RAMAS-Net: a module-optimized convolutional network model for aortic valve stenosis recognition in echocardiography
IntroductionAortic stenosis (AS) is a valvular heart disease that obstructs normal blood flow from the left ventricle to the aorta due to pathological changes in the valve, leading to impaired cardiac function. Echocardiography is a key diagnostic tool for AS; however, its accuracy is influenced by...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1587307/full |
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| author | Yejia Gan Wanzhong Huang Yan Deng Xiaoying Xie Yuanyuan Gu Yaozhuang Zhou Qian Zhang Maosheng Zhang Yangchun Liu |
| author_facet | Yejia Gan Wanzhong Huang Yan Deng Xiaoying Xie Yuanyuan Gu Yaozhuang Zhou Qian Zhang Maosheng Zhang Yangchun Liu |
| author_sort | Yejia Gan |
| collection | DOAJ |
| description | IntroductionAortic stenosis (AS) is a valvular heart disease that obstructs normal blood flow from the left ventricle to the aorta due to pathological changes in the valve, leading to impaired cardiac function. Echocardiography is a key diagnostic tool for AS; however, its accuracy is influenced by inter-observer variability, operator experience, and image quality, which can result in misdiagnosis. Therefore, alternative methods are needed to assist healthcare professionals in achieving more accurate diagnoses.MethodsWe proposed a deep learning model, RSMAS-Net, for the automated identification and diagnosis of AS using echocardiography. The model enhanced the ResNet50 backbone by replacing Stage 4 with Spatial and Channel Reconstruction Convolution (SCConv) and Multi-Dconv Head Transposed Attention (MDTA) modules, aiming to reduce redundant computations and improve feature extraction capabilities.ResultsThe proposed method was evaluated on the TMED-2 echocardiography dataset, achieving an accuracy of 94.67%, an F1-score of 94.37%, and an AUC of 0.95 for AS identification. Additionally, the model achieved an AUC of 0.93 for AS severity classification on TMED-2. RSMAS-Net outperformed multiple baseline models in recall, precision, parameter efficiency, and inference time. It also achieved an AUC of 0.91 on the TMED-1 dataset.ConclusionRSMAS-Net effectively diagnoses and classifies the severity of AS in echocardiographic images. The integration of SCConv and MDTA modules enhances diagnostic accuracy while reducing model complexity compared to the original ResNet50 architecture. These results highlight the potential of RSMAS-Net in improving AS assessment and supporting clinical decision-making. |
| format | Article |
| id | doaj-art-22af5a3be7554d4c8fce56fe23417cde |
| institution | Kabale University |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-22af5a3be7554d4c8fce56fe23417cde2025-08-20T03:53:43ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-04-011210.3389/fmed.2025.15873071587307RAMAS-Net: a module-optimized convolutional network model for aortic valve stenosis recognition in echocardiographyYejia Gan0Wanzhong Huang1Yan Deng2Xiaoying Xie3Yuanyuan Gu4Yaozhuang Zhou5Qian Zhang6Maosheng Zhang7Yangchun Liu8Department of Information and Management, Guangxi Medical University, Nanning, ChinaDepartment of Cardiology, Jiangbin Hospital of Guangxi Zhuang Autonomous Region, Nanning, ChinaDepartment of Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, ChinaDepartment of Cardiovascular Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, ChinaDepartment of Information and Management, Guangxi Medical University, Nanning, ChinaDepartment of Information and Management, Guangxi Medical University, Nanning, ChinaDepartment of Information and Management, Guangxi Medical University, Nanning, ChinaDepartment of Information and Management, Guangxi Medical University, Nanning, ChinaCardiothoracic Surgery Intensive Care Unit, The First Affiliated Hospital of Guangxi Medical University, Nanning, ChinaIntroductionAortic stenosis (AS) is a valvular heart disease that obstructs normal blood flow from the left ventricle to the aorta due to pathological changes in the valve, leading to impaired cardiac function. Echocardiography is a key diagnostic tool for AS; however, its accuracy is influenced by inter-observer variability, operator experience, and image quality, which can result in misdiagnosis. Therefore, alternative methods are needed to assist healthcare professionals in achieving more accurate diagnoses.MethodsWe proposed a deep learning model, RSMAS-Net, for the automated identification and diagnosis of AS using echocardiography. The model enhanced the ResNet50 backbone by replacing Stage 4 with Spatial and Channel Reconstruction Convolution (SCConv) and Multi-Dconv Head Transposed Attention (MDTA) modules, aiming to reduce redundant computations and improve feature extraction capabilities.ResultsThe proposed method was evaluated on the TMED-2 echocardiography dataset, achieving an accuracy of 94.67%, an F1-score of 94.37%, and an AUC of 0.95 for AS identification. Additionally, the model achieved an AUC of 0.93 for AS severity classification on TMED-2. RSMAS-Net outperformed multiple baseline models in recall, precision, parameter efficiency, and inference time. It also achieved an AUC of 0.91 on the TMED-1 dataset.ConclusionRSMAS-Net effectively diagnoses and classifies the severity of AS in echocardiographic images. The integration of SCConv and MDTA modules enhances diagnostic accuracy while reducing model complexity compared to the original ResNet50 architecture. These results highlight the potential of RSMAS-Net in improving AS assessment and supporting clinical decision-making.https://www.frontiersin.org/articles/10.3389/fmed.2025.1587307/fulldeep learningaortic valve stenosiscardiovascular diseaseechocardiographytransthoracic-echocardiographyartificial intelligence |
| spellingShingle | Yejia Gan Wanzhong Huang Yan Deng Xiaoying Xie Yuanyuan Gu Yaozhuang Zhou Qian Zhang Maosheng Zhang Yangchun Liu RAMAS-Net: a module-optimized convolutional network model for aortic valve stenosis recognition in echocardiography Frontiers in Medicine deep learning aortic valve stenosis cardiovascular disease echocardiography transthoracic-echocardiography artificial intelligence |
| title | RAMAS-Net: a module-optimized convolutional network model for aortic valve stenosis recognition in echocardiography |
| title_full | RAMAS-Net: a module-optimized convolutional network model for aortic valve stenosis recognition in echocardiography |
| title_fullStr | RAMAS-Net: a module-optimized convolutional network model for aortic valve stenosis recognition in echocardiography |
| title_full_unstemmed | RAMAS-Net: a module-optimized convolutional network model for aortic valve stenosis recognition in echocardiography |
| title_short | RAMAS-Net: a module-optimized convolutional network model for aortic valve stenosis recognition in echocardiography |
| title_sort | ramas net a module optimized convolutional network model for aortic valve stenosis recognition in echocardiography |
| topic | deep learning aortic valve stenosis cardiovascular disease echocardiography transthoracic-echocardiography artificial intelligence |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1587307/full |
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