Deep Learning-Based Algorithm for the Classification of Left Ventricle Segments by Hypertrophy Severity

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 accor...

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Veröffentlicht in:Journal of imaging Jg. 11; H. 7; S. 244
Hauptverfasser: Baccouch, Wafa, Hasnaoui, Bilel, Benameur, Narjes, Jemai, Abderrazak, Lahidheb, Dhaker, Labidi, Salam
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 20.07.2025
MDPI
Schlagworte:
Algorithms
Artificial intelligence
Artificial neural networks
automatic quantification
Automation
Cardiomyopathy
Cardiovascular disease
cine-MRI
Classification
CNN classification
Comparative analysis
Deep learning
Diagnosis
Disease prevention
Electrocardiography
Heart enlargement
Heart failure
Heart ventricle, Left
Hypertension
Image segmentation
left ventricle hypertrophy
Machine learning
Measurement
Methods
Metric space
Neural networks
Performance measurement
Physiological aspects
regional wall thickness
Regression analysis
Segmentation
Segments
Thickness
Tunisia
cine-MRI
automatic quantification
CNN classification
regional wall thickness
left ventricle hypertrophy
ISSN:2313-433X, 2313-433X
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Zusammenfassung: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.
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ISSN:2313-433X
2313-433X
DOI:10.3390/jimaging11070244