Dyspnea is one of the most common manifestations of patients with pulmonary disease,myocardial dysfunction,and neuromuscular disorder,among other conditions.Identifying the causes of dyspnea in clinical practice,espec...Dyspnea is one of the most common manifestations of patients with pulmonary disease,myocardial dysfunction,and neuromuscular disorder,among other conditions.Identifying the causes of dyspnea in clinical practice,especially for the general practitioner,remains a challenge.This pilot study aimed to develop a computeraided tool for improving the efficiency of differential diagnosis.The disease set with dyspnea as the chief complaint was established on the basis of clinical experience and epidemiological data.Differential diagnosis approaches were established and optimized by clinical experts.The artificial intelligence(AI)diagnosis model was constructed according to the dynamic uncertain causality graph knowledge-based editor.Twenty-eight diseases and syndromes were included in the disease set.The model contained 132 variables of symptoms,signs,and serological and imaging parameters.Medical records from the electronic hospital records of Suining Central Hospital were randomly selected.A total of 202 discharged patients with dyspnea as the chief complaint were included for verification,in which the diagnoses of 195 cases were coincident with the record certified as correct.The overall diagnostic accuracy rate of the model was 96.5%.In conclusion,the diagnostic accuracy of the AI model is promising and may compensate for the limitation of medical experience.展开更多
Disorders of sex development(DSD)are a group of rare complex clinical syndromes with multiple etiologies.Distinguishing the various causes of DSD is quite difficult in clinical practice,even for senior general physici...Disorders of sex development(DSD)are a group of rare complex clinical syndromes with multiple etiologies.Distinguishing the various causes of DSD is quite difficult in clinical practice,even for senior general physicians because of the similar and atypical clinical manifestations of these conditions.In addition,DSD are difficult to diagnose because most primary doctors receive insufficient training for DSD.Delayed diagnoses and misdiagnoses are common for patients with DSD and lead to poor treatment and prognoses.On the basis of the principles and algorithms of dynamic uncertain causality graph(DUCG),a diagnosis model for DSD was jointly constructed by experts on DSD and engineers of artificial intelligence.“Chaining”inference algorithm and weighted logic operation mechanism were applied to guarantee the accuracy and efficiency of diagnostic reasoning under incomplete situations and uncertain information.Verification was performed using 153 selected clinical cases involving nine common DSD-related diseases and three causes other than DSD as the differential diagnosis.The model had an accuracy of 94.1%,which was significantly higher than that of interns and third-year residents.In conclusion,the DUCG model has broad application prospects as a computer-aided diagnostic tool for DSDrelated diseases.展开更多
Importance:Heart sound auscultation is a routinely used physical examination in clinical practice to identify potential cardiac abnormalities. However, accurate interpretation of heart sounds requires specialized trai...Importance:Heart sound auscultation is a routinely used physical examination in clinical practice to identify potential cardiac abnormalities. However, accurate interpretation of heart sounds requires specialized training and experience, which limits its generalizability. Deep learning, a subset of machine learning, involves training artiffcial neural networks to learn from large datasets and perform complex tasks with intricate patterns. Over the past decade, deep learning has been successfully applied to heart sound analysis, achieving remarkable results and accumulating substantial heart sound data for model training. Although several reviews have summarized deep learning algorithms for heart sound analysis, there is a lack of comprehensive summaries regarding the available heart sound data and the clinical applications. Highlights:This review will compile the commonly used heart sound datasets, introduce the fundamentals and state-of-the-art techniques in heart sound analysis and deep learning, and summarize the current applications of deep learning for heart sound analysis, along with their limitations and areas for future improvement. Conclusions:The integration of deep learning into heart sound analysis represents a signiffcant advancement in clinical practice. The growing availability of heart sound datasets and the continuous development of deep learning techniques contribute to the improvement and broader clinical adoption of these models. However, ongoing research is needed to address existing challenges and reffne these technologies for broader clinical use.展开更多
基金This research was funded by the research project entitled“DUCG theory and application of medical aided diagnosis-algorithm of introducing classification variables in DUCG”by the Institute of Internet Industry,Tsinghua University.
文摘Dyspnea is one of the most common manifestations of patients with pulmonary disease,myocardial dysfunction,and neuromuscular disorder,among other conditions.Identifying the causes of dyspnea in clinical practice,especially for the general practitioner,remains a challenge.This pilot study aimed to develop a computeraided tool for improving the efficiency of differential diagnosis.The disease set with dyspnea as the chief complaint was established on the basis of clinical experience and epidemiological data.Differential diagnosis approaches were established and optimized by clinical experts.The artificial intelligence(AI)diagnosis model was constructed according to the dynamic uncertain causality graph knowledge-based editor.Twenty-eight diseases and syndromes were included in the disease set.The model contained 132 variables of symptoms,signs,and serological and imaging parameters.Medical records from the electronic hospital records of Suining Central Hospital were randomly selected.A total of 202 discharged patients with dyspnea as the chief complaint were included for verification,in which the diagnoses of 195 cases were coincident with the record certified as correct.The overall diagnostic accuracy rate of the model was 96.5%.In conclusion,the diagnostic accuracy of the AI model is promising and may compensate for the limitation of medical experience.
基金This research was supported by the National Key Research and Development Program of China(No.2016YFC0901501)CAMS Innovation Fund for Medical Science(No.CAMS-2017-I2M–1-011)the Research Project of the Institute of Internet Industry,Tsinghua University,titled“DUCG theory and application of medical aided diagnosis-algorithm of introducing classification variables in DUCG.”。
文摘Disorders of sex development(DSD)are a group of rare complex clinical syndromes with multiple etiologies.Distinguishing the various causes of DSD is quite difficult in clinical practice,even for senior general physicians because of the similar and atypical clinical manifestations of these conditions.In addition,DSD are difficult to diagnose because most primary doctors receive insufficient training for DSD.Delayed diagnoses and misdiagnoses are common for patients with DSD and lead to poor treatment and prognoses.On the basis of the principles and algorithms of dynamic uncertain causality graph(DUCG),a diagnosis model for DSD was jointly constructed by experts on DSD and engineers of artificial intelligence.“Chaining”inference algorithm and weighted logic operation mechanism were applied to guarantee the accuracy and efficiency of diagnostic reasoning under incomplete situations and uncertain information.Verification was performed using 153 selected clinical cases involving nine common DSD-related diseases and three causes other than DSD as the differential diagnosis.The model had an accuracy of 94.1%,which was significantly higher than that of interns and third-year residents.In conclusion,the DUCG model has broad application prospects as a computer-aided diagnostic tool for DSDrelated diseases.
基金supported by the National Natural Science Foundation of China(No.62102008)the Peking University People’s Hospital Scientific Research Development Funds(RDJP2022-39)the Clinical Medicine Plus X-Young Scholars Project of Peking University,and the Fundamental Research Funds for the Central Universities(PKU2024LCXQ030).
文摘Importance:Heart sound auscultation is a routinely used physical examination in clinical practice to identify potential cardiac abnormalities. However, accurate interpretation of heart sounds requires specialized training and experience, which limits its generalizability. Deep learning, a subset of machine learning, involves training artiffcial neural networks to learn from large datasets and perform complex tasks with intricate patterns. Over the past decade, deep learning has been successfully applied to heart sound analysis, achieving remarkable results and accumulating substantial heart sound data for model training. Although several reviews have summarized deep learning algorithms for heart sound analysis, there is a lack of comprehensive summaries regarding the available heart sound data and the clinical applications. Highlights:This review will compile the commonly used heart sound datasets, introduce the fundamentals and state-of-the-art techniques in heart sound analysis and deep learning, and summarize the current applications of deep learning for heart sound analysis, along with their limitations and areas for future improvement. Conclusions:The integration of deep learning into heart sound analysis represents a signiffcant advancement in clinical practice. The growing availability of heart sound datasets and the continuous development of deep learning techniques contribute to the improvement and broader clinical adoption of these models. However, ongoing research is needed to address existing challenges and reffne these technologies for broader clinical use.
