Evidence map›Paper›PMID 37615891›Full record

ArticleEuropean journal of pediatrics2023

A novel approach for screening standard anteroposterior pelvic radiographs in children.

Jia Sha, Luyu Huang, Yaopeng Chen, Jincong Lin, Zongzhi Fan, Yi Li, Yabo Yan

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In one paragraph

Article in European journal of pediatrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 2 pooled it
1.6field-weighted citation impact, top 17% 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

7 citing papers in PubMed, 2 syntheses or guidelines pooled it, 5 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Review
  5. Review
  6. Article
  7. Article
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

7 authors at 2 institutions in 1 country.

Jia ShaDepartment of Orthopedics, Xijing Hospital, Air Force Medical University, No.15 Changle Xi Road, Xi'an, 710032, China.ORCID http://orcid.org/0000-0003-2110-7862
Luyu HuangDepartment of Orthopedics, Xijing Hospital, Air Force Medical University, No.15 Changle Xi Road, Xi'an, 710032, China.ORCID http://orcid.org/0000-0002-9525-6935
Yaopeng ChenSchool of Telecommunications Engineering, Xidian University, Xi'an, China.ORCID http://orcid.org/0000-0001-9810-215X
Jincong LinDepartment of Orthopedics, Xijing Hospital, Air Force Medical University, No.15 Changle Xi Road, Xi'an, 710032, China.ORCID http://orcid.org/0000-0002-2895-6122
Zongzhi FanDepartment of Orthopedics, Xijing Hospital, Air Force Medical University, No.15 Changle Xi Road, Xi'an, 710032, China.ORCID http://orcid.org/0000-0002-7032-2780
Yi LiSchool of Telecommunications Engineering, Xidian University, Xi'an, China.ORCID http://orcid.org/0000-0003-4226-6635
Yabo YanDepartment of Orthopedics, Xijing Hospital, Air Force Medical University, No.15 Changle Xi Road, Xi'an, 710032, China. yanyabo@fmmu.edu.cn.ORCID http://orcid.org/0000-0001-7307-0112
Xijing Hospital · CNXidian University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Anteroposterior pelvic radiography is the first-line imaging modality for diagnosing developmental dysplasia of the hip (DDH). Nonstandard radiographs with pelvic malposition make the correct diagnosis of DDH challenging. However, as the only method available for screening standard pelvic radiographs, traditional manual assessment is relatively laborious and potentially erroneous. We retrospectively collected 3,247 pelvic radiographs. There were 2,887 radiographs randomly selected to train and optimize the AI model. Then 362 radiographs were used to test the model's diagnostic performance. Its diagnostic accuracy was assessed using receiver operating characteristic (ROC) curves and measurement consistency using Bland-Altman plots. In 362 radiographs, the AI model's area under ROC curves, accuracy, sensitivity, and specificity for quality assessment was 0.993, 99.4% (360/362), 98.6% (138/140), and 100.0% (222/222), respectively. Compared with clinicians, the 95% limits of agreement (Bland-Altman analysis) for pelvic tilt index (PTI) and pelvic rotation index (PRI), as determined by the model, were -0.052-0.072 and -0.088-0.055, respectively.

conclusionsThe artificial intelligence-assisted method was more efficient and highly consistent with clinical experts. This method can be used for real-time validation of the quality of pelvic radiographs in current picture archiving and communications systems (PACS). WHAT IS KNOWN: • Nonstandard pediatric radiographs with pelvic malposition make the correct diagnosis of developmental dysplasia of the hip (DDH) challenging. • Traditional manual assessment remains the only method available for screening standard pediatric pelvic radiographs, which is relatively laborious and potentially erroneous. WHAT IS NEW: • This study proposed an artificial intelligence-assisted model to assess the quality of pediatric pelvic radiographs accurately and efficiently. • We recommend the integration of the model into current picture archiving and communications systems (PACS) for real-time screening of standard pediatric pelvic radiographs.

Indexed as

Artificial IntelligenceDevelopmental Dysplasia of the HipChildHumansPelvisRadiographyRetrospective StudiesDevelopmental dysplasia of the hipMask R-CNNPelvic radiographQuality

Identifiers

PMID37615891
OpenAlexW4386117404

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.