Lee Do Weon, Song Dae Seok, Han Hyuk-Soo, Ro Du Hyun
Department of Orthopedic Surgery, Seoul National University College of Medicine, Seoul, South Korea.
Department of Orthopedic Surgery, Dongguk University Ilsan Hospital, Goyang, South Korea.
Knee Surg Relat Res. 2024 Aug 13;36(1):24. doi: 10.1186/s43019-024-00228-3.
Fine-grained classification deals with data with a large degree of similarity, such as cat or bird species, and similarly, knee osteoarthritis severity classification [Kellgren-Lawrence (KL) grading] is one such fine-grained classification task. Recently, a plug-in module (PIM) that can be integrated into convolutional neural-network-based or transformer-based networks has been shown to provide strong discriminative regions for fine-grained classification, with results that outperformed the previous deep learning models. PIM utilizes each pixel of an image as an independent feature and can subsequently better classify images with minor differences. It was hypothesized that, as a fine-grained classification task, knee osteoarthritis severity may be classified well using PIMs. The aim of the study was to develop this automated knee osteoarthritis classification model.
A deep learning model that classifies knee osteoarthritis severity of a radiograph was developed utilizing PIMs. A retrospective analysis on prospectively collected data was performed. The model was trained and developed using the Osteoarthritis Initiative dataset and was subsequently tested on an independent dataset, the Multicenter Osteoarthritis Study (test set size: 17,040). The final deep learning model was designed through an ensemble of four different PIMs.
The accuracy of the model was 84%, 43%, 70%, 81%, and 96% for KL grade 0, 1, 2, 3, and 4, respectively, with an overall accuracy of 75.7%.
The ensemble of PIMs could classify knee osteoarthritis severity using simple radiographs with a fine accuracy. Although improvements will be needed in the future, the model has been proven to have the potential to be clinically useful.
细粒度分类处理具有高度相似性的数据，如猫或鸟类物种，同样，膝关节骨关节炎严重程度分类[凯尔格伦-劳伦斯（KL）分级]就是这样一项细粒度分类任务。最近，一种可集成到基于卷积神经网络或基于Transformer的网络中的插件模块（PIM）已被证明能为细粒度分类提供强大的判别区域，其结果优于先前的深度学习模型。PIM将图像的每个像素作为独立特征，随后能更好地对差异微小的图像进行分类。据推测，作为一项细粒度分类任务，使用PIM可能会很好地对膝关节骨关节炎严重程度进行分类。本研究的目的是开发这种自动化的膝关节骨关节炎分类模型。
利用PIM开发了一种对X线片膝关节骨关节炎严重程度进行分类的深度学习模型。对前瞻性收集的数据进行回顾性分析。该模型使用骨关节炎倡议数据集进行训练和开发，随后在一个独立数据集——多中心骨关节炎研究（测试集大小：17,040）上进行测试。最终的深度学习模型通过四个不同的PIM集成设计而成。
该模型对于KL分级0、1、2、3和4的准确率分别为84%、43%、70%、81%和96%，总体准确率为75.7%。
PIM的集成能够使用简单的X线片对膝关节骨关节炎严重程度进行高精度分类。尽管未来还需要改进，但该模型已被证明具有临床应用的潜力。
