Evidence map›Paper›PMID 37484038›Full record

ArticlePaediatrics & child health2023

Remote diagnostic imaging using artificial intelligence for diagnosing hip dysplasia in infants: Results from a mixed-methods feasibility pilot study.

Jackie Libon, Candice Ng, Allan Bailey, Abhilash Hareendranathan, Reg Joseph, Sukhdeep Dulai

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Paediatrics & child health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 5 pooled it
4.8field-weighted citation impact, top 5% of its field
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

16 citing papers in PubMed, 5 syntheses or guidelines pooled it, 15 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Artificial Intelligence-Assisted Wireless Handheld Ultrasound for Screening Developmental Dysplasia of the Hip in Infants.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2026
    Article
  7. Review
  8. Article
  9. Review
  10. Review
  11. Review
  12. Review
  13. Review
  14. Review
  15. Article
  16. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Jackie LibonHealth Cities, Edmonton, Alberta.ORCID https://orcid.org/0000-0002-5020-3223
Candice NgHealth Cities, Edmonton, Alberta.
Allan BaileyDepartment of Family Medicine, University of Alberta, Edmonton, Alberta.
Abhilash HareendranathanDepartment of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta.
Reg JosephHealth Cities, Edmonton, Alberta.
Sukhdeep DulaiDepartment of Surgery, University of Alberta, Edmonton, Alberta.
Alberta Health Services · CAUniversity of Alberta · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: Infant hip dysplasia or Developmental Dysplasia of the Hip (DDH) occurs in 1-2% of births worldwide and leads to hip arthritis if untreated. We sought to evaluate the feasibility of implementing an artificial intelligence-enhanced portable ultrasound tool for infant hip dysplasia (DDH) screening in primary care, through determining its effectiveness in practice and evaluating patient and provider feedback. Methods: A US-FDA-cleared artificial intelligence (AI) screening device for DDH (MEDO-Hip) was added to routine well-child visits from age 6 to 10 weeks. A total of 306 infants were screened during a 1-year pilot study within three family medicine clinics in Alberta, Canada. Patient and provider satisfaction were quantified using the System Usability Survey (SUS), while provider perceptions were further investigated through semi-structured interviews. Results: Provider and user surveys commonly identified best features of the tool as immediate diagnosis, offering reassurance/knowledge and avoiding travel, and noted technical glitches most frequently as a barrier. A total of 369 scans of 306 infants were performed from Feb 1, 2021 until Mar 31, 2022. Eighty percent of hips scanned were normal on initial scans, 14% of scans required a follow-up study in the primary care clinic, and DDH cases were identified and treated at the expected 2% rate (6 infants). Conclusions: It is feasible to implement a point-of-care ultrasound AI screening tool in primary care to screen for infants with DDH. Beyond improved screening and detection, this innovation was well accepted by patients and fee-for-service providers with a culture and history of innovation.

Indexed as

artificial intelligencehip dislocationinfant healthpublic healthradiology

Identifiers

PMID37484038
PMCPMC10362962
OpenAlexW4365139349

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.