Segura Facundo Manuel, Segura Florencio Pablo, Zudaire María Paz Lucero, Segura Florencio Vicente
Segura, Centro Privado de Ortopedia y Traumatología, 358 Justo Jose de Urquiza Street, X5000, Córdoba City, Córdoba, Argentina.
Universidad Nacional de Córdoba, Haya de La Torre Av, X5000, Córdoba, Argentina.
Eur J Orthop Surg Traumatol. 2024 Dec 2;35(1):32. doi: 10.1007/s00590-024-04124-0.
Knee osteoarthritis is one of the most prevalent and debilitating musculoskeletal diseases, with a high incidence among the elderly population. Early detection and accurate classification can improve clinical outcomes for affected patients.
This study investigates the use of artificial intelligence (AI) and computer vision for automated detection and classification of knee osteoarthritis using the IKDC classification system. The aim was to develop an automated system for this purpose and evaluate its accuracy in classifying disease severity.
A public dataset containing radiographic knee images with varying degrees of osteoarthritis, previously classified according to the IKDC scale, was utilized. Images were processed using LandingLens software, an advanced computer vision platform facilitating AI model development and implementation. A machine learning model based on the ConvNext architecture-a convolutional neural network-was trained on 1901 images and evaluated using 380 test images.
The model demonstrated an overall accuracy of 95.16% in classifying knee osteoarthritis according to the IKDC scale, with a sensitivity of 95.11%. Class-specific accuracies were 92.40% for class A, 93.20% for class B, 98.45% for class C, and 95.69% for class D. These results highlight the model's capability to distinguish between different severity grades of osteoarthritis with high accuracy.
This study underscores the efficacy of AI and computer vision in automating knee osteoarthritis detection, providing a precise and reliable tool for physicians in disease diagnosis. Integrating these technologies into clinical practice has the potential to enhance efficiency and consistency in patient evaluation, potentially leading to improved clinical outcomes and more personalized medical care.
Level III.
膝关节骨关节炎是最常见且使人衰弱的肌肉骨骼疾病之一，在老年人群中发病率很高。早期检测和准确分类可改善受影响患者的临床结局。
本研究调查使用人工智能（AI）和计算机视觉，通过国际膝关节文献委员会（IKDC）分类系统对膝关节骨关节炎进行自动检测和分类。目的是为此开发一个自动化系统，并评估其在分类疾病严重程度方面的准确性。
使用一个公共数据集，其中包含根据IKDC量表预先分类的不同程度骨关节炎的膝关节X线图像。使用LandingLens软件对图像进行处理，这是一个先进的计算机视觉平台，有助于AI模型的开发和实施。基于ConvNext架构（一种卷积神经网络）的机器学习模型在1901张图像上进行训练，并使用380张测试图像进行评估。
该模型根据IKDC量表对膝关节骨关节炎进行分类的总体准确率为95.16%，灵敏度为95.11%。A类的类别特异性准确率为92.40%，B类为93.20%，C类为98.45%，D类为95.69%。这些结果突出了该模型以高精度区分不同严重程度骨关节炎等级的能力。
本研究强调了AI和计算机视觉在自动化膝关节骨关节炎检测中的有效性，为医生进行疾病诊断提供了一个精确且可靠的工具。将这些技术整合到临床实践中有可能提高患者评估的效率和一致性，潜在地改善临床结局并实现更个性化的医疗护理。
三级。
