2024 · Knee Surgery & Related Research
装置や撮影方法が異なる病院でも、下肢アライメントを同じ基準で測定できますか？
専門医の測定とのinter-observer ICCは0.936–0.997、同じ画像の反復測定ICCは1.000でした。
原論文Figure 2・3施設のX線
図を拡大する外部検証に用いた3施設（A・B・C）の撮影画像です。濃淡や撮影範囲の違いによる基準点認識性能を評価しました。
Sung Eun Kim, Jun Woo Nam, Joong Il Kim, Jong-Keun Kim, Du Hyun Ro. Knee Surgery & Related Research (2024). DOI: 10.1186/s43019-023-00209-y
研究で問うこと
対象と方法
主な結果
診療・手術で考える点
解釈の限界
簡単に説明しましょう
どの診療上の問いにつながりますか？
CONNEVO Metricの測定技術につながる外部検証です。迅速な反復測定とともに、原画像で基準点を確認し、撮影姿勢や関節内変形を評価することが大切です。
Keywords・キーワード
- Accuracy
- Alignment measurement
- Deep learning
- Full-leg radiographs
- Imaging protocol
- AI
- Metric
- alignment
Abstract・原文の抄録
Sung Eun Kim, Jun Woo Nam, Joong Il Kim, Jong-Keun Kim, Du Hyun Ro. Enhanced deep learning model enables accurate alignment measurement across diverse institutional imaging protocols. Knee surgery & related research. 2024;36(1):4. doi:10.1186/s43019-023-00209-y.
BACKGROUND
Achieving consistent accuracy in radiographic measurements across different equipment and protocols is challenging. This study evaluates an advanced deep learning (DL) model, building upon a precursor, for its proficiency in generating uniform and precise alignment measurements in full-leg radiographs irrespective of institutional imaging differences.
METHODS
The enhanced DL model was trained on over 10,000 radiographs. Utilizing a segmented approach, it separately identified and evaluated regions of interest (ROIs) for the hip, knee, and ankle, subsequently integrating these regions. For external validation, 300 datasets from three distinct institutes with varied imaging protocols and equipment were employed. The study measured seven radiologic parameters: hip-knee-ankle angle, lateral distal femoral angle, medial proximal tibial angle, joint line convergence angle, weight-bearing line ratio, joint line obliquity angle, and lateral distal tibial angle. Measurements by the model were compared with an orthopedic specialist's evaluations using inter-observer and intra-observer intraclass correlation coefficients (ICCs). Additionally, the absolute error percentage in alignment measurements was assessed, and the processing duration for radiograph evaluation was recorded.
RESULTS
The DL model exhibited excellent performance, achieving an inter-observer ICC between 0.936 and 0.997, on par with an orthopedic specialist, and an intra-observer ICC of 1.000. The model's consistency was robust across different institutional imaging protocols. Its accuracy was particularly notable in measuring the hip-knee-ankle angle, with no instances of absolute error exceeding 1.5 degrees. The enhanced model significantly improved processing speed, reducing the time by 30-fold from an initial 10-11 s to 300 ms.
CONCLUSIONS
The enhanced DL model demonstrated its ability for accurate, rapid alignment measurements in full-leg radiographs, regardless of protocol variations, signifying its potential for broad clinical and research applicability.
© The Author(s) 2024
オリジナル紙だ
Enhanced deep learning model enables accurate alignment measurement across diverse institutional imaging protocols
Sung Eun Kim, Jun Woo Nam, Joong Il Kim, Jong-Keun Kim, Du Hyun Ro
Knee Surgery & Related Research · 2024