Evidence map›Paper›PMID 38880791›Full record

ArticleSkeletal radiology2025

Automated weight-bearing foot measurements using an artificial intelligence-based software.

Louis Lassalle, Nor-Eddine Regnard, Jeanne Ventre, Vincent Marty, Lauryane Clovis, Zekun Zhang, Nicolas Nitche, Ali Guermazi, Jean-Denis Laredo

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Article in Skeletal radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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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

9 authors.

Louis LassalleRéseau Imagerie Sud Francilien, Lieusaint, France. louis.lassalle@gmail.com.
Nor-Eddine RegnardRéseau Imagerie Sud Francilien, Lieusaint, France.
Jeanne Ventre, Gleamer, Paris, France.
Vincent Marty, Gleamer, Paris, France.
Lauryane Clovis, Gleamer, Paris, France.
Zekun Zhang, Gleamer, Paris, France.
Nicolas Nitche, Gleamer, Paris, France.
Ali GuermaziDepartment of Radiology, Boston University School of Medicine, Boston, MA, USA.
Jean-Denis Laredo, Gleamer, Paris, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo assess the accuracy of an artificial intelligence (AI) software (BoneMetrics, Gleamer) in performing automated measurements on weight-bearing forefoot and lateral foot radiographs.

methodsConsecutive forefoot and lateral foot radiographs were retrospectively collected from three imaging institutions. Two senior musculoskeletal radiologists independently annotated key points to measure the hallux valgus, first-second metatarsal, and first-fifth metatarsal angles on forefoot radiographs and the talus-first metatarsal, medial arch, and calcaneus inclination angles on lateral foot radiographs. The ground truth was defined as the mean of their measurements. Statistical analysis included mean absolute error (MAE), bias assessed with Bland-Altman analysis between the ground truth and AI prediction, and intraclass coefficient (ICC) between the manual ratings.

resultsEighty forefoot radiographs were included (53 ± 17 years, 50 women), and 26 were excluded. Ninety-seven lateral foot radiographs were included (51 ± 20 years, 46 women), and 21 were excluded. MAE for the hallux valgus, first-second metatarsal, and first-fifth metatarsal angles on forefoot radiographs were respectively 1.2° (95% CI [1; 1.4], bias =  - 0.04°, ICC = 0.98), 0.7° (95% CI [0.6; 0.9], bias =  - 0.19°, ICC = 0.91) and 0.9° (95% CI [0.7; 1.1], bias = 0.44°, ICC = 0.96). MAE for the talus-first, medial arch, and calcaneal inclination angles on the lateral foot radiographs were respectively 3.9° (95% CI [3.4; 4.5], bias = 0.61° ICC = 0.88), 1.5° (95% CI [1.2; 1.8], bias =  - 0.18°, ICC = 0.95) and 1° (95% CI [0.8; 1.2], bias = 0.74°, ICC = 0.99). Bias and MAE between the ground truth and the AI prediction were low across all measurements. ICC between the two manual ratings was excellent, except for the talus-first metatarsal angle.

conclusionAI demonstrated potential for accurate and automated measurements on weight-bearing forefoot and lateral foot radiographs.

Indexed as

Artificial IntelligenceSoftwareWeight-BearingFemaleFootHallux ValgusHumansMaleMiddle AgedRadiographic Image Interpretation, Computer-AssistedRadiographyReproducibility of ResultsRetrospective StudiesArtificial intelligenceFoot measurementsFoot radiography

Identifiers

PMID38880791

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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.