Evidence map›Paper›PMID 40295351›Full record

ArticleSkeletal radiology2025

Are artificial intelligence generated lower extremity radiographic measurements accurate in a cohort with implants?

Holden Archer, Shuda Xia, Seth Reine, Louis Camilo Vazquez, Oganes Ashikyan, Parham Pezeshk, Ajay Kohli, Yin Xi, Joel E Wells, Allan Hummer and 2 more

Abstract read
PubMed Publisher
In one paragraph

Article in Skeletal radiology, 2025. 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

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

12 authors.

Holden ArcherDepartment of Orthopaedic Surgery, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, UT Southwestern, Dallas, Tx, 75390 - 9178, USA.
Shuda XiaDepartment of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA.
Seth ReineDepartment of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA.
Louis Camilo VazquezDepartment of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA.
Oganes AshikyanDepartment of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA.
Parham PezeshkDepartment of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA.
Ajay KohliDepartment of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA.
Yin XiDepartment of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA.
Joel E WellsDepartment of Orthopaedic Surgery, Baylor Scott & White, 5220 W University Dr, McKinney, TX, 75071, USA.
Allan HummerIB Lab GmbH, Zehetnergasse 6/2/2, 1140, Vienna, Austria.
Matthew DifrancoIB Lab GmbH, Zehetnergasse 6/2/2, 1140, Vienna, Austria.
Avneesh ChhabraDepartment of Orthopaedic Surgery, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, UT Southwestern, Dallas, Tx, 75390 - 9178, USA. avneesh.chhabra@utsouthwestern.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveLeg length discrepancy (LLD) and malalignment of the lower extremity can lead to pain and increased risk of osteoarthritis. Radiographic measurements on anteroposterior (AP) full-length radiographs can be used to assess LLD and lower extremity alignment. The primary aim of this study was to evaluate the accuracy of AI software in performing lower extremity radiographic measurements in patients with implants. The secondary aim was to compare its efficiency to that of radiologists. MATERIALS AND

methodsThis study used the following eight angles and five lengths: hip-knee-angle (HKA), anatomical-tibiofemoral angle (aTFA), anatomical-mechanical-axis angle (AMA), joint-line-convergence angle (JLCA), mechanical-lateral-proximal-femur-angle (mLPFA), mechanical-lateral-distal-femur-angle (mLDFA), mechanical-medial-proximal-tibia-angle (mMPTA), mechanical-lateral-distal-tibia- angle (mLDTA), femur length, tibia length, full leg length, leg-length-discrepancy (LLD), and mechanical-axis-deviation (MAD). Two radiologists and AI software independently performed these measurements on 156 legs. The statistical methods used to assess AI performance were intraclass correlation coefficient (ICC) and Bland-Altman analysis.

resultsThe AI generated output for 129/156 legs. 11/13 of the variables showed excellent agreement (ICC ≥ 0.75) between AI and the readers. Bland Altman performance targets were met for 5/13 variables. The mean (standard deviation) reading time for the AI and two readers, respectively, was 38 (6) seconds, 181 (41) seconds, and 214 (77) seconds.

conclusionIn a cohort with lower extremity metal implants, AI-based leg length measurements were fast and accurate although most angular measurements were not.

Indexed as

Artificial IntelligenceLeg Length InequalityLower ExtremityProstheses and ImplantsRadiographic Image Interpretation, Computer-AssistedAdultAgedCohort StudiesFemaleHumansMaleMiddle AgedReproducibility of ResultsRetrospective StudiesSoftwareArthroplasties, Replacement, KneeArtificial intelligenceBone malalignmentLeg length inequality

Identifiers

PMID40295351

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.