods have made early contributions;however,recent advancements in deep learning(DL)have revolutionized the field,offering state-of-the-art performance in image classification,segmentation,detection,fusion,registration,and enhancement.This comprehensive review presents an in-depth analysis of deep learning methodologies applied across medical image analysis tasks,highlighting both foundational models and recent innovations.The article begins by introducing conventional techniques and their limitations,setting the stage for DL-based solutions.Core DL architectures,including Convolutional Neural Networks(CNNs),Recurrent Neural Networks(RNNs),Generative Adversarial Networks(GANs),Vision Transformers(ViTs),and hybrid models,are discussed in detail,including their advantages and domain-specific adaptations.Advanced learning paradigms such as semi-supervised learning,selfsupervised learning,and few-shot learning are explored for their potential to mitigate data annotation challenges in clinical datasets.This review further categorizes major tasks in medical image analysis,elaborating on how DL techniques have enabled precise tumor segmentation,lesion detection,modality fusion,super-resolution,and robust classification across diverse clinical settings.Emphasis is placed on applications in oncology,cardiology,neurology,and infectious diseases,including COVID-19.Challenges such as data scarcity,label imbalance,model generalizability,interpretability,and integration into clinical workflows are critically examined.Ethical considerations,explainable AI(XAI),federated learning,and regulatory compliance are discussed as essential components of real-world deployment.Benchmark datasets,evaluation metrics,and comparative performance analyses are presented to support future research.The article concludes with a forward-looking perspective on the role of foundation models,multimodal learning,edge AI,and bio-inspired computing in the future of medical imaging.Overall,this review serves as a valuable resource for researchers,clinicians,and developers aiming to harness deep learning for intelligent,efficient,and clinically viable medical image analysis.
基金Projects(50934002,51074013,51304076,51104100)supported by the National Natural Science Foundation of ChinaProject(IRT0950)supported by the Program for Changjiang Scholars Innovative Research Team in Universities,ChinaProject(2012M510007)supported by China Postdoctoral Science Foundation
文摘Methods and procedures of three-dimensional （3D） characterization of the pore structure features in the packed ore particle bed are focused. X-ray computed tomography was applied to deriving the cross-sectional images of specimens with single particle size of 1-2, 2-3, 3-4, 4-5, 5-6, 6-7, 7-8, 8-9, 9-10 ram. Based on the in-house developed 3D image analysis programs using Matlab, the volume porosity, pore size distribution and degree of connectivity were calculated and analyzed in detail. The results indicate that the volume porosity, the mean diameter of pores and the effective pore size （d50） increase with the increasing of particle size. Lognormal distribution or Gauss distribution is mostly suitable to model the pore size distribution. The degree of connectivity investigated on the basis of cluster-labeling algorithm also increases with increasing the particle size approximately.
文摘To develop a quick, accurate and antinoise automated image registration technique for infrared images, the wavelet analysis technique was used to extract the feature points in two images followed by the compensation for input image with angle difference between them. A hi erarchical feature matching algorithm was adopted to get the final transform parameters between the two images. The simulation results for two infrared images show that the method can effectively, quickly and accurately register images and be antinoise to some extent.
文摘Aim： To establish a rat and mouse epididymal map based on the use of the Epiquatre automatic software for histologic image analysis. Methods： Epididymides from five adult rats and five adult mice were fixed in alcoholic Bouin＇s fixative and embedded in paraffin. Serial longitudinal sections through the medial aspect of the organ were cut at 10 jam and stained with hematoxylin and eosin. As determined from major connective tissue septa, nine subdivisions of the rat epididymis and seven for the mouse were determined, consisting of five sub-regions in the caput （rat and mouse）, one （mouse） or three （rat） in the corpus and one in the cauda （rat and mouse）. Using the Epiquatre software, several tubular, luminal and epithelial morphometric parameters were evaluated. Results： Statistical comparison of the quantitative parameters revealed regional differences （2-5 in the rat, 3-6 in the mouse, dependent on parameters） with caput regions 1 and 2 being largely distinguishable from the similar remaining caput and corpus, which were in turn recognizable from the cauda regions in both species. Conclusion： The use of the Epiquatre software allowed us to establish regression curves for different morphometric parameters that can permit the detection of changes in their values under different pathological or experimental conditions. （Asian J Androl 2005 Sep; 7： 267-275）
基金supports by the National Natural Science Foundation of China(Nos.82201135)"2015"Cultivation Program for Reserve Talents for Academic Leaders of Nanjing Stomatological School,Medical School of Nanjing University(No.0223A204).
文摘Early correction of childhood malocclusion is timely managing morphological,structural,and functional abnormalities at different dentomaxillofacial developmental stages.The selection of appropriate imaging examination and comprehensive radiological diagnosis and analysis play an important role in early correction of childhood malocclusion.This expert consensus is a collaborative effort by multidisciplinary experts in dentistry across the nation based on the current clinical evidence,aiming to provide general guidance on appropriate imaging examination selection,comprehensive and accurate imaging assessment for early orthodontic treatment patients.
基金support from the National Key Research and Development Program of China(No.2024YFB3713705)is acknowledgedWangzhong Mu would like to acknowledge the Strategic Mobility,Sweden(SSF,No.SM22-0039)+1 种基金the Swedish Foundation for International Cooperation in Research and Higher Education(STINT,No.IB2022-9228)the Jernkontoret(Sweden)for supporting this clean steel research.Gonghao Lian would like to acknowledge China Scholarship Council(CSC,No.202306080032).
文摘The detection and characterization of non-metallic inclusions are essential for clean steel production.Recently,imaging analysis combined with high-dimensional data processing of metallic materials using artificial intelligence(AI)-based machine learning(ML)has developed rapidly.This technique has achieved impressive results in the field of inclusion classification in process metallurgy.The present study surveys the ML modeling of inclusion prediction in advanced steels,including the detection,classification,and feature prediction of inclusions in different steel grades.Studies on clean steel with different features based on data and image analysis via ML are summarized.Regarding the data analysis,the inclusion prediction methodology based on ML establishes a connection between the experimental parameters and inclusion characteristics and analyzes the importance of the experimental parameters.Regarding the image analysis,the focus is placed on the classification of different types of inclusions via deep learning,in comparison with data analysis.Finally,further development of inclusion analyses using ML-based methods is recommended.This work paves the way for the application of AIbased methodologies for ultraclean-steel studies from a sustainable metallurgy perspective.
基金supported by the National Key Research and Development Project(Grant No.2023YFE0110900)the National Natural Science Foundation of China(Grant Nos.42320104003,42477168).
文摘Underground hydrogen storage has gained interest in recent years due to the enormous demand for clean energy.Hydrogen is more diffusive than air,with a smaller density and lower viscosity.These unique properties introduce distinctive hydrodynamic phenomena in hydrogen storage,one of which is fingering.Fingering could induce the fluid trapped in small clusters of pores,leading to a dramatic decrease in hydrogen saturation and a lower recovery rate.In this study,numerical simulations are performed at the microscopic scale to understand the evolution of hydrogen saturation and the impacts of injection and withdrawal cycles.Two sets of micromodels with different porosity(0.362 and 0.426)and minimum sizes of pore throats(0.362 mm and 0.181 mm)are developed in the numerical model.A parameter analysis is then conducted to understand the influence of injection velocity(in the range of 10^(-2)m/s to 10^(-5)m/s)and porous structure on the fingering pattern,followed by an image analysis to capture the evolution of the fingering pattern.Viscous fingering,capillary fingering,and crossover fingering are observed and identified under different boundary conditions.The fractal dimension,specific area,mean angle,and entropy of fingers are proposed as geometric descriptors to characterize the shape of the fingering pattern.When porosity increases from 0.362 to 0.426,the saturation of hydrogen increases by 26.2%.Narrower pore throats elevate capillary resistance,which hinders fluid invasion.These results underscore the importance of pore structures and the interaction between viscous and capillary forces for hydrogen recovery efficiency.This work illuminates the influence of the pore structures and the fluid properties on the immiscible displacement of hydrogen and can be further extended to optimize the injection strategy of hydrogen in underground hydrogen storage.
文摘The advent of 5G technology has significantly enhanced the transmission of images over networks,expanding data accessibility and exposure across various applications in digital technology and social media.Consequently,the protection of sensitive data has become increasingly critical.Regardless of the complexity of the encryption algorithm used,a robust and highly secure encryption key is essential,with randomness and key space being crucial factors.This paper proposes a new Robust Deoxyribonucleic Acid(RDNA)nucleotide-based encryption method.The RDNA encryption method leverages the unique properties of DNA nucleotides,including their inherent randomness and extensive key space,to generate a highly secure encryption key.By employing transposition and substitution operations,the RDNA method ensures significant diffusion and confusion in the encrypted images.Additionally,it utilises a pseudorandom generation technique based on the random sequence of nucleotides in the DNA secret key.The performance of the RDNA encryption method is evaluated through various statistical and visual tests,and compared against established encryption methods such as 3DES,AES,and a DNA-based method.Experimental results demonstrate that the RDNA encryption method outperforms its rivals in the literature,and achieves superior performance in terms of information entropy,avalanche effect,encryption execution time,and correlation reduction,while maintaining competitive values for NMAE,PSNR,NPCR,and UACI.The high degree of randomness and sensitivity to key changes inherent in the RDNA method offers enhanced security,making it highly resistant to brute force and differential attacks.
基金Supported by National Natural Science Foundation of China(No.82020108006,No.81730025).
文摘AIM:To summarize publication trends in the field of strabismus over the past 30y and predict future research hotspots.METHODS:A total of 2915 English-language articles and reviews on strabismus,published between 1993 and 2022,were retrieved from the Web of Science Core Collection.Bibliometric analyses were performed using VOSviewer and CiteSpace software to explore publication trends,as well as the contributions and collaborative networks of countries/regions,authors,institutions,and journals.RESULTS:The annual number of publications on strabismus showed a consistent upward trend.The United States(USA)maintained a leading position in this research field while Republic of Korea and China emerged as rapidly advancing contributors over the last decade.The University of California,Los Angeles ranked as the most productive institution,and Jonathan M.Holmes from USA was the most productive author.Journal of AAPOS was the leading journal with the most strabismus publications,whereas the two most highly cited articles were both published in Ophthalmology.Co-occurrence analysis identified pivotal keywords and burst terms,including intermittent exotropia(IXT),acute acquired comitant esotropia(AACE),functional magnetic resonance imaging(fMRI),and surgical treatment,which were confirmed as predominant and frontier topics.CONCLUSION:This study provides a comprehensive bibliometric analysis of strabismus research,revealing the evolution of research hotspots over the past 30y and outlining several cutting-edge directions for future investigation.
基金funded by the Ongoing Research Funding Program-Research Chairs(ORF-RC-2025-2400),King Saud University,Riyadh,Saudi Arabia。
文摘Recent studies indicate that millions of individuals suffer from renal diseases,with renal carcinoma,a type of kidney cancer,emerging as both a chronic illness and a significant cause of mortality.Magnetic Resonance Imaging(MRI)and Computed Tomography(CT)have become essential tools for diagnosing and assessing kidney disorders.However,accurate analysis of thesemedical images is critical for detecting and evaluating tumor severity.This study introduces an integrated hybrid framework that combines three complementary deep learning models for kidney tumor segmentation from MRI images.The proposed framework fuses a customized U-Net and Mask R-CNN using a weighted scheme to achieve semantic and instance-level segmentation.The fused outputs are further refined through edge detection using Stochastic FeatureMapping Neural Networks(SFMNN),while volumetric consistency is ensured through Improved Mini-Batch K-Means(IMBKM)clustering integrated with an Encoder-Decoder Convolutional Neural Network(EDCNN).The outputs of these three stages are combined through a weighted fusion mechanism,with optimal weights determined empirically.Experiments on MRI scans from the TCGA-KIRC dataset demonstrate that the proposed hybrid framework significantly outperforms standalone models,achieving a Dice Score of 92.5%,an IoU of 87.8%,a Precision of 93.1%,a Recall of 90.8%,and a Hausdorff Distance of 2.8 mm.These findings validate that the weighted integration of complementary architectures effectively overcomes key limitations in kidney tumor segmentation,leading to improved diagnostic accuracy and robustness in medical image analysis.
文摘BACKGROUND Pancreatic cancer remains one of the most lethal malignancies worldwide,with a poor prognosis often attributed to late diagnosis.Understanding the correlation between pathological type and imaging features is crucial for early detection and appropriate treatment planning.AIM To retrospectively analyze the relationship between different pathological types of pancreatic cancer and their corresponding imaging features.METHODS We retrospectively analyzed the data of 500 patients diagnosed with pancreatic cancer between January 2010 and December 2020 at our institution.Pathological types were determined by histopathological examination of the surgical spe-cimens or biopsy samples.The imaging features were assessed using computed tomography,magnetic resonance imaging,and endoscopic ultrasound.Statistical analyses were performed to identify significant associations between pathological types and specific imaging characteristics.RESULTS There were 320(64%)cases of pancreatic ductal adenocarcinoma,75(15%)of intraductal papillary mucinous neoplasms,50(10%)of neuroendocrine tumors,and 55(11%)of other rare types.Distinct imaging features were identified in each pathological type.Pancreatic ductal adenocarcinoma typically presents as a hypodense mass with poorly defined borders on computed tomography,whereas intraductal papillary mucinous neoplasms present as characteristic cystic lesions with mural nodules.Neuroendocrine tumors often appear as hypervascular lesions in contrast-enhanced imaging.Statistical analysis revealed significant correlations between specific imaging features and pathological types(P<0.001).CONCLUSION This study demonstrated a strong association between the pathological types of pancreatic cancer and imaging features.These findings can enhance the accuracy of noninvasive diagnosis and guide personalized treatment approaches.
基金Supported by the National Basic Research Program of China(2011CB707904)the Natural Science Foundation of China(61472289)Hubei Province Natural Science Foundation of China(2015CFB254)
文摘Segmenting the lesion regions from the ultrasound （US） images is an important step in the intra-operative planning of some computer-aided therapies. High-Intensity Focused Ultrasound （HIFU）, as a popular computer-aided therapy, has been widely used in the treatment of uterine fibroids. However, such segmentation in HIFU remains challenge for two reasons： （1） the blurry or missing boundaries of lesion regions in the HIFU images and （2） the deformation of uterine fibroids caused by the patient＇s breathing or an external force during the US imaging process, which can lead to complex shapes of lesion regions. These factors have prevented classical active contour-based segmentation methods from yielding desired results for uterine fibroids in US images. In this paper, a novel active contour-based segmentation method is proposed, which utilizes the correlation information of target shapes among a sequence of images as prior knowledge to aid the existing active contour method. This prior knowledge can be interpreted as a unsupervised clustering of shapes prior modeling. Meanwhile, it is also proved that the shapes correlation has the low-rank property in a linear space, and the theory of matrix recovery is used as an effective tool to impose the proposed prior on an existing active contour model. Finally, an accurate method is developed to solve the proposed model by using the Augmented Lagrange Multiplier （ALM）. Experimental results from both synthetic and clinical uterine fibroids US image sequences demonstrate that the proposed method can consistently improve the performance of active contour models and increase the robustness against missing or misleading boundaries, and can greatly improve the efficiency of HIFU therapy.
基金Supported by Research and Development Foundation for Major Science and Technology from Shenyang,No.19-112-4-105Big Data Foundation for Health Care from China Medical University,No.HMB201902105Natural Fund Guidance Plan from Liaoning,No.2019-ZD-0743.
文摘BACKGROUND It is evident that an accurate evaluation of T and N stage rectal cancer is essential for treatment planning.It has not been extensively investigated whether texture features derived from diffusion-weighted imaging(DWI)images and apparent diffusion coefficient(ADC)maps are associated with the extent of local invasion(pathological stage T1-2 vs T3-4)and nodal involvement(pathological stage N0 vs N1-2)in rectal cancer.AIM To predict different stages of rectal cancer using texture analysis based on DWI images and ADC maps.METHODS One hundred and fifteen patients with pathologically proven rectal cancer,who underwent preoperative magnetic resonance imaging,including DWI,were enrolled,retrospectively.The ADC measurements(ADCmean,ADCmin,ADCmax)as well as texture features,including the gray level co-occurrence matrix parameters,the gray level run-length matrix parameters and wavelet parameters were calculated based on DWI(b=0 and b=1000)images and the ADC maps.Independent sample t-tests or Mann-Whitney U tests were used for statistical analysis.Multivariate logistic regression analysis was conducted to establish the models.The predictive performance was validated by receiver operating characteristic curve analysis.RESULTS Dissimilarity,sum average,information correlation and run-length nonuniformity from DWIb=0 images,gray level nonuniformity,run percentage and run-length nonuniformity from DWIb=1000 images,and dissimilarity and run percentage from ADC maps were found to be independent predictors of local invasion(stage T3-4).The area under the operating characteristic curve of the model reached 0.793 with a sensitivity of 78.57%and a specificity of 74.19%.Sum average,gray level nonuniformity and the horizontal components of symlet transform(SymletH)from DWIb=0 images,sum average,information correlation,long run low gray level emphasis and SymletH from DWIb=1000 images,and ADCmax,ADCmean and information correlation from ADC maps were identified as independent predictors of nodal involvement.The area under the operating characteristic curve of the model reached 0.802 with a sensitivity of 80.77%and a specificity of 68.25%.CONCLUSION Texture features extracted from DWI images and ADC maps are useful clues for predicting pathological T and N stages in rectal cancer.
基金Supported by Interdisciplinary Program of Shanghai Jiao Tong University,No.YG2024 LC01National Natural Science Foundation of China,No.62406190.
文摘Confocal laser endomicroscopy(CLE)has become an indispensable tool in the diagnosis and detection of gastrointestinal(GI)diseases due to its high-resolution and high-contrast imaging capabilities.However,the early-stage imaging changes of gastrointestinal disorders are often subtle,and traditional medical image analysis methods rely heavily on manual interpretation,which is time-consuming,subject to observer variability,and inefficient for accurate lesion identification across large-scale image datasets.With the introduction of artificial intelligence(AI)technologies,AI-driven CLE image analysis systems can automatically extract pathological features and have demonstrated significant clinical value in lesion recognition,classification diagnosis,and malignancy prediction of GI diseases.These systems greatly enhance diagnostic efficiency and early detection capabilities.This review summarizes the applications of AI-assisted CLE in GI diseases,analyzes the limitations of current technologies,and explores future research directions.It is expected that the deep integration of AI and confocal imaging technologies will provide strong support for precision diagnosis and personalized treatment in the field of gastrointestinal disorders.
