The diagnostic accuracy of artificial intelligence in detecting foot and ankle fractures: a systematic review and meta-analysis of diagnostic test accuracy.
TL;DR
AI algorithms demonstrate high diagnostic accuracy for foot and ankle fracture detection and show meaningful clinical utility as adjunctive tools, however, current evidence is insufficient to support robust occult-fracture-specific conclusions.
Abstract
INTRODUCTION Foot and ankle fractures, including radiographically subtle or occult injuries, present a diagnostic challenge in emergency settings, with missed diagnoses causing severe complications. Artificial intelligence (AI), specifically deep learning, offers a promising adjunct for radiographic interpretation. This systematic review and meta-analysis evaluates the diagnostic test accuracy of AI in detecting foot and ankle fractures and appraises the available evidence for occult fracture detection where reported.
Method
Adhering to PRISMA-DTA guidelines, a systematic search was conducted across PubMed, Embase, and Scopus from inception to 10 May 2026. Studies evaluating AI algorithms for identifying foot and ankle fractures on radiographs or CT were included, with occult-fracture data extracted separately when available. Extracted data populated 2 × 2 contingency tables. We utilized a bivariate random-effects model to calculate pooled sensitivity, specificity, and the summary receiver operating characteristic (SROC) curve. Quality was assessed via QUADAS-2.
Results
Sixteen studies encompassing over 37,000 radiographs or images were included. Pooled sensitivity was 92.4% (95% CrI 82.5-96.1%) and pooled specificity was 95.1% (95% CrI 83.3-98.3%). The diagnostic odds ratio was 236.64 (95% CrI 33.56-1087.11). At a 25% pre-test probability, a positive AI result increased post-test probability to 86%, while a negative result reduced it to 3%. Substantial between-study heterogeneity was observed. Only two studies explicitly reported occult fracture cases; therefore, the pooled estimates primarily reflect AI performance for overall foot and ankle fracture detection rather than occult fractures alone.
Conclusion
AI algorithms demonstrate high diagnostic accuracy for foot and ankle fracture detection and show meaningful clinical utility as adjunctive tools. However, current evidence is insufficient to support robust occult-fracture-specific conclusions, and all included studies were retrospective. Prospective validation, standardized reporting of occult fracture subgroups, and real-world impact studies are needed before widespread clinical implementation.