2026 · Clinics in Orthopedic Surgery
ViTPose가 술전과 금속물 포함 술후 영상에서 HVA·IMA를 자동 측정할 수 있을까?
내부 MAE는 HVA 1.09°, IMA 0.76°였습니다. 외부 MAE는 HVA 1.33°, IMA 0.81°였습니다.
연구 구조
- 01대상
개발 2,985 images: 훈련 2,375, 검증 297, 내부 시험 313. 외부 검증 300 images; 내부 술후 subgroup 19 images.
- 02방법
Transformer 기반 자세 추정 모델을 개발했습니다. MAE·ICC(2)·Bland–Altman 분석으로 평가하고 외부 두 기관에서 검증했습니다.
- 03보고 결과
내부 MAE는 HVA 1.09°, IMA 0.76°였습니다. 외부 MAE는 HVA 1.33°, IMA 0.81°였습니다.
논문의 대상·방법·결과를 구분한 설명용 모식도입니다. 원문 figure가 아닙니다.
대상과 방법
원문에서 확인할 지점
주요 결과
진료·수술에서 읽을 지점
해석의 제한
짧게 정리하면
이 연구가 확인한 것은 무엇인가요?
내부 MAE는 HVA 1.09°, IMA 0.76°였습니다. 외부 MAE는 HVA 1.33°, IMA 0.81°였습니다.
개별 환자의 치료 결정에 바로 적용할 수 있나요?
술후 검증은 19장으로 예비 분석입니다. 다양한 수술과 고정물에 대한 추가 검증이 필요합니다.
Keywords · 주제어
- Hallux valgus
- Artificial intelligence
- Deep learning
- AI
- hallux valgus
- external validation
- surgical planning
Abstract · 원문 초록
Sang Gyo Seo, Donghyuk Kwak, Sehun Kim, Min Gyu Kyung, Dong Yeon Lee, Du Hyun Ro, Jahyung Kim. Externally Validated Automated Measurement of Hallux Valgus on Pre- and Postoperative Weight-Bearing Radiographs Using a Transformer-Based Model. Clinics in Orthopedic Surgery. 2026. doi:10.4055/cios26232.
Background
Accurate measurement of the hallux valgus angle (HVA) and intermetatarsal angle (IMA) is essential for surgical planning and postoperative assessment in hallux valgus. However, manual measurement is time-consuming and subject to interobserver variability. Existing deep learning models are limited by reliance on preoperative radiographs and lack of external validation. Therefore, this study aimed to develop and externally validate a fully automated artificial intelligence (AI)–based system for HVA and IMA measurement on weight-bearing radiographs and to preliminarily evaluate its feasibility in postoperative radiographs containing metallic implants.
Methods
A transformer-based pose estimation architecture (ViTPose) was developed for automated HVA and IMA measurement on weight-bearing anteroposterior foot radiographs. The model was developed using 2,985 radiographs, including 217 postoperative radiographs with metallic implants or hardware-related image artifacts. The dataset was divided into training, validation, and internal test sets of 2,375, 297, and 313 radiographs, respectively. Performance was evaluated using internal and external validation cohorts. Accuracy was assessed using mean absolute error (MAE), intraclass correlation coefficient type 2 (ICC2), and Bland- Altman analysis. Subgroup analyses were performed by deformity severity and postoperative status.
Results
In the internal test cohort, MAEs were 1.09° for HVA and 0.76° for IMA, with excellent agreement with expert measurements (ICC2 = 0.99 and 0.95). External validation on 300 independent radiographs from 2 institutions demonstrated comparable performance against a consensus-reviewed reference standard, with MAEs of 1.33° for HVA and 0.81° for IMA. In the postoperative subgroup of the internal test set (n = 19), the model showed MAEs of 1.09° for HVA and 0.54° for IMA, with ICC2 values of 0.98 and 0.95, respectively. Severity classification agreement between predicted and reference measurements was 89.5% for HVA and 78.6% for IMA. Automated measurement required 0.49 seconds per image, approximately 30-fold faster than manual measurement.
Conclusions
This externally validated transformer-based system provided accurate, fully automated measurement of HVA and IMA on weight-bearing radiographs and showed preliminary feasibility in a limited postoperative subgroup. The proposed approach may serve as a practical AI-based measurement-support tool for preoperative assessment. Broader postoperative application requires further validation in larger cohorts encompassing diverse hallux valgus procedures and fixation constructs.
Copyright © 2026 by The Korean Orthopaedic Association
원래 논문
Externally Validated Automated Measurement of Hallux Valgus on Pre- and Postoperative Weight-Bearing Radiographs Using a Transformer-Based Model
Sang Gyo Seo, Donghyuk Kwak, Sehun Kim, Min Gyu Kyung, Dong Yeon Lee, Du Hyun Ro, Jahyung Kim
Clinics in Orthopedic Surgery · 2026