Deep learning-based automated angle measurement for flatfoot diagnosis in weight-bearing lateral radiographs.

ABSTRACT: This study aimed to develop and evaluate a deep learning-based system for the automatic measurement of angles (specifically, Meary's angle and calcaneal pitch) in weight-bearing lateral radiographs of the foot for flatfoot diagnosis. We utilized 3960 lateral radiographs, either from the left or right foot, sourced from a pool of 4000 patients to construct and evaluate a deep learning-based model. These radiographs were captured between June and November 2021, and patients who had undergone total ankle replacement surgery or ankle arthrodesis surgery were excluded. Various methods, including correlation analysis, Bland-Altman plots, and paired T-tests, were employed to assess the concordance between the angles automatically measured using the system and those assessed by clinical experts. T

SUBMITTER: Noh WJ 

PROVIDER: S-EPMC11310201 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature