基于膝关节冠状面对齐的机器学习算法预测开楔高位胫骨截骨术后序列对齐变化。

IF 1.6 4区 医学 Q3 ORTHOPEDICS
Journal of Knee Surgery Pub Date : 2025-07-01 Epub Date: 2025-01-27 DOI:10.1055/a-2525-4622
Joon Hee Cho, Hee Seung Nam, Seong Yun Park, Jade Pei Yuik Ho, Yong Seuk Lee
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引用次数: 0

摘要

将排列分类为表型可用于预测和分析开放楔形高位胫骨截骨(OWHTO)术后排列变化。本研究的目的是:(1)建立预测OWHTO后最终对齐的膝关节冠状面对齐(CPAK)表型的机器学习模型;(2)分析最终对齐表型的预测因素。回顾性收集了2014年3月至2019年12月期间接受OWHTO的163个膝关节的数据。每个数据在三个时间点进行评估:术前、术后3个月和最终随访。宪法一致性也进行了评估。机器学习模型使用两个独立的特征集,包括一系列放射学参数和CPAK表型。曲线下面积(AUC)作为确定最佳模型的主要指标。为了评价特征的重要性,还对最佳模型进行Shapley加性解释(SHAP)分析。多层感知器(MLP)是最佳的预测模型,基于放射学参数的AUC最高,为0.867,基于CPAK表型的AUC最高,为0.783。术后3个月关节线倾角(JLO)是预测最终CPAK表型的最重要放射学参数。体格和术前矫正的特征也有影响,尽管术后3个月的矫正特征是最重要的预测因素。综上所述,所建立的MLP机器学习模型在预测OWHTO后CPAK最终表型方面表现出色。术后JLO是预测最终对准最重要的放射学参数。结合体质、术前和术后时期的特点,在预测最终对齐方面具有很高的准确性和性能。证据水平:回顾性队列研究;关键词:膝关节,胫骨高位截骨,CPAK分类,机器学习,预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of the Serial Alignment Change after Opening-Wedge High Tibial Osteotomy Based on Coronal Plane Alignment of the Knee Using Machine Learning Algorithm.

Categorization of alignment into phenotypes can be useful for predicting and analyzing postoperative alignment changes after opening-wedge high tibial osteotomy (OWHTO). The purposes of this study were to (1) develop a machine learning model for the predicting the Coronal Plane Alignment of the Knee (CPAK) phenotypes of final alignment after OWHTO, and (2) analyze predictive factors for final alignment phenotypes. Data were retrospectively collected from 163 knees that underwent OWHTO between March 2014 and December 2019. Each data were assessed at three time points: preoperatively, at 3 months postoperatively, and the final follow-up. Constitutional alignment was also evaluated. Machine learning models were developed using two independent feature sets consisting of serial radiologic parameters and CPAK phenotypes. The area under the receiver-operating characteristic curve (AUC) was used as a primary metric to determine the best model. To evaluate the feature importance, Shapley additive explanation (SHAP) analysis was also performed on the best model. Multilayer perceptron (MLP) was the best prediction model, with the highest AUC of 0.867 based on radiologic parameters and 0.783 based on CPAK phenotypes. Joint line obliquity (JLO) at 3 months postoperatively was the most important factor among the radiologic parameters for predicting the final CPAK phenotypes. The features of constitutional and preoperative alignments also contributed, although the features of alignments at 3 months postoperatively were the highest contributing predictors. In conclusion, the developed machine learning models of the MLP showed excellent performance in predicting the final CPAK phenotypes after OWHTO. Postoperative JLO was the most important radiologic parameter for predicting the final alignment. The combination of features of the constitutional, preoperative, and postoperative periods enabled high accuracy and performance in predicting the final alignment.A retrospective cohort study with the level of evidence as level III.

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来源期刊
CiteScore
4.50
自引率
5.90%
发文量
139
期刊介绍: The Journal of Knee Surgery covers a range of issues relating to the orthopaedic techniques of arthroscopy, arthroplasty, and reconstructive surgery of the knee joint. In addition to original peer-review articles, this periodical provides details on emerging surgical techniques, as well as reviews and special focus sections. Topics of interest include cruciate ligament repair and reconstruction, bone grafting, cartilage regeneration, and magnetic resonance imaging.
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