Evidence map›Paper›PMID 41961108›Full record

SynthesisPediatric radiology2026

Diagnostic accuracy of artificial intelligence-assisted infant hip ultrasound interpretation for developmental dysplasia of the hip: systematic review and meta-analysis.

Aryan Azmi, Adam McArthur, Steel McDonald, Abhilash Hareendranathan, Jacob Jaremko

Abstract readSystematic ReviewMeta-Analysis
PubMed Publisher
In one paragraph

Synthesis in Pediatric radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Aryan AzmiFaculty of Medicine and Dentistry, University of Alberta, 2J2.00 Walter C. MacKenzie Health Sciences Centre, T6G 2R7, Edmonton, Canada. aazmi@ualberta.ca.
Adam McArthurDepartment of Radiology & Diagnostic Imaging, University of Alberta, Edmonton, Canada.
Steel McDonaldFaculty of Medicine and Dentistry, University of Alberta, 2J2.00 Walter C. MacKenzie Health Sciences Centre, T6G 2R7, Edmonton, Canada.
Abhilash HareendranathanDepartment of Radiology & Diagnostic Imaging, University of Alberta, Edmonton, Canada.
Jacob JaremkoFaculty of Medicine and Dentistry, University of Alberta, 2J2.00 Walter C. MacKenzie Health Sciences Centre, T6G 2R7, Edmonton, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUltrasound is the standard imaging test for infant developmental dysplasia of the hip (DDH) but is highly operator-dependent, leading to variable image quality and classification. Artificial intelligence (AI)-assisted ultrasound may standardize acquisition and interpretation and support DDH screening beyond specialist centers.

objectiveTo evaluate the diagnostic accuracy and feasibility of AI-assisted ultrasound for infant DDH. MATERIALS AND

methodsWe performed a systematic review and diagnostic test-accuracy meta-analysis of studies enrolling infants (≤12 months) undergoing hip ultrasound, in which the index test was AI applied to two-dimensional (2D) or three-dimensional (3D) ultrasound and the reference standard was expert Graf-based interpretation or follow-up consensus. Risk of bias was assessed with QUADAS-2 (diagnostic accuracy bias tool). Sensitivity and specificity were pooled with a bivariate random-effects model.

resultsTwenty-nine studies were eligible; nine provided 2×2 data (6,351 hips) for pooling. Pooled sensitivity was 0.92 (95% CI 0.86-0.95) and specificity 0.96 (95% CI 0.91-0.98). Risk of bias was frequently high or unclear for patient selection and the index test. Feasibility signals included short operator training times (approx. 1-2 h) and scan acquisition time reductions (approx. 20-50%), while economic reporting was limited.

conclusionAI-assisted ultrasound demonstrates high diagnostic accuracy for infant DDH and may help standardize hip imaging and facilitate safe use by nonexpert operators, but larger multicenter studies with external validation and robust economic evaluation are needed.

Indexed as

Artificial IntelligenceDevelopmental Dysplasia of the HipHip Dislocation, CongenitalImage Interpretation, Computer-AssistedHumansInfantInfant, NewbornReproducibility of ResultsSensitivity and SpecificityUltrasonographyArtificial intelligenceDevelopmental dysplasia of the hipDiagnostic accuracyPediatricsSystematic reviewUltrasound

Identifiers

PMID41961108

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.