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Volume 15
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10.3390/diagnostics15111315
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Systematic Review

Artificial Intelligence in Rotator Cuff Tear Detection: A Systematic Review of MRI-Based Models

by
Umile Giuseppe Longo
1,2,*,
Benedetta Bandini
1,2,
Letizia Mancini
1,3,
Mario Merone
1,4,
Emiliano Schena
3,
Alessandro de Sire
5,6,
Pieter D’Hooghe
7,
Leandro Pecchia
1,4 and
Arianna Carnevale
1
1
Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Rome, Italy
2
Research Unit of Orthopaedic and Trauma Surgery, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Álvaro del Portillo, 21, 00128 Rome, Italy
3
Research Unit of Measurements and Biomedical Instrumentation, Università Campus Bio-Medico di Roma, Via Álvaro del Portillo, 21, 00128 Rome, Italy
4
Research Unit of Intelligent Health Technologies, Department of Engineering, Università Campus Bio-Medico di Roma, Via Álvaro del Portillo, 21, 00128 Rome, Italy
5
Department of Medical and Surgical Sciences, University of Catanzaro “Magna Graecia”, 88100 Catanzaro, Italy
6
Research Center on Musculoskeletal Health, MusculoSkeletalHealth@UMG, University of Catanzaro “Magna Graecia”, 88100 Catanzaro, Italy
7
Aspetar Orthopedic and Sports Medicine Hospital, Aspire Zone, Sportscity Street 1, Doha P.O. Box 29222, Qatar
*
Author to whom correspondence should be addressed.
Diagnostics 2025, 15(11), 1315; https://doi.org/10.3390/diagnostics15111315
Submission received: 11 April 2025 / Revised: 18 May 2025 / Accepted: 20 May 2025 / Published: 23 May 2025
(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)
Versions Notes

Abstract

Objective: This descriptive systematic review aimed to assess in the available literature on the current application and overall performance of Artificial Intelligence (AI) models in the diagnosis and classification of Rotator Cuff Tears (RCTs) using MRIs. Methods: The systematic review was performed by two of the authors from 2020 to November 2024. Only diagnostic studies involving AI application to MRI images of the rotator cuff were considered, including supraspinatus and biceps tears. Studies evaluating AI applications to Ultrasound or X-ray, or including only healthy rotator cuffs, were not analyzed in this paper. Results: The coronal plane in the T2 sequence emerged as the predominant imaging protocol, with the VGG network being the most widely utilized AI model. The studies included in this research exhibited a solid performance of the AI models with accuracy, ranging from 71.0% to 100%. The statistical analysis revealed no significant differences (p > 0.05) in accuracy, sensitivity, specificity, or precision between AI and human experts across studies that included such comparisons. Conclusions: While AI can significantly improve diagnostic efficiency and workflow optimization, future studies must focus on external validation, regulatory approval, and AI-human collaboration models to ensure safe and effective integration into orthopedic imaging.
Keywords: artificial intelligence; MRI; rotator cuff; diagnosis artificial intelligence; MRI; rotator cuff; diagnosis

Share and Cite

MDPI and ACS Style

Longo, U.G.; Bandini, B.; Mancini, L.; Merone, M.; Schena, E.; de Sire, A.; D’Hooghe, P.; Pecchia, L.; Carnevale, A. Artificial Intelligence in Rotator Cuff Tear Detection: A Systematic Review of MRI-Based Models. Diagnostics 2025, 15, 1315. https://doi.org/10.3390/diagnostics15111315

AMA Style

Longo UG, Bandini B, Mancini L, Merone M, Schena E, de Sire A, D’Hooghe P, Pecchia L, Carnevale A. Artificial Intelligence in Rotator Cuff Tear Detection: A Systematic Review of MRI-Based Models. Diagnostics. 2025; 15(11):1315. https://doi.org/10.3390/diagnostics15111315

Chicago/Turabian Style

Longo, Umile Giuseppe, Benedetta Bandini, Letizia Mancini, Mario Merone, Emiliano Schena, Alessandro de Sire, Pieter D’Hooghe, Leandro Pecchia, and Arianna Carnevale. 2025. "Artificial Intelligence in Rotator Cuff Tear Detection: A Systematic Review of MRI-Based Models" Diagnostics 15, no. 11: 1315. https://doi.org/10.3390/diagnostics15111315

APA Style

Longo, U. G., Bandini, B., Mancini, L., Merone, M., Schena, E., de Sire, A., D’Hooghe, P., Pecchia, L., & Carnevale, A. (2025). Artificial Intelligence in Rotator Cuff Tear Detection: A Systematic Review of MRI-Based Models. Diagnostics, 15(11), 1315. https://doi.org/10.3390/diagnostics15111315

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