Diagnostic Accuracy of Artificial Intelligence Systems in Early Prediction of Dislocation and Aseptic Loosening After Total Hip Arthroplasty
Journal of Arthroplasty, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1016/j.arth.2026.07.013
- Dergi Adı: Journal of Arthroplasty
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CINAHL, EMBASE, MEDLINE
- Anahtar Kelimeler: artificial intelligence, aseptic loosening, dislocation, postoperative radiograph, total hip arthroplasty
- Marmara Üniversitesi Adresli: Evet
Özet
Background Artificial intelligence (AI) is increasingly used in total hip arthroplasty (THA), yet its role in postoperative imaging remains limited. This study evaluated the diagnostic performance of AI systems in interpreting postoperative radiographs following THA. Methods A total of 1,045 patients who underwent primary THA between 2011 and 2015 were retrospectively analyzed. The first-day postoperative anteroposterior pelvic radiographs were assessed using AI systems to estimate the risk of dislocation and aseptic loosening. During a mean follow-up of 11.6 years (range, 10 to 14), 34 patients developed dislocation, 22 developed aseptic loosening, and 989 patients remained free of complications. Actual complications were confirmed through clinical and radiological records. Sensitivity, specificity, and area under the curve (AUC) values were calculated and compared. Results There was one AI system that demonstrated the highest diagnostic accuracy (AUC 0.91 for dislocation and 0.90 for aseptic loosening), followed by the other systems (0.84 and 0.82) and (0.79 and 0.77), respectively. Repeated analyses produced identical outputs across sessions, confirming reproducibility. Conclusions The AI-based analyses can accurately and consistently evaluate postoperative THA radiographs. There was one system that showed the best balance between sensitivity and specificity, indicating that AI-assisted interpretation may serve as a reliable adjunct to clinical follow-up for early complication prediction.