End-to-end deep learning framework for automated angle estimation in hallux valgus from full-field weight-bearing radiographs
Authors:
Open Access
- 13 January 2026
- journal article
- research article
- Published by Springer Nature in BMC Medical Imaging
- Vol. 26 (1)
Abstract
A fully automated, end-to-end deep learning framework was developed and validated for accurate and reliable estimation of the hallux valgus angle (HVA) andKeywords
Funding Information
- Shaanxi Provincial Natural Science Foundation (Project No. 2025JC-YBMS-927)
- Xi'an Municipal Health Commission (Project No. 2025ms10)
This publication has 28 references indexed in Scilit:
- Deep learning for medical image segmentation: State-of-the-art advancements and challengesInformatics in Medicine Unlocked, 2024
- Automatic estimation of hallux valgus angle using deep neural network with axis-based annotationSkeletal Radiology, 2024
- Labeled temperate hardwood tree stomatal image datasets from seven taxa of Populus and 17 hardwood speciesScientific Data, 2024
- Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation2018
- Tversky Loss Function for Image Segmentation Using 3D Fully Convolutional Deep Networks2017
- U-Net: Convolutional Networks for Biomedical Image Segmentation2015
- LabelMe: A Database and Web-Based Tool for Image AnnotationInternational Journal of Computer Vision, 2007
- Quality-of-life outcome after hallux valgus surgeryQuality of Life Research, 2007
- Thirteen Ways to Look at the Correlation CoefficientThe American Statistician, 1988
- STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENTThe Lancet, 1986