ArticleBone & joint open2026
Artificial intelligence to support ultrasound in the detection of developmental dysplasia of the hip : a scoping review.
Article in Bone & joint open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Artificial intelligence to support ultrasound in the detection of developmental dysplasia of the hip : a scoping review.Bone & joint open · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.