DCFFNet: A New Dual-channel Cross-Feature Fusion Net for Evaluating the Degree of Aortic Valve Calcification Based on Echocardiographic Images.
Authors
Affiliations (10)
Affiliations (10)
- School of Control Science and Engineering, Shandong University, Jinan, China.
- School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China.
- Icahn School of Medicine at Mount Sinai, Department of Neuroscience and Friedman Brain Institute, New York, USA.
- State Key Laboratory for Innovation and Transformation of Luobing Theory, Jinan, China.
- Key Laboratory of Cardiovascular Remodeling and Function Research, Chinese Ministry of Education, Chinese National Health Commission and Chinese Academy of Medical Sciences, Jinan, China.
- Department of Cardiology, Qilu Hospital of Shandong University, Jinan, China.
- Department of Cardiovascular Surgery, Qilu Hospital, Shandong University, Jinan, China.
- State Key Laboratory for Innovation and Transformation of Luobing Theory, Jinan, China. [email protected].
- Key Laboratory of Cardiovascular Remodeling and Function Research, Chinese Ministry of Education, Chinese National Health Commission and Chinese Academy of Medical Sciences, Jinan, China. [email protected].
- Department of Cardiology, Qilu Hospital of Shandong University, Jinan, China. [email protected].
Abstract
Aortic valve calcification is a common cause of stenosis. Echocardiography, although being the preferred and most prevailing diagnostic technique for aortic valve diseases, lacks effective methods for accurately rating the degree of aortic valve calcification. In this study, a dual-channel cross-feature fusion neural network was developed to classify the degree of aortic valve calcification. The dual-channel input is designed to accept both end-systolic and end-diastolic ultrasound images of a patient, thereby maximizing the retention of vital information from both phases of the cardiac cycle. Feature extraction scale was also dynamically adjusted using the squeeze-and-excitation module. To better integrate multilevel and multiscale information, a dual-branch feature fusion module with cross-feature fusion and multiscale feature extraction was designed, thereby enabling the network to merge global and local feature information. Moreover, to address the specific noise characteristics of ultrasound images and low valve occupancy in the aortic short-axis view, a unified preprocessing algorithm was developed. A total of 420 volunteers were internally selected and classified based on computed tomography scan calcification scores (140 cases per category: healthy, nonsevere, and severe).Each patient contributed 2-4 cardiac cycles, resulting in a final effective dataset of 1092 samples. The classification model achieved an accuracy, precision, F1 score, and recall of 96.79%, 98.59%, 97.97%, and 97.22%, respectively. The artificial intelligence-assisted diagnosis system proposed in this study exhibits high precision in evaluating the degree of aortic valve calcification, positioning echocardiographic examination as a promising alternative in routine aortic valve calcification analysis and screening.