Heart sound model based on DenseNet121 architecture for diagnosis of aortic stenosis: A prospective clinical trial
- VernacularTitle: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
- Author:
Zhengda CHEN
1
,
2
;
Bo FU
3
;
Jianyu WANG
1
,
2
;
Nan JIANG
3
;
Zhigang GUO
3
Author Information
1. Department of Cardiovascular Surgery, Tianjin Chest Hospital, Tianjin, 300222, P. R. China;
2. Tianjin Medical University, Tianjin, 300070, P. R. China
3. Department of Cardiovascular Surgery, Tianjin Chest Hospital, Tianjin, 300222, P. R. China
- Publication Type:Journal Article
- Keywords:
Aortic stenosis;
deep learning;
heart sounds;
artificial intelligence
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2023;30(04):514-521
- CountryChina
- Language:Chinese
-
Abstract:
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. Results A total of 100 patients including 11 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.
