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

Abstract

Objectives: This study delves into the cutting-edge field of deep learning techniques, particularly deep convolutional neural networks (DCNNs), which have demonstrated unprecedented potential in assisting radiologists and orthopedic surgeons in precisely identifying meniscal tears. This research aims to evaluate the effectiveness of deep learning models in recognizing, localizing, describing, and categorizing meniscal tears in magnetic resonance images (MRIs).

Materials and methods: This systematic review was rigorously conducted, strictly following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Extensive searches were conducted on MEDLINE (PubMed), Web of Science, Cochrane Library, and Google Scholar. All identified articles underwent a comprehensive risk of bias analysis. Predictive performance values were either extracted or calculated for quantitative analysis, including sensitivity and specificity. The meta-analysis was performed for all prediction models that identified the presence and location of meniscus tears.

Results: This study's findings underscore that a range of deep learning models exhibit robust performance in detecting and classifying meniscal tears, in one case surpassing the expertise of musculoskeletal radiologists. Most studies in this review concentrated on identifying tears in the medial or lateral meniscus and even precisely locating tears-whether in the anterior or posterior horn-with exceptional accuracy, as demonstrated by AUC values ranging from 0.83 to 0.94.

Conclusions: Based on these findings, deep learning models have showcased significant potential in analyzing knee MR images by learning intricate details within images. They offer precise outcomes across diverse tasks, including segmenting specific anatomical structures and identifying pathological regions. Contributions: This study focused exclusively on DL models for identifying and localizing meniscus tears. It presents a meta-analysis that includes eight studies for detecting the presence of a torn meniscus and a meta-analysis of three studies with low heterogeneity that localize and classify the menisci. Another novelty is the analysis of arthroscopic surgery as ground truth. The quality of the studies was assessed against the CLAIM checklist, and the risk of bias was determined using the QUADAS-2 tool.

Keywords: MRI; classification; deep learning model; diagnosis; meniscus tear.

Figure 3

ROC curve (left); area under the ROC curve (AUC) (right).

Figure 1

Orientation-based classification of meniscal tears.

Figure 2

DCNN architecture for knee MRIs [16].

Figure 4

Literature search pipeline.

Figure 5

Histogram of individual items of the CLAIM checklist.

Figure 6

Other information group of items and their observance in the twelve studies [12,13,41,42,43,44,45,46,47,48,49,50].

Figure 7

Overall conformance with the CLAIM checklist [12,13,41,42,43,44,45,46,47,48,49,50].

Figure 8

Diagram for risk of bias.

Figure 9

Concerns regarding applicability.

Figure 10

The QUADAS-2 score assesses the risk of bias [12,13,41,42,43,44,45,46,47,48,49,50].

Figure 11

Forest plot representing the reported sensitivity and specificity values for meniscal tear identification analysis. Receiver operating curves (ROCs) for meniscal tear identification analysis and the SROC curve [12,13,43,45,46,47,48,50].

Figure 11

Forest plot representing the reported sensitivity and specificity values for meniscal tear identification analysis. Receiver operating curves (ROCs) for meniscal tear identification analysis and the SROC curve [12,13,43,45,46,47,48,50].

Figure 12

Forest plot representing the reported sensitivity and specificity values for medial meniscal tear identification analysis. Receiver operating curves (ROCs) for medial meniscal tear identification analysis and the SROC curve [43,45,48].

Figure 13

Forest plot representing the reported sensitivity and specificity values for lateral meniscal tear identification analysis, receiver operating curves (ROCs) for lateral meniscal tear identification analysis, and the SROC curve [43,45,48].

Figure 14

AUC performance diagram [12,13,41,42,43,44,45,46,47,48,49,50].

Figure 15

The DL pipeline.