Evidence map›Paper›PMID 36637464›Full record

ArticlePediatric radiology2023

Dating birth-related clavicular fractures: pediatric radiologists versus artificial intelligence.

Andy Tsai, Jeannette M Pérez-Rosselló, Kirsten Ecklund, Michele M Walters, Paul K Kleinman

Abstract read
In one paragraph

Article in Pediatric radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
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1citing papers in PubMed
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1 · What the graph read from it

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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

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Andy TsaiDepartment of Radiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave., Boston, MA, 02115, USA. andy.tsai@childrens.harvard.edu.ORCID 0000-0002-0089-6463
Jeannette M Pérez-RossellóDepartment of Radiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave., Boston, MA, 02115, USA.
Kirsten EcklundDepartment of Radiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave., Boston, MA, 02115, USA.
Michele M WaltersDepartment of Radiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave., Boston, MA, 02115, USA.
Paul K KleinmanDepartment of Radiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave., Boston, MA, 02115, USA.

Funding

Machine learning Approaches to Radiological Dating of Infant Clavicle Fractures"R21HD108634 · NICHD · BOSTON CHILDREN'S HOSPITAL · PI TSAI, ANDY · 2023 to 2024
$487k
NICHD NIH HHS R21 HD108634
6 · The paper itself

Abstract

backgroundFracture dating from skeletal surveys is crucial in the diagnosis and investigation of infant abuse. However, this task is challenging because of the subjective nature of the radiologic interpretation and the lack of ground truth. Researchers have used birth-related clavicle fractures as a surrogate to study the radiographic pattern of healing; however, they did not elucidate the accuracy performance of the radiologists in dating fractures.

objectiveTo determine the accuracy of radiologists in dating birth-related clavicle fractures and compare their performance to that achieved by computer algorithm. MATERIALS AND

methodsWe used a previously assembled birth-related clavicle fracture database consisting of 416 anteroposterior clavicle radiographs as the study cohort. The average and standard deviation of the fracture age within this database were 24 days and 18 days, respectively. Three blinded radiologists independently estimated the ages of the clavicle fractures depicted in the radiographs within the database. We compared these estimation results to those made by a recently published deep-learning (DL) model conducted with the identical infant cohort. We calculated standard error metrics to compare the accuracy performances of the radiologists and the computer model.

resultsThe intra- and inter-reader agreements of the fracture age estimates by the radiologists were moderate to good. The radiologists estimated the fracture ages with a mean absolute error (MAE) of 6.1-7.1 days, and standard deviation of the absolute error of 6.3-8.3 days. The accuracy performances of the three radiologists were not significantly different from one another. In comparison, the DL model estimated the age of clavicle fractures with an MAE of 4.2 days, significantly lower than all of the radiologists (P < 0.001).

conclusionThree experienced pediatric radiologists dated clavicular fractures with moderate-good intra- and inter-reader agreements. The correlations between the radiologists' estimates and the ground truth were moderate to good. The fracture ages assigned by the DL model showed superior correlation with the ground truth compared to radiologists' dating estimates.

Indexed as

ClavicleFractures, BoneArtificial IntelligenceChildFracture HealingHumansInfantInfant, NewbornRadiologistsRetrospective StudiesArtificial intelligenceChild abuseClavicleDeep learningFractureFracture datingHealingInfantsRadiography

Identifiers

PMID36637464
PMCPMC10630084

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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.