4.6 Article

Deep Learning of Ultrasound Imaging for Evaluating Ambulatory Function of Individuals with Duchenne Muscular Dystrophy

Journal

DIAGNOSTICS
Volume 11, Issue 6, Pages -

Publisher

MDPI
DOI: 10.3390/diagnostics11060963

Keywords

Duchenne muscular dystrophy; deep learning; ultrasound imaging

Funding

  1. Ministry of Science and Technology in Taiwan
  2. MOST [109-2223-E-182-001-MY3]
  3. Chang Gung Memorial Hospital at Linkou in Taiwan [CMRPD1K0421, CMRPD1H0381]

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The study utilized deep learning of ultrasound imaging for classifying patients with DMD, with the VGG-19 model demonstrating satisfactory classification performance and feature recognition for evaluating ambulatory function. This suggests that deep learning of muscle ultrasound is a potential strategy for DMD characterization.
Duchenne muscular dystrophy (DMD) results in loss of ambulation and premature death. Ultrasound provides real-time, safe, and cost-effective routine examinations. Deep learning allows the automatic generation of useful features for classification. This study utilized deep learning of ultrasound imaging for classifying patients with DMD based on their ambulatory function. A total of 85 individuals (including ambulatory and nonambulatory subjects) underwent ultrasound examinations of the gastrocnemius for deep learning of image data using LeNet, AlexNet, VGG-16, VGG-16(TL), VGG-19, and VGG-19(TL) models (the notation TL indicates fine-tuning pretrained models). Gradient-weighted class activation mapping (Grad-CAM) was used to visualize features recognized by the models. The classification performance was evaluated using the confusion matrix and receiver operating characteristic (ROC) curve analysis. The results show that each deep learning model endows muscle ultrasound imaging with the ability to enable DMD evaluations. The Grad-CAMs indicated that boundary visibility, muscular texture clarity, and posterior shadowing are relevant sonographic features recognized by the models for evaluating ambulatory function. Of the proposed models, VGG-19 provided satisfying classification performance (the area under the ROC curve: 0.98; accuracy: 94.18%) and feature recognition in terms of physical characteristics. Deep learning of muscle ultrasound is a potential strategy for DMD characterization.

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