Computer-Aided Diagnosis of Duchenne Muscular Dystrophy Based on Texture Pattern Recognition on Ultrasound Images Using Unsupervised Clustering Algorithms and Deep Learning

Ai-Ho Liao; Chih-Hung Wang; Chong-Yu Wang; Hao-Li Liu; Ho-Chiao Chuang; Wei-Jye Tseng; Wen-Chin Weng; Cheng-Ping Shih; Po-Hsiang Tsui

doi:10.1016/j.ultrasmedbio.2024.03.022

Computer-Aided Diagnosis of Duchenne Muscular Dystrophy Based on Texture Pattern Recognition on Ultrasound Images Using Unsupervised Clustering Algorithms and Deep Learning

Ultrasound Med Biol. 2024 Apr 17:S0301-5629(24)00155-8. doi: 10.1016/j.ultrasmedbio.2024.03.022. Online ahead of print.

Authors

Ai-Ho Liao¹, Chih-Hung Wang², Chong-Yu Wang³, Hao-Li Liu⁴, Ho-Chiao Chuang⁵, Wei-Jye Tseng³, Wen-Chin Weng⁶, Cheng-Ping Shih⁷, Po-Hsiang Tsui⁸

Affiliations

¹ Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan; Department of Biomedical Engineering, National Defense Medical Center, Taipei, Taiwan. Electronic address: aiho@mail.ntust.edu.tw.
² Division of Otolaryngology, Taipei Veterans General Hospital, Taoyuan Branch, Taoyuan, Taiwan; Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei, Taiwan; Department of Otolaryngology-Head and Neck Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
³ Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.
⁴ Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan.
⁵ Department of Mechanical Engineering, National Taipei University of Technology, Taipei, Taiwan.
⁶ Department of Pediatrics, National Taiwan University Hospital, and College of Medicine, National Taiwan University, Taipei, Taiwan; Department of Pediatric Neurology, National Taiwan University Children's Hospital, Taipei, Taiwan.
⁷ Department of Otolaryngology-Head and Neck Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
⁸ Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; Research Center for Radiation Medicine, Chang Gung University, Taoyuan, Taiwan.

PMID: 38637169
DOI: 10.1016/j.ultrasmedbio.2024.03.022

Abstract

Objective: The feasibility of using deep learning in ultrasound imaging to predict the ambulatory status of patients with Duchenne muscular dystrophy (DMD) was previously explored for the first time. The present study further used clustering algorithms for the texture reconstruction of ultrasound images of DMD data sets and analyzed the difference in echo intensity between disease stages.

Methods: k-means (Kms) and fuzzy c-means (FCM) clustering algorithms were used to reconstruct the DMD data-set textures. Each image was reconstructed using seven texture-feature categories, six of which were used as the primary analysis items. The task of automatically identifying the ambulatory function and DMD severity was performed by establishing a machine-learning model.

Results: The experimental results indicated that the Gaussian Naïve Bayes and k-nearest neighbors classification models achieved an accuracy of 86.78% in ambulatory function classification. The decision-tree model achieved an identification accuracy of 83.80% in severity classification. A deep convolutional neural network model was established as the main structure of the deep-learning model while automatic auxiliary interpretation tasks of ambulatory function and severity were performed, and data augmentation was used to improve the recognition performance of the trained model. Both the visual geometry group (VGG)-16 and VGG-19 models achieved 98.53% accuracy in ambulatory-function classification. The VGG-19 model achieved 92.64% accuracy in severity classification.

Conclusion: Regarding the overall results, the Kms and FCM clustering algorithms were used in this study to reconstruct the characteristic texture of the gastrocnemius muscle group in DMD, which was indeed helpful in quantitatively analyzing the deterioration of the gastrocnemius muscle group in patients with DMD at different stages. Subsequent combination of machine-learning and deep-learning technologies can automatically and accurately assist in identifying DMD symptoms and tracking DMD deterioration for long-term observation.

Keywords: Clustering algorithm; Deep learning; Duchenne muscular dystrophy; Machine learning; Ultrasound imaging.