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

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Skeletal Radiology Aims and scope Submit manuscript

Abstract

Objective

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

Methods

Consecutive 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.

Results

Eighty 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.

Conclusion

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

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Data availability

The dataset used and analyzed for the current study is available from the corresponding author upon reasonable request. The dataset used for develo** the AI model, as well as the AI model itself, are components of proprietary software and are therefore not publicly available.

Abbreviations

AI:

Artificial intelligence

MSK:

Musculoskeletal

MAE:

Mean absolute error

ICC:

Intraclass coefficient

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Funding

This study was funded by Gleamer.

Author information

Authors and Affiliations

  1. Réseau Imagerie Sud Francilien, Lieusaint, France

    Louis Lassalle & Nor-eddine Regnard

  2. Clinique du Mousseau, Ramsay Santé, Evry, France

    Louis Lassalle & Nor-eddine Regnard

  3. Gleamer, Paris, France

    Louis Lassalle, Nor-eddine Regnard, Jeanne Ventre, Vincent Marty, Lauryane Clovis, Zekun Zhang, Nicolas Nitche & Jean-Denis Laredo

  4. Department of Radiology, Boston University School of Medicine, Boston, MA, USA

    Ali Guermazi

  5. Service de Radiologie, Institut Mutualiste Montsouris, Paris, France

    Jean-Denis Laredo

  6. Laboratoire (B3OA) de Biomécanique Et Biomatériaux Ostéo-Articulaires, Faculté de Médecine Paris-Cité, Paris, France

    Jean-Denis Laredo

  7. Professeur Émérite d’Imagerie Médicale, Université Paris-Cité, Paris, France

    Jean-Denis Laredo

Authors
  1. Louis Lassalle
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  2. Nor-eddine Regnard
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  4. Vincent Marty
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  7. Nicolas Nitche
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Corresponding author

Correspondence to Louis Lassalle.

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Conflict of interest

LL, NER, JV, VM, LC, ZZ, NN, and JDL are employees of Gleamer. AG is shareholder of BICL, LLC, and consultant to Pfizer, Novartis, TrialSpark, Coval, ICM, Medipost, and TissueGene.

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Lassalle, L., Regnard, Ne., Ventre, J. et al. Automated weight-bearing foot measurements using an artificial intelligence–based software. Skeletal Radiol (2024). https://doi.org/10.1007/s00256-024-04726-z

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  • DOI: https://doi.org/10.1007/s00256-024-04726-z

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