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基于DenseNet121架构的心音模型诊断主动脉瓣狭窄的前瞻性临床研究

Heart sound model based on DenseNet121 architecture for diagnosis of aortic stenosis:A prospective clinical trial
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摘要 目的通过基于DenseNet121架构的深度学习模型对主动脉瓣狭窄患者的心音进行识别,探索其在临床上筛查主动脉瓣狭窄的应用潜力。方法前瞻性纳入2021年6月—2022年2月天津市胸科医院主动脉瓣疾病患者,采集其心音,并收集临床资料。应用采集的心音数据建立深度学习模型,对其进行训练、验证与测试。最后使用测试结果绘制受试者工作特征曲线及精确度-召回率曲线评价模型性能。结果共纳入100例患者,包括11例无症状患者。其中主动脉瓣狭窄患者组(狭窄组)50例,男30例、女20例,年龄(68.18±10.63)岁;无主动脉瓣疾病患者组(阴性组)50例,男26例、女24例,年龄(45.98±12.51)岁。模型对从主动脉瓣狭窄患者于临床环境采集的心音数据具有优秀的区分能力:准确度为91.67%,灵敏度为90.00%,特异度为92.50%,受试者工作特征曲线下面积为0.917。结论基于深度学习应用心音诊断主动脉瓣狭窄的模型在临床筛查上具有优秀的应用前景,能为主动脉瓣狭窄患者的早期识别提供新思路。 Objective To identify the heart sounds of aortic stenosis by deep learning model based on DenseNet121 architecture,and to explore its application potential in clinical screening aortic stenosis.Methods We prospectively collected heart sounds and clinical data of patients with aortic stenosis in Tianjin Chest Hospital,from June 2021 to February 2022.The collected heart sound data were used to train,verify and test a deep learning model.We evaluated the performance of the model by drawing receiver operating characteristic curve and precision-recall curve.ResultsA total of 100 patients including 1l asymptomatic patients were included.There were 50 aortic stenosis patients with 30 males and 20 females at an average age of 68.18±10.63 years in an aortic stenosis group(stenosis group).And 50 patients without aortic valve disease were in a negative group,including 26 males and 24 females at an average age of 45.98±12.51 years.The model had an excellent ability to distinguish heart sound data collected from patients with aortic stenosis in clinical settings:accuracy at 91.67%,sensitivity at 90.00%,specificity at 92.50%,and area under receiver operating characteristic curve was 0.917.Conclusion The model of heart sound diagnosis of aortic stenosis based on deep learning has excellent application prospects in clinical screening,which can provide a new idea for the early identification of patients with aortic stenosis.
作者 陈正大 付博 王建宇 姜楠 郭志刚 CHEN Zhengda;FU Bo;WANG Jianyu;JIANG Nan;GUO Zhigang(Department of Cardiovascular Surgery,Tianjin Chest Hospital,Tianjin,300222,P.R.China;Tianjin Medical University,Tianjin,300070,P.R.China)
出处 《中国胸心血管外科临床杂志》 CSCD 北大核心 2023年第4期514-521,共8页 Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
基金 国家重点研发计划项目(2020YFC2008100) 天津市医学重点学科建设项目 天津大学医工结合项目(20JCZDJC00810)。
关键词 主动脉瓣狭窄 深度学习 心音 人工智能 Aortic stenosis deep learning heart sounds artificial intelligence
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