Diagnosis of knee meniscal injuries using artificial intelligence: A systematic review and meta-analysis of diagnostic performance
Soheil Mohammadi, Ali Jahanshahi, Mohammad Shahrabi Farahani, Mohammad Amin Salehi, Negin Frounchi, Ali Guermazi
Abstract
The aim was to systematically review the literature and perform a meta-analysis to estimate the performance of artificial intelligence (AI) algorithms in detecting meniscal injuries.
Introduction
The menisci are crescent-shaped fibrocartilaginous structures in the knee joint, vital for weight-bearing, shock absorption, and leg movements [1,2]. Meniscal damage is considered the most frequent knee injury and can be either traumatic or degenerative.
Materials and methods
This study was performed based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines with a major focus on Diagnostic Test Accuracy extension (PRISMA-DTA) and adherence to the preferred essential items in writing a systematic review of diagnostic test accuracy studies [16,17].
Results
As a result of our systematic search, 3294 studies were extracted and downloaded to Endnote version 20. After removing duplicates, 2822 studies underwent the title and abstract screening process.
Discussion
The main findings of our study are as follows: AI algorithms had a lower diagnostic accuracy compared with clinicians on internal validation, with pooled sensitivity and specificity of 81% (95% CI: 78, 85) and 78% (95% CI: 72, 83), respectively. Almost the same pattern was applied to the medial and lateral menisci. Still, interestingly, AI algorithms reached a better diagnostic accuracy for medial meniscus damage than lateral meniscus.
Conclusions
To conclude, using AI as a diagnostic tool is burgeoning, especially in image-based diagnoses. The results of this study imply the lower diagnostic performance of AI-based algorithms in knee meniscal injuries compared with radiologists.
Citation: Mohammadi S, Jahanshahi A, Shahrabi Farahani M, Salehi MA, Frounchi N, Guermazi A (2025) Diagnosis of knee meniscal injuries using artificial intelligence: A systematic review and meta-analysis of diagnostic performance. PLoS One 20(6): e0326339. https://doi.org/10.1371/journal.pone.0326339
Editor: Osama Farouk, Assiut University Faculty of Medicine, EGYPT
Received: December 26, 2024; Accepted: May 27, 2025; Published: June 24, 2025
Copyright: © 2025 Mohammadi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting information files.
Funding: The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.
Competing interests: Ali Guermazi is a consultant to Novartis, Coval, Scarcell, 4Moving, Paradigm, Peptinov, Levicept, Pacira, TissueGene, Medipost, ICM and Formation Bio. He is a shareholder of BICL, LLC. This does not alter our adherence to PLOS ONE policies on sharing data and materials. All other authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.
