Accessibility settings

This paper is in the following e-collection/theme issue:

Imaging Informatics (529) Clinical Information and Decision Making (3985) Decision Support for Health Professionals (2413) Machine Learning (3404) Artificial Intelligence (5152) Medical imaging (208)

Published on in Vol 14 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/84804, first published .
Man lying in MRI scanner for medical imaging and diagnosis

AI-Assisted Detection of Supraspinatus Tendon Pathologies Using a Hierarchical Deep Learning Model to Improve Clinical Applicability: Development and Evaluation Study

AI-Assisted Detection of Supraspinatus Tendon Pathologies Using a Hierarchical Deep Learning Model to Improve Clinical Applicability: Development and Evaluation Study

Authors of this article:

Kun-Hui Chen1, 2, 3 Author Orcid Image ;   Jacky Chung-Hao Wu4, 5, 6 Author Orcid Image ;   Hsin-Yu Chang6 Author Orcid Image ;   En-Rung Chiang2, 3 Author Orcid Image ;   Hsuan-Hsiao Ma2, 3 Author Orcid Image ;   Hsin-Yi Wang3, 7 Author Orcid Image ;   Henry Horng-Shing Lu5, 6, 8, 9 Author Orcid Image ;   Chih-Yu Yang1, 10, 11, 12 Author Orcid Image
Article Authors Cited by (1) Tweetations Metrics

Journals

  1. Büyükdoğan H, Sarıtaş T, Tan A, Yalçınkaya A, Abay E, Yücel M, Arıcı V, Doğan N, Şahin A, Ertürk C. YOLO11m-Based Artificial Intelligence Versus Orthopaedic Physician Panels for Arthroscopy-Referenced Three-Category MRI Classification of the Posterosuperior Rotator Cuff: A Multi-Reader Diagnostic Accuracy Study. Diagnostics 2026;16(21):3535 View