AI-Assisted Sequential Graf Assessment of Infant Hip Ultrasound: A Retrospective Single-Centre Evaluation in a Referral Cohort

Abstract

Graf-method ultrasound supports early assessment of developmental dysplasia of the hip (DDH). This study assessed a sequential artificial-intelligence-assisted workflow and clinicians’ Checklist I decisions with and without computer-aided diagnosis (CAD). This retrospective single-centre study of referred infants used 1803 development images from 679 examinations and separate cohorts of 1024 workflow and 100 reader images; Graf IV hips were excluded. ResTransUNet segmented eight Checklist I structures to assess reportability. Fifteen clinicians assessed the reader images unaided and four weeks later with CAD. Stand-alone Checklist I accuracy was 93.0% (93/100; 95% CI 86.3–96.6). Of 1024 workflow images, 846 received final outputs; Checklist II non-compliance precision was 48.1%. Among accepted images, the non-type-I endpoint had 81.0% accuracy, 82.7% sensitivity and 80.4% specificity; exact Graf-type agreement was 40.1%. Across entering non-type-I images, 167/266 (62.8%) received a correct positive output. Mean absolute errors were 3.59∘ for the α angle and 11.91∘ for the β angle, with a β-angle bias of +11.07∘. Clinician accuracy was 72.3% unaided and 90.2% assisted—an observed 17.9 percentage-point difference (95% CI 10.0–26.1); 26 correct decisions became incorrect on AI-error cases. The workflow supported Checklist I assessment, but fixed order precludes causal CAD inference; gate failures and angle errors require refinement and prospective evaluation before clinical implementation.