Vol. 9, Issue 3, Part A (2025)

Background: Developmental dysplasia of the hip (DDH) ranges from acetabular immaturity to dislocation; delayed diagnosis causes long-term morbidity. Clinical examination and selective imaging miss many clinically silent cases.
Objective: To synthesise evidence for universal Graf-standard ultrasonography, evaluate artificial-intelligence (AI) performance for DDH detection, and propose a practical, scalable screening framework.
Methods: Narrative synthesis of prospective universal sonography cohorts, meta-analyses of AI models, implementation studies of AI-augmented workflows, and consensus statements (searches to April 2025). Outcomes included sensitivity, specificity, detection of clinically silent DDH, recall/referral rates, treatment initiation and feasibility metrics.
Results: Clinical examination detected roughly 20% of sonographically confirmed DDH in reviewed cohorts. Universal Graf ultrasound markedly improved early detection and reduced late diagnoses. Pooled AI studies reported high performance (pooled sensitivity and specificity reported by recent meta-analyses). Primary-care implementations using AI-augmented handheld ultrasound showed feasible screening with recall rates stabilising around 10-14% after training and confirmed DDH incidence approximating expected population rates.
Conclusion: Universal Graf-standard ultrasonography, reinforced by AI-assisted imaging, offers a precise, scalable pathway for early DDH detection. Country-level implementation requires standardised acquisition, training, internal recall loops and governance to preserve specificity and equity.