Towards streamlining orthopedic consultations: Machine learning classification of knee diagnosis groups via computer-assisted history taking

IF 1.6 4区医学 Q3 ORTHOPEDICS

Knee Pub Date : 2025-06-05 DOI:10.1016/j.knee.2025.05.017

Jacobien H.F. Oosterhoff , Twan Slaats , Tristan Warren , Walter van der Weegen

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引用次数: 0

Abstract

Background

The number of patients suffering from knee complaints is increasing, leading to increased orthopedic healthcare consumption. Predicting knee diagnoses prior to consultation may be valuable in optimizing the consultation workflow. Therefore, the purpose of this study was to develop and internally validate a machine learning (ML) algorithm for predicting a knee diagnosis group for patients aged 18 years and older, based on computer-assisted history taking.

Methods

A prospective cohort study at a single general district hospital was conducted to identify patients referred to an orthopedic surgeon for knee complaints. In total, 1172 patients were included, with an average age of 54 years (interquartile range 36–66), of which the majority were female (n = 594, 50.7%). The most frequent diagnosis group was knee osteoarthritis (n = 775, 66.1%), followed by ligamentous injuries (n = 208, 17.7%) and otherwise classified (n = 189, 16.1%). First, the dataset was randomly split 80:20 into training and test subsets. Then, a random forest algorithm was used to identify the variables predictive of a knee diagnosis group. Five different ML algorithms were developed, internally validated, and assessed by discrimination (area under the receiver operating characteristic curve, AUC), accuracy, precision (positive predictive value), recall (sensitivity), and F1‑score (the harmonic mean of precision and recall).

Results

The models included patient characteristics and computer-assisted history taking. The support vector machine algorithm had the best performance for knee diagnosis group prediction, with good discrimination (area under the receiver operating characteristic curve, AUC = 0.92), accuracy (0.84), precision (0.85), recall (0.84) and F1-score (0.82).

Conclusions

The developed ML algorithm shows promise in predicting a knee diagnosis group in patients presenting with knee complaints to an orthopedic practice. Integrating this algorithm could streamline the consultation workflow by directing patients predicted to have knee osteoarthritis to orthopedic surgeons specializing in knee osteoarthritis, and those predicted to have ligamentous injuries to orthopedic surgeons specializing in sports and traumatic injuries.

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简化骨科会诊：通过计算机辅助历史记录对膝关节诊断组进行机器学习分类

背景：膝关节主诉患者数量不断增加，导致骨科医疗保健消费增加。在会诊前预测膝关节诊断可能对优化会诊工作流程有价值。因此，本研究的目的是开发并内部验证一种机器学习（ML）算法，基于计算机辅助的病史记录，预测18岁及以上患者的膝关节诊断组。方法在一家综合性地区医院进行前瞻性队列研究，以确定因膝关节疾病转诊到骨科医生处的患者。共纳入1172例患者，平均年龄54岁（四分位数范围36 ~ 66），其中女性居多（n = 594, 50.7%）。最常见的诊断组为膝关节骨关节炎（775例，66.1%），其次为韧带损伤（208例，17.7%），其他分类（189例，16.1%）。首先，将数据集以80:20的比例随机分成训练子集和测试子集。然后，使用随机森林算法识别预测膝关节诊断组的变量。我们开发了五种不同的机器学习算法，进行了内部验证，并通过鉴别（受试者工作特征曲线下面积，AUC）、准确度、精密度（阳性预测值）、召回率（灵敏度）和F1得分（精度和召回率的调和平均值）进行了评估。结果模型包括患者特征和计算机辅助的病史记录。支持向量机算法在膝关节诊断组预测中表现最佳，具有良好的识别能力（受试者工作特征曲线下面积，AUC = 0.92）、准确率（0.84）、精密度（0.85）、召回率（0.84）和f1评分（0.82）。结论：所开发的机器学习算法在预测膝关节诊断组患者的膝关节投诉到骨科实践。整合该算法可以将预测患有膝关节骨关节炎的患者引导到专门治疗膝关节骨关节炎的骨科医生那里，将预测患有韧带损伤的患者引导到专门治疗运动和创伤性损伤的骨科医生那里，从而简化会诊流程。

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.