1. Evaluation of a deep learning software for automated measurements on full-leg standing radiographs.
- Author
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Lassalle L, Regnard NE, Durteste M, Ventre J, Marty V, Clovis L, Zhang Z, Nitche N, Ducarouge A, Laredo JD, and Guermazi A
- Abstract
Background: Precise lower limb measurements are crucial for assessing musculoskeletal health; fully automated solutions have the potential to enhance standardization and reproducibility of these measurements. This study compared the measurements performed by BoneMetrics (Gleamer, Paris, France), a commercial artificial intelligence (AI)-based software, to expert manual measurements on anteroposterior full-leg standing radiographs., Methods: A retrospective analysis was conducted on a dataset comprising consecutive anteroposterior full-leg standing radiographs obtained from four imaging institutions. Key anatomical landmarks to define the hip-knee-ankle angle, pelvic obliquity, leg length, femoral length, and tibial length were annotated independently by two expert musculoskeletal radiologists and served as the ground truth. The performance of the AI was compared against these reference measurements using the mean absolute error, Bland-Altman analyses, and intraclass correlation coefficients., Results: A total of 175 anteroposterior full-leg standing radiographs from 167 patients were included in the final dataset (mean age = 49.9 ± 23.6 years old; 103 women and 64 men). Mean absolute error values were 0.30° (95% confidence interval [CI] [0.28, 0.32]) for the hip-knee-ankle angle, 0.75 mm (95% CI [0.60, 0.88]) for pelvic obliquity, 1.03 mm (95% CI [0.91,1.14]) for leg length from the top of the femoral head, 1.45 mm (95% CI [1.33, 1.60]) for leg length from the center of the femoral head, 0.95 mm (95% CI [0.85, 1.04]) for femoral length from the top of the femoral head, 1.23 mm (95% CI [1.12, 1.32]) for femoral length from the center of the femoral head, and 1.38 mm (95% CI [1.21, 1.52]) for tibial length. The Bland-Altman analyses revealed no systematic bias across all measurements. Additionally, the software exhibited excellent agreement with the gold-standard measurements with intraclass correlation coefficient (ICC) values above 0.97 for all parameters., Conclusions: Automated measurements on anteroposterior full-leg standing radiographs offer a reliable alternative to manual assessments. The use of AI in musculoskeletal radiology has the potential to support physicians in their daily practice without compromising patient care standards., Competing Interests: Declarations. Ethics approval and consent to participate: The study was performed in accordance with the ethical standards in the 1964 Declaration of Helsinki. The ethics committee of the Collège des Enseignants en Radiologie de France (CERF), the Centre d’Éthique de la Recherche en Imagerie Médicale (CERIM), approved the study (Institutional Review Board approval number CRM-2209-306). Consent for publication: Consent forms were not required for patients, as the images used in this study were completely unidentifiable and there were no specific details on individuals reported within the manuscript. Furthermore, all patients had been informed about the use of their anonymized data for research purposes, which included clear instructions on how to opt out if they chose to do so. Competing interests: L.L., M.D., J.V., V.M., L.C., Z.Z., N.N., and J.D.L. are employees of Gleamer; N.E.R. is a co-founder and chief medical officer of Gleamer. A.D. is a co-founder and chief technical officer of Gleamer. A.G. is a shareholder of BICL, LLC, and consultant to Pfizer, Novartis, TrialSpark, Coval, ICM, Medipost, and TissueGene., (© 2024. The Author(s).)
- Published
- 2024
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