在有膝内翻或膝外翻畸形的患者行膝关节矫正截骨术后的长腿正位 X 线片中，对膝关节对线进行全自动评估。
Fully automated assessment of the knee alignment on long leg radiographs following corrective knee osteotomies in patients with valgus or varus deformities.
机构信息
Michael Ogon Laboratory for Orthopaedic Research, Orthopaedic Hospital Vienna Speising, Speisinger Straße 109, 1130, Vienna, Austria.
Center for Anatomy and Cell Biology, Medical University Vienna Speising, Währinger Straße 13, 1090, Vienna, Austria.
出版信息
Arch Orthop Trauma Surg. 2024 Mar;144(3):1029-1038. doi: 10.1007/s00402-023-05151-y. Epub 2023 Dec 13.
INTRODUCTION
The assessment of the knee alignment on long leg radiographs (LLR) postoperative to corrective knee osteotomies (CKOs) is highly dependent on the reader's expertise. Artificial Intelligence (AI) algorithms may help automate and standardise this process. The study aimed to analyse the reliability of an AI-algorithm for the evaluation of LLRs following CKOs.
MATERIALS AND METHODS
In this study, we analysed a validation cohort of 110 postoperative LLRs from 102 patients. All patients underwent CKO, including distal femoral (DFO), high tibial (HTO) and bilevel osteotomies. The agreement between manual measurements and the AI-algorithm was assessed for the mechanical axis deviation (MAD), hip knee ankle angle (HKA), anatomical-mechanical-axis-angle (AMA), joint line convergence angle (JLCA), mechanical lateral proximal femur angle (mLPFA), mechanical lateral distal femoral angle (mLDFA), mechanical medial proximal tibia angle (mMPTA) and mechanical lateral distal tibia angle (mLDTA), using the intra-class-correlation (ICC) coefficient between the readers, each reader and the AI and the mean of the manual reads and the AI-algorithm and Bland-Altman Plots between the manual reads and the AI software for the MAD, HKA, mLDFA and mMPTA.
RESULTS
In the validation cohort, the AI software showed excellent agreement with the manual reads (ICC: 0.81-0.99). The agreement between the readers (Inter-rater) showed excellent correlations (ICC: 0.95-0. The mean difference in the DFO group for the MAD, HKA, mLDFA and mMPTA were 0.50 mm, - 0.12°, 0.55° and 0.15°. In the HTO group the mean difference for the MAD, HKA, mLDFA and mMPTA were 0.36 mm, - 0.17°, 0.57° and 0.08°, respectively. Reliable outputs were generated in 95.4% of the validation cohort.
CONCLUSION
he application of AI-algorithms for the assessment of lower limb alignment on LLRs following CKOs shows reliable and accurate results.
LEVEL OF EVIDENCE
Diagnostic Level III.
引言
膝关节矫正截骨术后的下肢全长正位片（LLR）的膝关节对线评估高度依赖于阅片者的专业知识。人工智能（AI）算法可能有助于实现这一过程的自动化和标准化。本研究旨在分析一种用于评估膝关节矫正截骨术后 LLR 的 AI 算法的可靠性。
材料与方法
在这项研究中，我们分析了 102 名患者的 110 例术后 LLR 的验证队列。所有患者均接受了膝关节矫正截骨术，包括股骨远端（DFO）、胫骨高位（HTO）和双平面截骨术。通过使用读者之间、每位读者与 AI 之间的组内相关系数（ICC），以及手动测量值和 AI 算法的平均值和 Bland-Altman 图，评估手动测量值与 AI 算法之间的机械轴偏差（MAD）、髋膝踝角（HKA）、解剖机械轴角（AMA）、关节线会聚角（JLCA）、机械外侧股骨近端角（mLPFA）、机械外侧股骨远端角（mLDFA）、机械内侧胫骨近端角（mMPTA）和机械外侧胫骨远端角（mLDTA）的一致性。
结果
在验证队列中，AI 软件与手动测量值显示出极好的一致性（ICC：0.81-0.99）。读者之间的一致性（组内）显示出极好的相关性（ICC：0.95-0.99）。在 DFO 组中，MAD、HKA、mLDFA 和 mMPTA 的平均差异为 0.50mm、-0.12°、0.55°和 0.15°。在 HTO 组中，MAD、HKA、mLDFA 和 mMPTA 的平均差异分别为 0.36mm、-0.17°、0.57°和 0.08°。在验证队列中，95.4%的病例生成了可靠的输出。
结论
AI 算法在评估膝关节矫正截骨术后 LLR 下肢对线中的应用具有可靠和准确的结果。
证据水平
诊断 III 级。
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