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Classification of Muscular Dystrophies from MR Images Improves Using the Swin Transformer Deep Learning Model

التفاصيل البيبلوغرافية
العنوان: Classification of Muscular Dystrophies from MR Images Improves Using the Swin Transformer Deep Learning Model
المؤلفون: Alfonso Mastropietro, Nicola Casali, Maria Giovanna Taccogna, Maria Grazia D’Angelo, Giovanna Rizzo, Denis Peruzzo
المصدر: Bioengineering, Vol 11, Iss 6, p 580 (2024)
بيانات النشر: MDPI AG, 2024.
سنة النشر: 2024
المجموعة: LCC:Technology
LCC:Biology (General)
مصطلحات موضوعية: deep learning, classification, MRI, Swin Transformer, neuromuscular diseases, skeletal muscle, Technology, Biology (General), QH301-705.5
الوصف: Muscular dystrophies present diagnostic challenges, requiring accurate classification for effective diagnosis and treatment. This study investigates the efficacy of deep learning methodologies in classifying these disorders using skeletal muscle MRI scans. Specifically, we assess the performance of the Swin Transformer (SwinT) architecture against traditional convolutional neural networks (CNNs) in distinguishing between healthy individuals, Becker muscular dystrophy (BMD), and limb–girdle muscular Dystrophy type 2 (LGMD2) patients. Moreover, 3T MRI scans from a retrospective dataset of 75 scans (from 54 subjects) were utilized, with multiparametric protocols capturing various MRI contrasts, including T1-weighted and Dixon sequences. The dataset included 17 scans from healthy volunteers, 27 from BMD patients, and 31 from LGMD2 patients. SwinT and CNNs were trained and validated using a subset of the dataset, with the performance evaluated based on accuracy and F-score. Results indicate the superior accuracy of SwinT (0.96), particularly when employing fat fraction (FF) images as input; it served as a valuable parameter for enhancing classification accuracy. Despite limitations, including a modest cohort size, this study provides valuable insights into the application of AI-driven approaches for precise neuromuscular disorder classification, with potential implications for improving patient care.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2306-5354
Relation: https://www.mdpi.com/2306-5354/11/6/580; https://doaj.org/toc/2306-5354
DOI: 10.3390/bioengineering11060580
URL الوصول: https://doaj.org/article/ff9a5d1373894a00afd11070fd080e9a
رقم الأكسشن: edsdoj.ff9a5d1373894a00afd11070fd080e9a
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:23065354
DOI:10.3390/bioengineering11060580