An AI-based system for fully automated knee alignment assessment in standard AP knee radiographs

IF 1.6 4区医学 Q3 ORTHOPEDICS

Knee Pub Date : 2025-03-03 DOI:10.1016/j.knee.2025.02.013

Dominic Cullen , Peter Thompson , David Johnson , Claudia Lindner

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引用次数: 0

Abstract

Background

Accurate assessment of knee alignment in pre- and post-operative radiographs is crucial for knee arthroplasty planning and evaluation. Current methods rely on manual alignment assessment, which is time-consuming and error-prone. This study proposes a machine learning-based approach to fully automatically measure anatomical varus/valgus alignment in standard anteroposterior (AP) knee radiographs.

Methods

We collected a training dataset of 566 pre-operative and 457 one-year post-operative AP knee radiographs from total knee arthroplasty patients, along with a separate test set of 376 patients. The distal femur and proximal tibia/fibula were manually outlined using points to capture the knee joint. The outlines were used to develop an automatic system to locate the points. The anatomical femorotibial angle was calculated using the points, with varus/valgus defined as negative/positive deviations from zero. Fifty test images were clinically measured on two occasions by an orthopaedic surgeon. Agreement between points-based manual, automatic, and clinical measurements was assessed using intra-class correlation coefficient (ICC), mean absolute difference (MAD) and Bland-Altman analysis.

Results

The agreement between automatic and manual measurements was excellent pre-/post-operatively with ICC 0.98/0.96 and MAD 0.8°/0.7°. The agreement between automatic and clinical measurements was excellent pre-operatively (ICC: 0.97; MAD: 1.2°) but lacked performance post-operatively (ICC: 0.78; MAD: 1.5°). The clinical intra-observer agreement was excellent pre-/post-operatively with ICC 0.99/0.95 and MAD 0.9°/0.8°.

Conclusion

The developed system demonstrates high reliability in automatically measuring varus/valgus alignment pre- and post-operatively, and shows excellent agreement with clinical measurements pre-operatively. It provides a promising approach for automating the measurement of anatomical alignment.

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一种基于人工智能的系统，用于标准AP膝关节x线片的全自动膝关节对齐评估

背景在术前和术后的x线片中准确评估膝关节对齐对膝关节置换术的计划和评估至关重要。当前的方法依赖于手动校准评估，这是耗时且容易出错的。本研究提出了一种基于机器学习的方法来全自动测量标准膝关节前后位（AP） x线片上的解剖内翻/外翻对齐。方法：我们收集了来自全膝关节置换术患者的566张术前和457张术后1年AP膝关节x线片的训练数据集，以及376名患者的单独测试数据集。用点捕获膝关节，手动勾勒股骨远端和胫骨/腓骨近端。这些轮廓被用来开发一个自动定位点的系统。利用这些点计算股骨胫骨解剖角度，内翻/外翻定义为与零的负/正偏差。50个测试图像是由骨科医生在两次临床测量。使用类内相关系数（ICC）、平均绝对差（MAD）和Bland-Altman分析评估基于点的手动、自动和临床测量之间的一致性。结果术前/术后自动测量与人工测量吻合良好，ICC为0.98/0.96，MAD为0.8°/0.7°。术前自动测量值与临床测量值吻合良好(ICC: 0.97；MAD: 1.2°)，但术后表现不佳(ICC: 0.78；疯狂:1.5°)。临床观察内一致性非常好，术前/术后ICC为0.99/0.95，MAD为0.9°/0.8°。结论该系统在术前、术后自动测量内翻/外翻对准度方面具有较高的可靠性，与临床术前测量结果吻合良好。它为解剖对准的自动化测量提供了一种很有前途的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Knee 医学-外科

CiteScore

3.80

自引率

5.30%

发文量

171

审稿时长

6 months

期刊介绍： The Knee is an international journal publishing studies on the clinical treatment and fundamental biomechanical characteristics of this joint. The aim of the journal is to provide a vehicle relevant to surgeons, biomedical engineers, imaging specialists, materials scientists, rehabilitation personnel and all those with an interest in the knee. The topics covered include, but are not limited to: • Anatomy, physiology, morphology and biochemistry; • Biomechanical studies; • Advances in the development of prosthetic, orthotic and augmentation devices; • Imaging and diagnostic techniques; • Pathology; • Trauma; • Surgery; • Rehabilitation.