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Volume 15
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10.3390/diagnostics15060776
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Review

Deep Learning Models to Detect Anterior Cruciate Ligament Injury on MRI: A Comprehensive Review

by
Michele Mercurio
1,2,†,
Federica Denami
1,*,
Dimitra Melissaridou
3,
Katia Corona
4,
Simone Cerciello
5,
Domenico Laganà
6,7,
Giorgio Gasparini
1,2,† on behalf of the IORS and
Roberto Minici
7
1
Department of Orthopaedic and Trauma Surgery, Magna Graecia University, “Renato Dulbecco” University Hospital, 88100 Catanzaro, Italy
2
Research Center on Musculoskeletal Health, MusculoSkeletal Health@UMG, Magna Graecia University, 88100 Catanzaro, Italy
3
1st Department of Orthopaedic Surgery, National and Kapodistrian University of Athens, Attikon Hospital, 12462 Athens, Greece
4
Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, Italy
5
School of Medicine, Saint Camillus University, 00131 Rome, Italy
6
Department of Experimental and Clinical Medicine, Magna Graecia University, 88100 Catanzaro, Italy
7
Radiology Unit, Department of Experimental and Clinical Medicine, Magna Graecia University, “Renato Dulbecco” University Hospital, 88100 Catanzaro, Italy
*
Author to whom correspondence should be addressed.
†
Members of the IORS are indicated in Acknowledgments.
Diagnostics 2025, 15(6), 776; https://doi.org/10.3390/diagnostics15060776
Submission received: 2 February 2025 / Revised: 14 March 2025 / Accepted: 17 March 2025 / Published: 19 March 2025
(This article belongs to the Special Issue Artificial Intelligence in Clinical Medical Imaging: 2nd Edition)
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Abstract

Magnetic resonance imaging (MRI) is routinely used to confirm the suspected diagnosis of anterior cruciate ligament (ACL) injury. Recently, many studies explored the role of artificial intelligence (AI) and deep learning (DL), a sub-category of AI, in the musculoskeletal field and medical imaging. The aim of this study was to review the current applications of DL models to detect ACL injury on MRI, thus providing an updated and critical synthesis of the existing literature and identifying emerging trends and challenges in the field. A total of 23 relevant articles were identified and included in the review. Articles originated from 10 countries, with China having the most contributions (n = 9), followed by the United State of America (n = 4). Throughout the article, we analyzed the concept of DL in ACL tears and provided examples of how these tools can impact clinical practice and patient care. DL models for MRI detection of ACL injury reported high values of accuracy, especially helpful for less experienced clinicians. Time efficiency was also demonstrated. Overall, the deep learning models have proven to be a valid resource, although still requiring technological developments for implementation in daily practice.
Keywords: artificial intelligence; deep learning; knee; ligament; sport; anterior cruciate ligament; magnetic resonance imaging artificial intelligence; deep learning; knee; ligament; sport; anterior cruciate ligament; magnetic resonance imaging

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MDPI and ACS Style

Mercurio, M.; Denami, F.; Melissaridou, D.; Corona, K.; Cerciello, S.; Laganà, D.; Gasparini, G., on behalf of the IORS; Minici, R. Deep Learning Models to Detect Anterior Cruciate Ligament Injury on MRI: A Comprehensive Review. Diagnostics 2025, 15, 776. https://doi.org/10.3390/diagnostics15060776

AMA Style

Mercurio M, Denami F, Melissaridou D, Corona K, Cerciello S, Laganà D, Gasparini G on behalf of the IORS, Minici R. Deep Learning Models to Detect Anterior Cruciate Ligament Injury on MRI: A Comprehensive Review. Diagnostics. 2025; 15(6):776. https://doi.org/10.3390/diagnostics15060776

Chicago/Turabian Style

Mercurio, Michele, Federica Denami, Dimitra Melissaridou, Katia Corona, Simone Cerciello, Domenico Laganà, Giorgio Gasparini on behalf of the IORS, and Roberto Minici. 2025. "Deep Learning Models to Detect Anterior Cruciate Ligament Injury on MRI: A Comprehensive Review" Diagnostics 15, no. 6: 776. https://doi.org/10.3390/diagnostics15060776

APA Style

Mercurio, M., Denami, F., Melissaridou, D., Corona, K., Cerciello, S., Laganà, D., Gasparini, G., on behalf of the IORS, & Minici, R. (2025). Deep Learning Models to Detect Anterior Cruciate Ligament Injury on MRI: A Comprehensive Review. Diagnostics, 15(6), 776. https://doi.org/10.3390/diagnostics15060776

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