BibTex format
@article{Musbahi:2025:10.1002/jeo2.70342,
author = {Musbahi, O and Ahmed, A and Hall, T and Siddique, M and Katyula, K and Cobb, J and van, Arkel R and Jones, G},
doi = {10.1002/jeo2.70342},
journal = {Journal of Experimental Orthopaedics},
title = {Deep learning classification models demonstrate high accuracy and clinical potential in radiograph interpretation in the arthroplasty clinical pathway: a systematic review and meta-analysis},
url = {http://dx.doi.org/10.1002/jeo2.70342},
volume = {12},
year = {2025}
}
RIS format (EndNote, RefMan)
TY - JOUR
AB - PurposeImaging is a cornerstone of the osteoarthritis (OA)-arthroplasty clinical pathway: the continuum of care that patients with OA undergo, from initial diagnosis through to arthroplasty and postoperative follow-up. With growing interest from the orthopaedic community, this meta-analysis broadly evaluates the performance of deep learning algorithms in interpreting radiographs and cross-sectional imaging in this pathway. The authors hypothesise that deep learning algorithms will have comparable performance to clinicians when interpreting radiographs, but not cross-sectional imaging, with negligible difference in diagnostic and prognostic tasks.MethodsOvid Medline, Ovid Embase, Scopus and Web of Science were searched for studies published between January 1, 2012, and April 1, 2024, evaluating deep learning algorithms for diagnostic and prognostic tasks along the pathway. Eligible studies included those that used established diagnostic or surgical candidacy assessments as ground truth. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool, and pooled sensitivity and specificity were determined. Hierarchical summary receiver operating characteristic curves assessed diagnostic performance.ResultsThe meta-analysis of artificial intelligence (AI) interpretation included 66 studies, with a pooled sensitivity of 0.88 (95% confidence interval [CI]: 0.81–0.92) and a pooled specificity of 0.91 (95% CI: 0.87–0.94). Sensitivity and specificity values were higher for AI interpretation of radiographs (55 studies) compared to cross-sectional imaging, with no significant difference in performance between diagnostic and prognostic tasks. For clinician interpretation, 11 studies showed a pooled sensitivity of 0.76 (95% CI: 0.64–0.85) and a pooled specificity of 0.79 (95% CI: 0.59–0.90).ConclusionsThis meta-analysis highlights the potential of deep learning algorithms to improve efficiency in OA classification and progno
AU - Musbahi,O
AU - Ahmed,A
AU - Hall,T
AU - Siddique,M
AU - Katyula,K
AU - Cobb,J
AU - van,Arkel R
AU - Jones,G
DO - 10.1002/jeo2.70342
PY - 2025///
SN - 2197-1153
TI - Deep learning classification models demonstrate high accuracy and clinical potential in radiograph interpretation in the arthroplasty clinical pathway: a systematic review and meta-analysis
T2 - Journal of Experimental Orthopaedics
UR - http://dx.doi.org/10.1002/jeo2.70342
VL - 12
ER -