Deep learning in the diagnosis, staging, and prognosis of osteonecrosis of the femoral head: a systematic review
Abstract
Background: Osteonecrosis of the femoral head (ONFH) is a progressive ischemic bone disorder that often leads to structural collapse and early arthroplasty in adults. Early detection and accurate staging are essential for joint-preserving management. Deep learning (DL) has shown high promise in musculoskeletal imaging; however, the methodological quality and How well these ONFH-related DL models translate to clinical practice remain unclear. This systematic review synthesized current evidence on DL applications for ONFH diagnosis, staging, segmentation, and prognosis across imaging modalities, with emphasis on performance, quality, and explainability.
Methods: Following PRISMA 2020 guidelines, PubMed, Embase, Scopus, and Web of Science were searched on 18 August 2025. Studies applying DL to MRI, CT or radiograph for ONFH diagnosis, segmentation, staging, or prediction were included. Two reviewers independently conducted screening and data extraction; Methodological quality was appraised using the Must AI Criteria-10 (MAIC-10); Due to heterogeneity, findings were narratively synthesized by imaging modality and analytical task.
Results: Among 301 records, 26 studies met inclusion criteria (1993-2025). MRI was the most used modality (n = 14), followed by radiography (n = 7), CT (n = 2), and multimodal frameworks (n = 3). Diagnostic AUCs typically ranged from 0.89 to 0.98, while necrotic-lesion segmentation performance yielded Dice scores between 0.57 and 0.75. Anatomical…
How strong is the evidence?
Evidence Hub reads this as a systematic review, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid. Reported size: 14 participants or records.
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