Lau Lawrence Chun Man, Chui Elvis Chun Sing, Man Gene Chi Wai, Xin Ye, Ho Kevin Ki Wai, Mak Kyle Ka Kwan, Ong Michael Tim Yun, Law Sheung Wai, Cheung Wing Hoi, Yung Patrick Shu Hang
Department of Orthopaedics and Traumatology, Faculty of Medicine, The Chinese University of Hong Kong, The Prince of Wales Hospital, Shatin, Hong Kong.
J Orthop Translat. 2022 Oct 6;36:177-183. doi: 10.1016/j.jot.2022.07.004. eCollection 2022 Sep.
Loosening is the leading cause of total knee arthroplasty (TKA) revision. This is a heavy burden toward the healthcare system owing to the difficulty in diagnosis and complications occurring from the delay management. Based on automatic analytical model building, machine learning, may potentially help to automatically recognize the risk of loosening based on radiographs alone. The aim of this study was to build an image-based machine-learning model for detecting TKA loosening.
Image-based machine-learning model was developed based on ImageNet, Xception model and a TKA patient X-ray image dataset. Based on a dataset with TKA patient clinical parameters, another system was then created for developing the clinical-information-based machine learning model with random forest classifier. In addition, the Xception Model was pre-trained on the ImageNet database with python and TensorFlow deep learning library for the prediction of loosening. Class activation maps were also used to interpret the prediction decision made by model. Two senior orthopaedic specialists were invited to assess loosening from X-ray images for 3 attempts in setting up comparison benchmark.
In the image-based machine learning loosening model, the precision rate and recall rate were 0.92 and 0.96, respectively. While for the accuracy rate, 96.3% for visualization classification was observed. However, the addition of clinical-information-based model, with precision rate of 0.71 and recall rate of 0.20, did not further showed improvement on the accuracy. Moreover, as class activation maps showed corresponding signals over bone-implant interface that is loosened radiographically, this confirms that the current model utilized a similar image recognition pattern as that of inspection by clinical specialists.
The image-based machine learning model developed demonstrated high accuracy and predictability of knee arthroplasty loosening. And the class activation heatmap matched well with the radiographic features used clinically to detect loosening, which highlighting its potential role in assisting clinicians in their daily practice. However, addition of clinical-information-based machine-learning model did not offer further improvement in detection. As far as we know, this is the first report of pure image-based machine learning model with high detection accuracy. Importantly, this is also the first model to show relevant class activation heatmap corresponding to loosening location.
The finding in this study indicated image-based machine learning model can detect knee arthroplasty loosening with high accuracy and predictability, which the class activation heatmap can potentially assist surgeons to identify the sites of loosening.
松动是全膝关节置换术（TKA）翻修的主要原因。由于诊断困难以及延迟处理引发的并发症，这给医疗系统带来了沉重负担。基于自动分析模型构建，机器学习有可能仅根据X线片自动识别松动风险。本研究的目的是建立一个基于图像的机器学习模型来检测TKA松动。
基于ImageNet、Xception模型和一个TKA患者X线图像数据集开发基于图像的机器学习模型。然后，基于一个包含TKA患者临床参数的数据集，创建另一个系统来开发带有随机森林分类器的基于临床信息的机器学习模型。此外，使用Python和TensorFlow深度学习库在ImageNet数据库上对Xception模型进行预训练以预测松动情况。还使用类激活图来解释模型做出的预测决策。邀请两位资深骨科专家对X线图像进行3次评估以确定松动情况，从而建立比较基准。
在基于图像的机器学习松动模型中，精确率和召回率分别为0.92和0.96。可视化分类的准确率为96.3%。然而，添加基于临床信息的模型后，精确率为0.71，召回率为0.20，并未进一步提高准确率。此外，由于类激活图在影像学上显示松动的骨 - 植入物界面处有相应信号，这证实当前模型利用的图像识别模式与临床专家检查时相似。
所开发的基于图像的机器学习模型在膝关节置换术松动检测方面显示出高准确性和可预测性。并且类激活热图与临床上用于检测松动的影像学特征匹配良好，突出了其在协助临床医生日常实践中的潜在作用。然而，添加基于临床信息的机器学习模型并未在检测方面带来进一步改善。据我们所知，这是首次报道具有高检测准确性的纯基于图像的机器学习模型。重要的是，这也是第一个显示与松动位置相对应的相关类激活热图的模型。
本研究结果表明，基于图像的机器学习模型能够以高准确性和可预测性检测膝关节置换术松动，其中类激活热图有可能帮助外科医生识别松动部位。
