Artificial intelligence to support ultrasound in the detection of developmental dysplasia of the hip

Aims: Ultrasound is a highly sensitive method to detect developmental dysplasia of the hip (DDH). However, the cost of expert sonographers performing the tests is an important factor preventing wider adoption of this technique. AI has been used to enable non-specialists to undertake imaging in other disciplines, lowering costs and improving patient access. This scoping review aims to assess and map the available evidence pertaining to AI-assisted ultrasound for DDH detection. Methods: The Association for Computing Machinery (ACM) Digital Library, EMBASE, OVID MEDLINE, PubMed, COCHRANE library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Australian New Zealand Clinical Trials Registry (ANZCTR), and Institute of Electrical and Electronics Engineers (IEEE) Xplore databases were searched. Additionally, we searched clinical trial registries from International Committee of Medical Journal Editors (ICMJE) and the World Health Organization (WHO), as well as publicly available commercial material from Exo.AI. Results: A total of 600 records were identified, of which 41 discussed the use of AI-assisted ultrasound for DDH. The search of commercial sources revealed one FDA-approved device, which featured in two of the records scrutinized in the review. Common challenges across studies included limited access to large, high-quality imaging datasets, resulting in poor generalizability due to the small sample sizes used in model training and testing. There was poor transparency in the patient population selected, insufficient reporting on model inference time, and no cost-effectiveness analyses. Conclusion: AI-based assistance for ultrasound detection of DDH shows promise, but the evidence base is incomplete. Future research should focus on standardizing development processes, improving transparency in study reporting, considering the varied perspectives of multidisciplinary teams, and prioritize comprehensive health economic evaluations. Addressing these challenges will be critical for successful development and integration of AI into this area of clinical practice.