Tiwari Anjali, Poduval Murali, Bagaria Vaibhav
Department ofOrthopedics, Sir H. N. Reliance Foundation Hospital and Research Centre, Mumbai 400004, India.
Lifesciences Engineering, Tata Consultancy Services, Mumbai 400096, India.
World J Orthop. 2022 Jun 18;13(6):603-614. doi: 10.5312/wjo.v13.i6.603.
Deep learning, a form of artificial intelligence, has shown promising results for interpreting radiographs. In order to develop this niche machine learning (ML) program of interpreting orthopedic radiographs with accuracy, a project named deep learning algorithm for orthopedic radiographs was conceived. In the first phase, the diagnosis of knee osteoarthritis (KOA) as per the standard Kellgren-Lawrence (KL) scale in medical images was conducted using the deep learning algorithm for orthopedic radiographs.
To compare efficacy and accuracy of eight different transfer learning deep learning models for detecting the grade of KOA from a radiograph and identify the most appropriate ML-based model for the detecting grade of KOA.
The study was performed on 2068 radiograph exams conducted at the Department of Orthopedic Surgery, Sir HN Reliance Hospital and Research Centre (Mumbai, India) during 2019-2021. Three orthopedic surgeons reviewed these independently, graded them for the severity of KOA as per the KL scale and settled disagreement through a consensus session. Eight models, namely ResNet50, VGG-16, InceptionV3, MobilnetV2, EfficientnetB7, DenseNet201, Xception and NasNetMobile, were used to evaluate the efficacy of ML in accurately classifying radiographs for KOA as per the KL scale. Out of the 2068 images, 70% were used initially to train the model, 10% were used subsequently to test the model, and 20% were used finally to determine the accuracy of and validate each model. The idea behind transfer learning for KOA grade image classification is that if the existing models are already trained on a large and general dataset, these models will effectively serve as generic models to fulfill the study's objectives. Finally, in order to benchmark the efficacy, the results of the models were also compared to a first-year orthopedic trainee who independently classified these models according to the KL scale.
Our network yielded an overall high accuracy for detecting KOA, ranging from 54% to 93%. The most successful of these was the DenseNet model, with accuracy up to 93%; interestingly, it even outperformed the human first-year trainee who had an accuracy of 74%.
The study paves the way for extrapolating the learning using ML to develop an automated KOA classification tool and enable healthcare professionals with better decision-making.
深度学习作为人工智能的一种形式，在解读X光片方面已显示出有前景的结果。为了准确开发这个用于解读骨科X光片的小众机器学习（ML）程序，构思了一个名为骨科X光片深度学习算法的项目。在第一阶段，使用骨科X光片深度学习算法对医学图像中按照标准凯尔格伦 - 劳伦斯（KL）分级的膝关节骨关节炎（KOA）进行诊断。
比较八种不同的迁移学习深度学习模型从X光片中检测KOA分级的疗效和准确性，并确定用于检测KOA分级的最合适的基于ML的模型。
该研究对2019年至2021年期间在印度孟买HN Reliance医院和研究中心骨科进行的2068次X光检查进行。三位骨科医生独立审查这些检查，根据KL分级对KOA的严重程度进行分级，并通过共识会议解决分歧。使用八个模型，即ResNet50、VGG - 16、InceptionV3、MobilnetV2、EfficientnetB7、DenseNet201、Xception和NasNetMobile，来评估ML根据KL分级准确分类KOAX光片的疗效。在2068张图像中，最初70%用于训练模型，随后10%用于测试模型，最后20%用于确定每个模型的准确性并进行验证。KOA分级图像分类的迁移学习背后的理念是，如果现有模型已经在一个大型通用数据集上进行了训练，这些模型将有效地作为通用模型来实现研究目标。最后，为了对疗效进行基准测试，还将模型的结果与一名根据KL分级独立对这些模型进行分类的一年级骨科实习生的结果进行了比较。
我们的网络在检测KOA方面总体准确率较高，范围从54%到93%。其中最成功的是DenseNet模型，准确率高达93%；有趣的是，它甚至超过了准确率为74%的一年级人类实习生。
该研究为利用ML进行学习以开发自动化KOA分类工具并使医疗保健专业人员能够做出更好的决策铺平了道路。
