Evidence Hub

The value of deep learning-based X-ray techniques in detecting and classifying K-L grades of knee osteoarthritis: a systematic review and meta-analysis

Hao-Ming Zhao, Liang Ou, Ziming Zhang, Le Zhang, Ke Liu, Jianjun Kuang

European Radiology, 2024

87 citations

Abstract

Knee osteoarthritis (KOA), a prevalent degenerative joint disease, is primarily diagnosed through X-ray imaging. The Kellgren-Lawrence grading system (K-L) is the gold standard for evaluating KOA severity through X-ray analysis. However, this method is highly subjective and non-quantifiable, limiting its effectiveness in detecting subtle joint changes on X-rays. Recent researchers have been directed towards developing deep-learning (DL) techniques for a more accurate diagnosis of KOA using X-ray images. Despite advancements in these intelligent methods, the debate over their diagnostic sensitivity continues. Hence, we conducted the current meta-analysis. A comprehensive search was conducted in PubMed, Cochrane, Embase, Web of Science, and IEEE up to July 11, 2023. The QUADAS-2 tool was employed to assess the risk of bias in the included studies. Given the multi-classification nature of DL tasks, the sensitivity of DL across different K-L grades was meta-analyzed. A total of 19 studies were included, encompassing 62,158 images. These images consisted of 22,388 for K-L0, 13,415 for K-L1, 15,597 for K-L2, 7768 for K-L3, and 2990 for K-L4. The meta-analysis demonstrated that the sensitivity of DL was 86.74% for K-L0 (95% CI: 80.01%–92.28%), 64.00% for K-L1 (95% CI: 51.81%–75.35%), 75.03% for K-L2 (95% CI: 66.00%–83.09%), 84.76% for K-L3 (95% CI: 78.34%–90.25%), and 90.32% for K-L4 (95% CI: 85.39%–94.40%). The DL multi-classification methods based on X-ray imaging generally…

How strong is the evidence?

Evidence Hub reads this as a meta-analysis. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.

How the five levels of evidence work. The level is assigned automatically from the abstract and can be wrong, so check the paper.

Read the paper

Related studies

Go deeper in Evidence Hub

Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.

Open this study Sign up free