Journal Article Epublish
PubMed: 40710630 DOI: 10.3390/jimaging11070244
Deep Learning-Based Algorithm for the Classification of Left Ventricle Segments by Hypertrophy Severity.
J Imaging 11(7)
Authors: Wafa Baccouch, Bilel Hasnaoui, Narjes Benameur, Abderrazak Jemai, Dhaker Lahidheb, Salam Labidi
Affiliations
- Research Laboratory of Biophysics and Medical Technologies LR13ES07, Higher Institute of Medical Technologies of Tunis, University of Tunis El Manar, Tunis 1006, Tunisia.
- SERCOM-Lab, Tunisia Polytechnic School, EPT, Carthage University, Tunis 2078, Tunisia.
- SERCOM-Lab, Tunisia Polytechnic School, INSAT, Carthage University, Tunis 1080, Tunisia.
- Faculty of Medicine of Tunis, University of Tunis El Manar, Tunis 1007, Tunisia.
- Department of Cardiology, Military Hospital of Tunis, Tunis 1008, Tunisia.
Abstract
In clinical practice, left ventricle hypertrophy (LVH) continues to pose a considerable challenge, highlighting the need for more reliable diagnostic approaches. This study aims to propose an automated framework for the quantification of LVH extent and the classification of myocardial segments according to hypertrophy severity using a deep learning-based algorithm. The proposed method was validated on 133 subjects, including both healthy individuals and patients with LVH. The process starts with automatic LV segmentation using U-Net and the segmentation of the left ventricle cavity based on the American Heart Association (AHA) standards, followed by the division of each segment into three equal sub-segments. Then, an automated quantification of regional wall thickness (RWT) was performed. Finally, a convolutional neural network (CNN) was developed to classify each myocardial sub-segment according to hypertrophy severity. The proposed approach demonstrates strong performance in contour segmentation, achieving a Dice Similarity Coefficient (DSC) of 98.47% and a Hausdorff Distance (HD) of 6.345 ± 3.5 mm. For thickness quantification, it reaches a minimal mean absolute error (MAE) of 1.01 ± 1.16. Regarding segment classification, it achieves competitive performance metrics compared to state-of-the-art methods with an accuracy of 98.19%, a precision of 98.27%, a recall of 99.13%, and an F1-score of 98.7%. The obtained results confirm the high performance of the proposed method and highlight its clinical utility in accurately assessing and classifying cardiac hypertrophy. This approach provides valuable insights that can guide clinical decision-making and improve patient management strategies.
MeSH
Classification, Hypertrophy, Algorithms, Deep Learning, Learning