Machine and deep learning models for ligament injury recognition: a systematic review and meta-analysis of imaging and novel diagnostic techniques

ML and DL models demonstrate high diagnostic accuracy in detecting ligament injuries. Their strong performance supports ongoing integration into clinical practice, offering valuable support for musculoskeletal specialists in image interpretation and diagnosis.

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Publication Details

Journal
EFORT Open Reviews
Published
2026-01-01
DOI
https://doi.org/10.1530/eor-2025-0038
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

Machine and deep learning models for ligament injury recognition: a systematic review and meta-analysis of imaging and novel diagnostic techniques

Felipe Feijoo, Carlos Jorquera, Joaquín Villagrán, Emilia Varas
EFORT Open Reviews
Artificial Intelligence in Healthcare and Education
article

Machine and deep learning models for ligament injury recognition: a systematic review and meta-analysis of imaging and novel diagnostic techniques

Felipe Feijoo, Carlos Jorquera, Joaquín Villagrán, Emilia Varas
article en

Abstract

ML and DL models demonstrate high diagnostic accuracy in detecting ligament injuries. Their strong performance supports ongoing integration into clinical practice, offering valuable support for musculoskeletal specialists in image interpretation and diagnosis.

EFORT Open ReviewsVol. 11(1)
Universidad Mayor (CL), Pontificia Universidad Católica de Valparaíso (CL), Universidad de Los Andes, Chile (CL), Clínica MEDS (Chile) (CL)
Openalex Percentile: Top 92%
Artificial Intelligence in Healthcare and Education
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