Lung cancer remains a leading cause of mortality globally,with particularly high incidence rates in China.This review investigates the pivotal role of artificial intelligence(AI)in the clinical trials of lung cancer d...Lung cancer remains a leading cause of mortality globally,with particularly high incidence rates in China.This review investigates the pivotal role of artificial intelligence(AI)in the clinical trials of lung cancer drugs,aiming to address the challenges of drug development and clinical research processes.The objective is to explore how AI technologies,including machine learning and deep learning,enhance clinical trials’efficiency,accuracy,and personalization in this context.Our methodology involved a comprehensive literature search and analysis,focusing on integrating AI into various stages of clinical trials,namely,preclinical drug discovery,trial design,participant recruitment,and data analysis.The results indicate that AI demonstrates proficiency in integrating and analyzing extensive datasets,facilitating the identification of novel drug targets,and repurposing existing drugs.This capability enhances insights crucial for drug development,optimizes trial designs,streamlines participant recruitment,and conducts precise data analyses of trial outcomes.In conclusion,this review highlights the transformative potential of AI in lung cancer clinical trials and emphasizes the need for high-quality data collection and interpretability improvement to realize its benefits fully.Future research could focus on integrating multimodal approaches and multi-omics data into the entire process of clinical trials on lung cancer drugs to improve patient treatment outcomes and alleviate the global burden of lung cancer.展开更多
Artificial lung membranes as the core module of the extracorporeal membrane oxygenation technology(ECMO)execute the function of extracorporeal blood-gas barrier accomplishing CO_(2)/O_(2)exchange with blood.However,th...Artificial lung membranes as the core module of the extracorporeal membrane oxygenation technology(ECMO)execute the function of extracorporeal blood-gas barrier accomplishing CO_(2)/O_(2)exchange with blood.However,the unsatisfactory hemocompatibility and difficulty in functionalization are the promi-nent challenges faced by current artificial lung membrane materials.In this study,polyethersulfone(PES)composite membranes with self-anticoagulant property and high gas exchange efficient are fabricated by blending PES matrix with poly(vinylamine)(PVAm)modified carboxylic carbon nanotubes(mCNTs)and citrate-based poly(octamethylene-citrate)(POC)pre-polymers.The mCNTs construct specific gas transfer channels within the composite membranes to enhance the gas permeability,while the POC pre-polymers provide anticoagulant property based on the chelation to blood Ca^(2+)and the inactivation effect to in-trinsic coagulation factors.Importantly,directed by the actual ECMO gas exchange mode,we design a gas-liquid convectional circulation device that could evaluate gas exchange efficiency for the composite membranes under mimetic ECMO state.Therefore,this strategy not only proposes a new design method of advanced artificial lung membranes to solve the practical challenges in the current ECMO technology,but also establishes a scientific testing method to evaluate the gas exchange performance for new-type artificial lung membrane materials in the future.展开更多
The object of our present study is to develop a piecewise constant hazard model by using an Artificial Neural Network (ANN) to capture the complex shapes of the hazard functions, which cannot be achieved with conventi...The object of our present study is to develop a piecewise constant hazard model by using an Artificial Neural Network (ANN) to capture the complex shapes of the hazard functions, which cannot be achieved with conventional survival analysis models like Cox proportional hazard. We propose a more convenient approach to the PEANN created by Fornili et al. to handle a large amount of data. In particular, it provides much better prediction accuracies over both the Poisson regression and generalized estimating equations. This has been demonstrated with lung cancer patient data taken from the Surveillance, Epidemiology and End Results (SEER) program. The quality of the proposed model is evaluated by using several error measurement criteria.展开更多
Lung cancer is the most common cause of cancer death in the world.Early diagnosis,screening and precise individualized treatment can significantly reduce the death rate of lung cancer.Artificial intelligence(AI)has be...Lung cancer is the most common cause of cancer death in the world.Early diagnosis,screening and precise individualized treatment can significantly reduce the death rate of lung cancer.Artificial intelligence(AI)has been shown to be able to help clinicians make more accurate judgments and decisions in many ways.It has been involved in the screening of lung cancer,the judgment of benign and malignant degree of pulmonary nodules,the classification of histological cancer,the differentiation of histological subtypes,the identification of genomics,the judgment of the effectiveness of treatment and even the prognosis.AI has shown that it can be an excellent assistant for clinicians.This paper reviews the application of AI in the field of non-small cell lung cancer and describes the relevant progress.Although most of the studies to evaluate the clinical application of AI in non-small cell lung cancer have not been repeatable and generalizable,the research results highlight the efforts to promote the clinical application of AI technology and influence the future treatment direction.展开更多
Currently,artificial-membrane lungs consist of thousands of hollow fiber membranes where blood flows around the fibers and gas flows inside the fibers,achieving diffusive gas exchange.At both ends of the fibers,the in...Currently,artificial-membrane lungs consist of thousands of hollow fiber membranes where blood flows around the fibers and gas flows inside the fibers,achieving diffusive gas exchange.At both ends of the fibers,the interspaces between the hollow fiber membranes and the plastic housing are filled with glue to separate the gas from the blood phase.During a uniaxial centrifugation process,the glue forms the“potting.”The shape of the cured potting is then determined by the centrifugation process,limiting design possibilities and leading to unfavorable stagnation zones associated with blood clotting.In this study,a new multiaxial centrifugation process was developed,expanding the possible shapes of the potting and allowing for completely new module designs with potentially superior blood flow guidance within the potting margins.Two-phase simulations of the process in conceptual artificial lungs were performed to explore the possibilities of a biaxial centrifugation process and determine suitable parameter sets.A corresponding biaxial centrifugation setup was built to prove feasibility and experimentally validate four conceptual designs,resulting in good agreement with the simulations.In summary,this study shows the feasibility of a multiaxial centrifugation process allowing greater variety in potting shapes,eliminating inefficient stagnation zones and more favorable blood flow conditions in artificial lungs.展开更多
AIM: To evaluate the efficacy and safety of the TECA-I bioartificial liver support system (BALSS) in treating canines with acute liver failure (ALF). METHODS: Ten canines with ALF induced by 80% liver resection receiv...AIM: To evaluate the efficacy and safety of the TECA-I bioartificial liver support system (BALSS) in treating canines with acute liver failure (ALF). METHODS: Ten canines with ALF induced by 80% liver resection received BALSS treatment (BALSS group). Blood was perfused through a hollow fiber tube containing 1X10(10) porcine hepatocytes.Four canines with ALF were treated with BALSS without porcine hepatocytes (control group), and five canines with ALF received drug treatment (drug group). Each treatment lasted 6 hours. RESULTS: BALSS treatment yielded beneficial effects for partial liver resection induced ALF canines with survival and decreased plasma ammonia, ALT, AST and BIL. There was an obvious decrease in PT level and increase in PA level, and there were no changes in the count of lymphocytes, immunoglobulins (IgA, IgG and IgM) and complement (C3 and C4) levels after BALSS treatment. In contrast, for the canines with ALF in non-hepatocyte BALSS group (control group) and drug group, there were no significant changes in ammonia, ALT, AST, BIL, PT and PA levels. ALF canines in BALSS group, control group and drug group lived respectively an average time of 108.0h +/- 12.0h, 24.0h +/- 6.0h and 20.4h +/- 6.4h,and three canines with ALF survived in BALSS group. CONCLUSION: TECA-I BALSS is efficacious and safe for ALF canines induced by partial liver resection.展开更多
Artificial intelligence(AI)is a branch of computer science where machines are trained to imitate human-level intelligence and perform well-defined tasks.AI can provide accurate results as well as analyze vast amounts ...Artificial intelligence(AI)is a branch of computer science where machines are trained to imitate human-level intelligence and perform well-defined tasks.AI can provide accurate results as well as analyze vast amounts of data that cannot be analyzed via conventional statistical methods.AI has been utilized in pulmonary medicine for almost two decades and its utilization continues to expand.AI can help in making diagnoses and predicting outcomes in pulmonary diseases based on clinical data,chest imaging,lung pathology,and pulmonary function testing.AI-based applications enable physicians to use enormous amounts of data and improve their precision in the treatment of pulmonary diseases.Given the growing role of AI in pulmonary medicine,it is important for practitioners caring for patients with pulmonary diseases to understand how AI can work in order to implement it into clinical practices and improve patient care.The goal of this mini-review is to discuss the use of AI in pulmonary medicine and imaging in cases of obstructive lung disease,interstitial lung disease,infections,nodules,and lung cancer.展开更多
1|The Evolution of Lung Cancer Screening Despite the paradigm shift in treatment modalities driven by immunology,mortality from lung cancer remains the highest of all cancers[1],rendering it an unrelenting and formida...1|The Evolution of Lung Cancer Screening Despite the paradigm shift in treatment modalities driven by immunology,mortality from lung cancer remains the highest of all cancers[1],rendering it an unrelenting and formidable public health threat.Fortunately,coordinated global efforts have been made,both in the United States and internationally to reduce lung cancer mortality via primary and secondary screening measures,that is,smoking cessation and population screening[2].展开更多
Lung cancer is the leading cause of cancer-related death around the globe.The treatment and survival rates among lung cancer patients are significantly impacted by early diagnosis.Most diagnostic techniques can identi...Lung cancer is the leading cause of cancer-related death around the globe.The treatment and survival rates among lung cancer patients are significantly impacted by early diagnosis.Most diagnostic techniques can identify and classify only one type of lung cancer.It is crucial to close this gap with a system that detects all lung cancer types.This paper proposes an intelligent decision support system for this purpose.This system aims to support the quick and early detection and classification of all lung cancer types and subtypes to improve treatment and save lives.Its algorithm uses a Convolutional Neural Network(CNN)tool to perform deep learning and a Random Forest Algorithm(RFA)to help classify the type of cancer present using several extracted features,including histograms and energy.Numerous simulation experiments were conducted on MATLAB,evidencing that this system achieves 98.7%accuracy and over 98%precision and recall.A comparative assessment assessing accuracy,recall,precision,specificity,and F-score between the proposed algorithm and works from the literature shows that the proposed system in this study outperforms existing methods in all considered metrics.This study found that using CNNs and RFAs is highly effective in detecting lung cancer,given the high accuracy,precision,and recall results.These results lead us to believe that bringing this kind of technology to doctors diagnosing lung cancer is critical.展开更多
Cancer disease is a deadliest disease cause more dangerous one.By identifying the disease through Artificial intelligence to getting the mage features directly from patients.This paper presents the lung knob division ...Cancer disease is a deadliest disease cause more dangerous one.By identifying the disease through Artificial intelligence to getting the mage features directly from patients.This paper presents the lung knob division and disease characterization by proposing an enhancement calculation.Most of the machine learning techniques failed to observe the feature dimensions leads inaccuracy in feature selection and classification.This cause inaccuracy in sensitivity and specificity rate to reduce the identification accuracy.To resolve this problem,to propose a Chicken Sine Cosine Algorithm based Deep Belief Network to identify the disease factor.The general technique of the created approach includes four stages,such as pre-processing,segmentation,highlight extraction,and the order.From the outset,the Computerized Tomography(CT)image of the lung is taken care of to the division.When the division is done,the highlights are extricated through morphological factors for feature observation.By getting the features are analysed and the characterization is done dependent on the Deep Belief Network(DBN)which is prepared by utilizing the proposed Chicken-Sine Cosine Algorithm(CSCA)which distinguish the lung tumour,giving two classes in particular,knob or non-knob.The proposed system produce high performance as well compared to the other system.The presentation assessment of lung knob division and malignant growth grouping dependent on CSCA is figured utilizing three measurements to be specificity,precision,affectability,and the explicitness.展开更多
The treatment with a mechanical ventilator is required whenever a patient’s respiratory system becomes unable to keep the concentrations of O<sub>2</sub> and CO<sub>2 </sub>in blood at tolerab...The treatment with a mechanical ventilator is required whenever a patient’s respiratory system becomes unable to keep the concentrations of O<sub>2</sub> and CO<sub>2 </sub>in blood at tolerable levels. However, in particular cases, the thoracic artificial lung (TAL) can be regarded as a viable alternative to mechanical ventilation (MV). We aim at studying the effects of mechanical ventilators and thoracic artificial lungs devices will have on the cardiovascular system. We will give careful consideration to cardiovascular energetic parameters, such as left and right ventricular external work, pressure-volume area, and cardiac mechanical efficiency. We simulated both, mechanically ventilated patients who are not subject to the application of an artificial lung and patients who are provided with a thoracic artificial lung (TAL). In the case of a thoracic artificial lung, we involved in our simulation studies all the usual operating modes of a TAL, viz. series mode, parallel mode, and hybrid mode of the TAL with regard to the native lung. In particular, the presented simulation results will contribute to elucidate the specific characteristics of each of the aforementioned operating modes. Generally, the energetic variables are influenced by different values of input TAL resistance in both modes: parallel and in series. In this paper, we concentrated on simulation studies of the effects evoked by TAL assistance on cardiac energetic and some other important circulatory parameters. The simulation results presented show that at all modes of TAL assistance exists a strong dependency on the TAL input variables and on the value of the mean intrathoracic pressure which has been assumed for a given situation.展开更多
The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis andplanning intervention. This research work addresses the major issues pertaining to the field of medical image...The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis andplanning intervention. This research work addresses the major issues pertaining to the field of medical imageprocessing while focusing on lung cancer Computed Tomography (CT) images. In this context, the paper proposesan improved lung cancer segmentation technique based on the strengths of nature-inspired approaches. Thebetter resolution of CT is exploited to distinguish healthy subjects from those who have lung cancer. In thisprocess, the visual challenges of the K-means are addressed with the integration of four nature-inspired swarmintelligent techniques. The techniques experimented in this paper are K-means with Artificial Bee Colony (ABC),K-means with Cuckoo Search Algorithm (CSA), K-means with Particle Swarm Optimization (PSO), and Kmeanswith Firefly Algorithm (FFA). The testing and evaluation are performed on Early Lung Cancer ActionProgram (ELCAP) database. The simulation analysis is performed using lung cancer images set against metrics:precision, sensitivity, specificity, f-measure, accuracy,Matthews Correlation Coefficient (MCC), Jaccard, and Dice.The detailed evaluation shows that the K-means with Cuckoo Search Algorithm (CSA) significantly improved thequality of lung cancer segmentation in comparison to the other optimization approaches utilized for lung cancerimages. The results exhibit that the proposed approach (K-means with CSA) achieves precision, sensitivity, and Fmeasureof 0.942, 0.964, and 0.953, respectively, and an average accuracy of 93%. The experimental results prove thatK-meanswithABC,K-meanswith PSO,K-meanswith FFA, andK-meanswithCSAhave achieved an improvementof 10.8%, 13.38%, 13.93%, and 15.7%, respectively, for accuracy measure in comparison to K-means segmentationfor lung cancer images. Further, it is highlighted that the proposed K-means with CSA have achieved a significantimprovement in accuracy, hence can be utilized by researchers for improved segmentation processes of medicalimage datasets for identifying the targeted region of interest.展开更多
Background:Despite the existence of proposed prognostic features on computed tomography(CT)for patients with advanced-stage non-small cell lung cancer(NSCLC),including radiologists'handcrafted(RaH)features,radiomi...Background:Despite the existence of proposed prognostic features on computed tomography(CT)for patients with advanced-stage non-small cell lung cancer(NSCLC),including radiologists'handcrafted(RaH)features,radiomics features,and deep learning features,comprehensive studies that examine their reproducibility,stability,and biological interpretability remain limited.Methods:The Image Biomarker Standardization Initiative-reported tolerance,Kappa,interclass correlation coefficient,and coefficient of variance were employed to identify reproducible features among RaH,radiomics,and deep learning features derived from NSCLC phantoms.The reproducible features were then input into six artificial intelligence algorithms to develop prognostic models for targeted therapy and immunotherapy using real-world patients with advanced-stage NSCLC to assess their capability and stability.Pathway enrichment was also conducted to explore the underlying biological pathways associated with these reproducible features.Results:Reproducible features in advanced NSCLC included RaH features(9/9,100%),radiomics features(572/1835,31.17%),and deep learning features(3442/4096,84.03%).Among the six artificial intelligence-based prognostic methods,the RaH features exhibited least variability.We also observed that the optimal CT-based prognostic approach differed depending on treatment regimens for advanced NSCLC.In analysis using the Cancer Genome Atlas Program lung adenocarcinoma dataset,the identified reproducible prognostic features,specifically tumor size-derived radiomics and RaH features,showed significant associations with five key signaling pathways involved in NSCLC survival outcomes(false-discovery rate p<0.05).Conclusions:By elucidating the reproducibility,stability,and biological as-sociations of prognostic CT features,our study provides valuable evidence for future NSCLC studies and modeling approaches.展开更多
基金supported by the International Science and Technology Cooperation Program of Guangdong(2022A0505050048)the Natural Science Foundation of Guangdong(2024A1515012369)the Beijing Xisike Clinical Oncology Research Foundation(Y-HS202102-0038).
文摘Lung cancer remains a leading cause of mortality globally,with particularly high incidence rates in China.This review investigates the pivotal role of artificial intelligence(AI)in the clinical trials of lung cancer drugs,aiming to address the challenges of drug development and clinical research processes.The objective is to explore how AI technologies,including machine learning and deep learning,enhance clinical trials’efficiency,accuracy,and personalization in this context.Our methodology involved a comprehensive literature search and analysis,focusing on integrating AI into various stages of clinical trials,namely,preclinical drug discovery,trial design,participant recruitment,and data analysis.The results indicate that AI demonstrates proficiency in integrating and analyzing extensive datasets,facilitating the identification of novel drug targets,and repurposing existing drugs.This capability enhances insights crucial for drug development,optimizes trial designs,streamlines participant recruitment,and conducts precise data analyses of trial outcomes.In conclusion,this review highlights the transformative potential of AI in lung cancer clinical trials and emphasizes the need for high-quality data collection and interpretability improvement to realize its benefits fully.Future research could focus on integrating multimodal approaches and multi-omics data into the entire process of clinical trials on lung cancer drugs to improve patient treatment outcomes and alleviate the global burden of lung cancer.
基金the National Natural Science Foundation of China(Nos.U21A2098,51903171)the Key Research and Development Support Programme of Chengdu(No.2020-YF05-00316-SN)+2 种基金the State Key Research Development Pro-gramme of China(No.2018YFC1106400)the Science and Technol-ogy Project of Sichuan Province(No.2021YJ0188)the State Key Laboratory of Polymer Materials Engineering(No.sklpme2020-3-06).
文摘Artificial lung membranes as the core module of the extracorporeal membrane oxygenation technology(ECMO)execute the function of extracorporeal blood-gas barrier accomplishing CO_(2)/O_(2)exchange with blood.However,the unsatisfactory hemocompatibility and difficulty in functionalization are the promi-nent challenges faced by current artificial lung membrane materials.In this study,polyethersulfone(PES)composite membranes with self-anticoagulant property and high gas exchange efficient are fabricated by blending PES matrix with poly(vinylamine)(PVAm)modified carboxylic carbon nanotubes(mCNTs)and citrate-based poly(octamethylene-citrate)(POC)pre-polymers.The mCNTs construct specific gas transfer channels within the composite membranes to enhance the gas permeability,while the POC pre-polymers provide anticoagulant property based on the chelation to blood Ca^(2+)and the inactivation effect to in-trinsic coagulation factors.Importantly,directed by the actual ECMO gas exchange mode,we design a gas-liquid convectional circulation device that could evaluate gas exchange efficiency for the composite membranes under mimetic ECMO state.Therefore,this strategy not only proposes a new design method of advanced artificial lung membranes to solve the practical challenges in the current ECMO technology,but also establishes a scientific testing method to evaluate the gas exchange performance for new-type artificial lung membrane materials in the future.
文摘The object of our present study is to develop a piecewise constant hazard model by using an Artificial Neural Network (ANN) to capture the complex shapes of the hazard functions, which cannot be achieved with conventional survival analysis models like Cox proportional hazard. We propose a more convenient approach to the PEANN created by Fornili et al. to handle a large amount of data. In particular, it provides much better prediction accuracies over both the Poisson regression and generalized estimating equations. This has been demonstrated with lung cancer patient data taken from the Surveillance, Epidemiology and End Results (SEER) program. The quality of the proposed model is evaluated by using several error measurement criteria.
文摘Lung cancer is the most common cause of cancer death in the world.Early diagnosis,screening and precise individualized treatment can significantly reduce the death rate of lung cancer.Artificial intelligence(AI)has been shown to be able to help clinicians make more accurate judgments and decisions in many ways.It has been involved in the screening of lung cancer,the judgment of benign and malignant degree of pulmonary nodules,the classification of histological cancer,the differentiation of histological subtypes,the identification of genomics,the judgment of the effectiveness of treatment and even the prognosis.AI has shown that it can be an excellent assistant for clinicians.This paper reviews the application of AI in the field of non-small cell lung cancer and describes the relevant progress.Although most of the studies to evaluate the clinical application of AI in non-small cell lung cancer have not been repeatable and generalizable,the research results highlight the efforts to promote the clinical application of AI technology and influence the future treatment direction.
文摘Currently,artificial-membrane lungs consist of thousands of hollow fiber membranes where blood flows around the fibers and gas flows inside the fibers,achieving diffusive gas exchange.At both ends of the fibers,the interspaces between the hollow fiber membranes and the plastic housing are filled with glue to separate the gas from the blood phase.During a uniaxial centrifugation process,the glue forms the“potting.”The shape of the cured potting is then determined by the centrifugation process,limiting design possibilities and leading to unfavorable stagnation zones associated with blood clotting.In this study,a new multiaxial centrifugation process was developed,expanding the possible shapes of the potting and allowing for completely new module designs with potentially superior blood flow guidance within the potting margins.Two-phase simulations of the process in conceptual artificial lungs were performed to explore the possibilities of a biaxial centrifugation process and determine suitable parameter sets.A corresponding biaxial centrifugation setup was built to prove feasibility and experimentally validate four conceptual designs,resulting in good agreement with the simulations.In summary,this study shows the feasibility of a multiaxial centrifugation process allowing greater variety in potting shapes,eliminating inefficient stagnation zones and more favorable blood flow conditions in artificial lungs.
文摘AIM: To evaluate the efficacy and safety of the TECA-I bioartificial liver support system (BALSS) in treating canines with acute liver failure (ALF). METHODS: Ten canines with ALF induced by 80% liver resection received BALSS treatment (BALSS group). Blood was perfused through a hollow fiber tube containing 1X10(10) porcine hepatocytes.Four canines with ALF were treated with BALSS without porcine hepatocytes (control group), and five canines with ALF received drug treatment (drug group). Each treatment lasted 6 hours. RESULTS: BALSS treatment yielded beneficial effects for partial liver resection induced ALF canines with survival and decreased plasma ammonia, ALT, AST and BIL. There was an obvious decrease in PT level and increase in PA level, and there were no changes in the count of lymphocytes, immunoglobulins (IgA, IgG and IgM) and complement (C3 and C4) levels after BALSS treatment. In contrast, for the canines with ALF in non-hepatocyte BALSS group (control group) and drug group, there were no significant changes in ammonia, ALT, AST, BIL, PT and PA levels. ALF canines in BALSS group, control group and drug group lived respectively an average time of 108.0h +/- 12.0h, 24.0h +/- 6.0h and 20.4h +/- 6.4h,and three canines with ALF survived in BALSS group. CONCLUSION: TECA-I BALSS is efficacious and safe for ALF canines induced by partial liver resection.
文摘Artificial intelligence(AI)is a branch of computer science where machines are trained to imitate human-level intelligence and perform well-defined tasks.AI can provide accurate results as well as analyze vast amounts of data that cannot be analyzed via conventional statistical methods.AI has been utilized in pulmonary medicine for almost two decades and its utilization continues to expand.AI can help in making diagnoses and predicting outcomes in pulmonary diseases based on clinical data,chest imaging,lung pathology,and pulmonary function testing.AI-based applications enable physicians to use enormous amounts of data and improve their precision in the treatment of pulmonary diseases.Given the growing role of AI in pulmonary medicine,it is important for practitioners caring for patients with pulmonary diseases to understand how AI can work in order to implement it into clinical practices and improve patient care.The goal of this mini-review is to discuss the use of AI in pulmonary medicine and imaging in cases of obstructive lung disease,interstitial lung disease,infections,nodules,and lung cancer.
文摘1|The Evolution of Lung Cancer Screening Despite the paradigm shift in treatment modalities driven by immunology,mortality from lung cancer remains the highest of all cancers[1],rendering it an unrelenting and formidable public health threat.Fortunately,coordinated global efforts have been made,both in the United States and internationally to reduce lung cancer mortality via primary and secondary screening measures,that is,smoking cessation and population screening[2].
基金The authors would like to confirm that this research work was funded by Institutional Fund Projects under Grant No.(IFPIP:646-829-1443)。
文摘Lung cancer is the leading cause of cancer-related death around the globe.The treatment and survival rates among lung cancer patients are significantly impacted by early diagnosis.Most diagnostic techniques can identify and classify only one type of lung cancer.It is crucial to close this gap with a system that detects all lung cancer types.This paper proposes an intelligent decision support system for this purpose.This system aims to support the quick and early detection and classification of all lung cancer types and subtypes to improve treatment and save lives.Its algorithm uses a Convolutional Neural Network(CNN)tool to perform deep learning and a Random Forest Algorithm(RFA)to help classify the type of cancer present using several extracted features,including histograms and energy.Numerous simulation experiments were conducted on MATLAB,evidencing that this system achieves 98.7%accuracy and over 98%precision and recall.A comparative assessment assessing accuracy,recall,precision,specificity,and F-score between the proposed algorithm and works from the literature shows that the proposed system in this study outperforms existing methods in all considered metrics.This study found that using CNNs and RFAs is highly effective in detecting lung cancer,given the high accuracy,precision,and recall results.These results lead us to believe that bringing this kind of technology to doctors diagnosing lung cancer is critical.
文摘Cancer disease is a deadliest disease cause more dangerous one.By identifying the disease through Artificial intelligence to getting the mage features directly from patients.This paper presents the lung knob division and disease characterization by proposing an enhancement calculation.Most of the machine learning techniques failed to observe the feature dimensions leads inaccuracy in feature selection and classification.This cause inaccuracy in sensitivity and specificity rate to reduce the identification accuracy.To resolve this problem,to propose a Chicken Sine Cosine Algorithm based Deep Belief Network to identify the disease factor.The general technique of the created approach includes four stages,such as pre-processing,segmentation,highlight extraction,and the order.From the outset,the Computerized Tomography(CT)image of the lung is taken care of to the division.When the division is done,the highlights are extricated through morphological factors for feature observation.By getting the features are analysed and the characterization is done dependent on the Deep Belief Network(DBN)which is prepared by utilizing the proposed Chicken-Sine Cosine Algorithm(CSCA)which distinguish the lung tumour,giving two classes in particular,knob or non-knob.The proposed system produce high performance as well compared to the other system.The presentation assessment of lung knob division and malignant growth grouping dependent on CSCA is figured utilizing three measurements to be specificity,precision,affectability,and the explicitness.
文摘The treatment with a mechanical ventilator is required whenever a patient’s respiratory system becomes unable to keep the concentrations of O<sub>2</sub> and CO<sub>2 </sub>in blood at tolerable levels. However, in particular cases, the thoracic artificial lung (TAL) can be regarded as a viable alternative to mechanical ventilation (MV). We aim at studying the effects of mechanical ventilators and thoracic artificial lungs devices will have on the cardiovascular system. We will give careful consideration to cardiovascular energetic parameters, such as left and right ventricular external work, pressure-volume area, and cardiac mechanical efficiency. We simulated both, mechanically ventilated patients who are not subject to the application of an artificial lung and patients who are provided with a thoracic artificial lung (TAL). In the case of a thoracic artificial lung, we involved in our simulation studies all the usual operating modes of a TAL, viz. series mode, parallel mode, and hybrid mode of the TAL with regard to the native lung. In particular, the presented simulation results will contribute to elucidate the specific characteristics of each of the aforementioned operating modes. Generally, the energetic variables are influenced by different values of input TAL resistance in both modes: parallel and in series. In this paper, we concentrated on simulation studies of the effects evoked by TAL assistance on cardiac energetic and some other important circulatory parameters. The simulation results presented show that at all modes of TAL assistance exists a strong dependency on the TAL input variables and on the value of the mean intrathoracic pressure which has been assumed for a given situation.
基金the Researchers Supporting Project(RSP2023R395),King Saud University,Riyadh,Saudi Arabia.
文摘The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis andplanning intervention. This research work addresses the major issues pertaining to the field of medical imageprocessing while focusing on lung cancer Computed Tomography (CT) images. In this context, the paper proposesan improved lung cancer segmentation technique based on the strengths of nature-inspired approaches. Thebetter resolution of CT is exploited to distinguish healthy subjects from those who have lung cancer. In thisprocess, the visual challenges of the K-means are addressed with the integration of four nature-inspired swarmintelligent techniques. The techniques experimented in this paper are K-means with Artificial Bee Colony (ABC),K-means with Cuckoo Search Algorithm (CSA), K-means with Particle Swarm Optimization (PSO), and Kmeanswith Firefly Algorithm (FFA). The testing and evaluation are performed on Early Lung Cancer ActionProgram (ELCAP) database. The simulation analysis is performed using lung cancer images set against metrics:precision, sensitivity, specificity, f-measure, accuracy,Matthews Correlation Coefficient (MCC), Jaccard, and Dice.The detailed evaluation shows that the K-means with Cuckoo Search Algorithm (CSA) significantly improved thequality of lung cancer segmentation in comparison to the other optimization approaches utilized for lung cancerimages. The results exhibit that the proposed approach (K-means with CSA) achieves precision, sensitivity, and Fmeasureof 0.942, 0.964, and 0.953, respectively, and an average accuracy of 93%. The experimental results prove thatK-meanswithABC,K-meanswith PSO,K-meanswith FFA, andK-meanswithCSAhave achieved an improvementof 10.8%, 13.38%, 13.93%, and 15.7%, respectively, for accuracy measure in comparison to K-means segmentationfor lung cancer images. Further, it is highlighted that the proposed K-means with CSA have achieved a significantimprovement in accuracy, hence can be utilized by researchers for improved segmentation processes of medicalimage datasets for identifying the targeted region of interest.
基金National Natural Science Foundation of China,Grant/Award Numbers:92259104,82001904。
文摘Background:Despite the existence of proposed prognostic features on computed tomography(CT)for patients with advanced-stage non-small cell lung cancer(NSCLC),including radiologists'handcrafted(RaH)features,radiomics features,and deep learning features,comprehensive studies that examine their reproducibility,stability,and biological interpretability remain limited.Methods:The Image Biomarker Standardization Initiative-reported tolerance,Kappa,interclass correlation coefficient,and coefficient of variance were employed to identify reproducible features among RaH,radiomics,and deep learning features derived from NSCLC phantoms.The reproducible features were then input into six artificial intelligence algorithms to develop prognostic models for targeted therapy and immunotherapy using real-world patients with advanced-stage NSCLC to assess their capability and stability.Pathway enrichment was also conducted to explore the underlying biological pathways associated with these reproducible features.Results:Reproducible features in advanced NSCLC included RaH features(9/9,100%),radiomics features(572/1835,31.17%),and deep learning features(3442/4096,84.03%).Among the six artificial intelligence-based prognostic methods,the RaH features exhibited least variability.We also observed that the optimal CT-based prognostic approach differed depending on treatment regimens for advanced NSCLC.In analysis using the Cancer Genome Atlas Program lung adenocarcinoma dataset,the identified reproducible prognostic features,specifically tumor size-derived radiomics and RaH features,showed significant associations with five key signaling pathways involved in NSCLC survival outcomes(false-discovery rate p<0.05).Conclusions:By elucidating the reproducibility,stability,and biological as-sociations of prognostic CT features,our study provides valuable evidence for future NSCLC studies and modeling approaches.
