2026 · Clinics in Orthopedic Surgery
ViTPoseは術前と金属インプラントを含む術後X線画像でHVA・IMAを自動測定できるか？
内部検証のMAEはHVA 1.09°、IMA 0.76°、外部検証ではそれぞれ1.33°、0.81°でした。
研究構造
- 01対象
開発2,985画像：訓練2,375、検証297、内部テスト313。外部検証300画像、内部術後サブグループ19画像。
- 02方法
Transformerを用いた姿勢推定モデルを開発し、MAE・ICC(2)・Bland–Altman解析で評価したうえで、外部の2施設で検証しました。
- 03報告された結果
内部検証のMAEはHVA 1.09°、IMA 0.76°、外部検証ではそれぞれ1.33°、0.81°でした。
研究対象・方法・結果を分けて示した説明用の模式図です。論文の原図ではありません。
対象と方法
オリジナルのチェックポイント
主な結果
手術に関する読書のポイント
解釈の限界
簡単に説明しましょう
この研究で何が確認されましたか?
内部検証のMAEはHVA 1.09°、IMA 0.76°、外部検証ではそれぞれ1.33°、0.81°でした。
治療決定に直接適用できるか?
術後検証は19画像の予備的解析です。さまざまな手術と固定器具で追加検証が必要です。
Keywords・キーワード
- Hallux valgus
- Artificial intelligence
- Deep learning
- AI
- hallux valgus
- external validation
- surgical planning
Abstract・原文の抄録
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
オリジナル紙だ
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