Artificial intelligence applied to magnetic resonance imaging reliably detects the presence, but not the location, of meniscus tears: a systematic review and meta-analysis

Abstract

Objectives To review and compare the accuracy of convolutional neural networks (CNN) for the diagnosis of meniscal tears in the current literature and analyze the decision-making processes utilized by these CNN algorithms. Materials and methods PubMed, MEDLINE, EMBASE, and Cochrane databases up to December 2022 were searched in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) statement. Risk of analysis was used for all identified articles. Predictive performance values, including sensitivity and specificity, were extracted for quantitative analysis. The meta-analysis was divided between AI prediction models identifying the presence of meniscus tears and the location of meniscus tears. Results Eleven articles were included in the final review, with a total of 13,467 patients and 57,551 images. Heterogeneity was statistically significantly large for the sensitivity of the tear identification analysis (I2 = 79%). A higher level of accuracy was observed in identifying the presence of a meniscal tear over locating tears in specific regions of the meniscus (AUC, 0.939 vs 0.905). Pooled sensitivity and specificity were 0.87 (95% confidence interval (CI) 0.80–0.91) and 0.89 (95% CI 0.83–0.93) for meniscus tear identification and 0.88 (95% CI 0.82–0.91) and 0.84 (95% CI 0.81–0.85) for locating the tears. Conclusions AI prediction models achieved favorable performance in the diagnosis, but not location, of meniscus tears. Further studies on the clinical utilities of deep learning should include standardized reporting, external validation, and full reports of the predictive performances of these models, with a view to localizing tears more accurately. Clinical relevance statement Meniscus tears are hard to diagnose in the knee magnetic resonance images. AI prediction models may play an important role in improving the diagnostic accuracy of clinicians and radiologists. Key Points •Artificial intelligence (AI) provides great potential in improving the diagnosis of meniscus tears. •The pooled diagnostic performance for artificial intelligence (AI) in identifying meniscus tears was better (sensitivity 87%, specificity 89%) than locating the tears (sensitivity 88%, specificity 84%). •AI is good at confirming the diagnosis of meniscus tears, but future work is required to guide the management of the disease.

A Comprehensive Evaluation of Deep Learning Models on Knee MRIs for the Diagnosis and Classification of Meniscal Tears: A Systematic Review and Meta-Analysis

“…The meta-analysis compared the pooled quantitative diagnostic accuracy values for the DL models [36,37]. We first retrieved or calculated the sensitivity and specificity values or calculated these values if studies did not provide them [38]. Meta-DiSc, version 1.4, explored heterogeneity with various statistics, including chi-square, I-squared, and Spearman correlation tests and accuracy estimates.…”

“…The first group of items (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13) refers to the quality of the title, abstract, introduction, methods section, study design, and data. The next group (14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24) examines the quality of ground truth, data partitions, and model; items (25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37) refer to the training, evaluation, and results section with data and model performance; and items (38)(39)(40)(41)(42) examine the discussion section and other information [33]. Item 41 states that readers can access the full study protocol, which is essential for further investigations and the overall credibility of the study.…”