JMIR medical informatics · 2026
AI-Assisted Detection of Supraspinatus Tendon Pathologies Using a Hierarchical Deep Learning Model to Improve Clinical Applicability: Development and Evaluation Study.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Chen Kun-Hui, Wu Jacky Chung-Hao, Chang Hsin-Yu, Chiang En-Rung, Ma Hsuan-Hsiao, Wang Hsin-Yi, Lu Henry Horng-Shing, Yang Chih-Yu
Аннотация
BACKGROUND: Supraspinatus tendon pathologies are common causes of shoulder pain. Magnetic resonance imaging (MRI) is the reference imaging method but requires expert interpretation. Automated classification may improve diagnostic consistency and support musculoskeletal imaging workflows.
OBJECTIVE: This study aimed to develop and evaluate a hierarchical deep learning model to classify supraspinatus tendon status as intact tendons, tendinopathy/partial-thickness tears, or full-thickness tears.
METHODS: A total of 1192 shoulder MRI scans were analyzed. The hierarchical system consisted of a left-right orientation classifier, a full-thickness tear detector (model F), and a classifier for distinguishing intact tendons from tendinopathy/partial-thickness tears (model ITP). A flat 3-class model served as a baseline comparator. Performance was evaluated on both an internal test set and an independent external cohort.
RESULTS: On the internal test set, the hierarchical system achieved a system-level sensitivity of 68.1% for tendinopathy/partial-thickness tears, outperforming the flat baseline (57.4%) while maintaining comparable sensitivity for full-thickness tears (hierarchical vs flat: 94.1% vs 95.1%). On the independent external cohort, the sensitivity for tendinopathy/partial-thickness tears was 45.5% for the hierarchical model and 18.2% for the flat baseline. The hierarchical model also showed a numerically higher balanced accuracy (hierarchical vs flat: 68.1% vs 64.5%), macro F1-score, and macro area under the curve, although its overall accuracy was lower (76.4% vs 79.8%).
CONCLUSIONS: A hierarchical deep learning approach that mirrors clinical diagnostic reasoning may improve the recognition of tendinopathy and partial-thickness tears, a challenging category for nonspecialist readers. Given the overlapping CIs, these findings should be interpreted as indicative of a trend rather than definitive improvement. External validation supports feasibility across different MRI sources, though the predominance of data from a single institution limits generalizability and warrants further prospective evaluation.
OBJECTIVE: This study aimed to develop and evaluate a hierarchical deep learning model to classify supraspinatus tendon status as intact tendons, tendinopathy/partial-thickness tears, or full-thickness tears.
METHODS: A total of 1192 shoulder MRI scans were analyzed. The hierarchical system consisted of a left-right orientation classifier, a full-thickness tear detector (model F), and a classifier for distinguishing intact tendons from tendinopathy/partial-thickness tears (model ITP). A flat 3-class model served as a baseline comparator. Performance was evaluated on both an internal test set and an independent external cohort.
RESULTS: On the internal test set, the hierarchical system achieved a system-level sensitivity of 68.1% for tendinopathy/partial-thickness tears, outperforming the flat baseline (57.4%) while maintaining comparable sensitivity for full-thickness tears (hierarchical vs flat: 94.1% vs 95.1%). On the independent external cohort, the sensitivity for tendinopathy/partial-thickness tears was 45.5% for the hierarchical model and 18.2% for the flat baseline. The hierarchical model also showed a numerically higher balanced accuracy (hierarchical vs flat: 68.1% vs 64.5%), macro F1-score, and macro area under the curve, although its overall accuracy was lower (76.4% vs 79.8%).
CONCLUSIONS: A hierarchical deep learning approach that mirrors clinical diagnostic reasoning may improve the recognition of tendinopathy and partial-thickness tears, a challenging category for nonspecialist readers. Given the overlapping CIs, these findings should be interpreted as indicative of a trend rather than definitive improvement. External validation supports feasibility across different MRI sources, though the predominance of data from a single institution limits generalizability and warrants further prospective evaluation.