Automatic estimation of hallux valgus angle using deep neural network with axis-based annotation.

ABSTRACT:

Objectives

We developed the deep neural network (DNN) model to automatically measure hallux valgus angle (HVA) and intermetatarsal angle (IMA) on foot radiographs. The objective is to assess the accuracy of the model by comparing to the manual measurement of foot and ankle surgeons.

Materials and methods

A DNN was developed to predict the bone axes of the first proximal phalanx and all metatarsals from the first to the fifth in foot radiographs. The dataset used for model development consisted of 1798 radiographs collected from a population-based cohort and patients at our foot and ankle clinic. The retrospective validation cohort comprised of 92 radiographs obtained from 92 consecutive patients visiting our foot and ankle clinic. The mean absolute error (MAE) between automa

SUBMITTER: Takeda R 

PROVIDER: S-EPMC11410836 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature