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Automated weight-bearing foot measurements using an artificial intelligence-based software.
Lassalle, Louis; Regnard, Nor-Eddine; Ventre, Jeanne; Marty, Vincent; Clovis, Lauryane; Zhang, Zekun; Nitche, Nicolas; Guermazi, Ali; Laredo, Jean-Denis.
Afiliação
  • Lassalle L; Réseau Imagerie Sud Francilien, Lieusaint, France. louis.lassalle@gmail.com.
  • Regnard NE; Clinique du Mousseau, Ramsay Santé, Evry, France. louis.lassalle@gmail.com.
  • Ventre J; , Gleamer, Paris, France. louis.lassalle@gmail.com.
  • Marty V; Réseau Imagerie Sud Francilien, Lieusaint, France.
  • Clovis L; Clinique du Mousseau, Ramsay Santé, Evry, France.
  • Zhang Z; , Gleamer, Paris, France.
  • Nitche N; , Gleamer, Paris, France.
  • Guermazi A; , Gleamer, Paris, France.
  • Laredo JD; , Gleamer, Paris, France.
Skeletal Radiol ; 2024 Jun 17.
Article em En | MEDLINE | ID: mdl-38880791
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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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Skeletal Radiol Ano de publicação: 2024 Tipo de documento: Article País de afiliação: França

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Skeletal Radiol Ano de publicação: 2024 Tipo de documento: Article País de afiliação: França