Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK S7N 5C9, Canada.
Department of Radiology, Chonbuk National University Hospital, 20 Geonji-ro, Geumam 2(i)-dong, Deokjin-gu, Jeonju, Jeollabuk-do 54907, South Korea.
Comput Med Imaging Graph. 2020 Jun;82:101718. doi: 10.1016/j.compmedimag.2020.101718. Epub 2020 Apr 26.
Ankylosing spondylitis (AS) is an arthritis with symptoms visible in medical imagery. This paper proposes, to the authors' best knowledge, the first use of statistical machine learning- and deep learning-based classifiers to detect erosion, an early AS symptom, via analysis of computed tomography (CT) imagery, giving some consideration to patient age in so doing. We used gray-level co-occurrence matrices and local binary patterns to generate input features to machine learning algorithms, specifically k-nearest neighbors (k-NN) and random forest. Deep learning solutions based on a modified InceptionV3 architecture were designed and tested, with one classifier produced by training with a cross-entropy loss function and another produced by additionally seeking to minimize validation loss. We found that the random forest classifiers outperform the k-NN classifiers and achieve an eightfold cross-validation average accuracy, recall, and area under receiver operator characteristic curve (ROC AUC) of 96.0%, 92.9%, and 0.97, respectively, for erosion vs. young control patients, and 82.4%, 80.6%, and 0.91, respectively, for erosion vs. old control patients. We found that the deep learning classifier trained without minimizing validation loss was best and achieves an eightfold cross-validation accuracy, recall, and ROC AUC of 99.0%, 97.5%, and 0.97, respectively, for erosion vs. all (combined young and old) control patients; this classifier outperforms a musculoskeletal radiologist with 9 years of experience in raw sensitivity and specificity by 8.4% and 9.5%, respectively. Despite the relatively small dataset on which we trained and cross-validated, our results indicate the potential of machine and deep learning to aid AS diagnosis, and further research using larger datasets should be conducted.
强直性脊柱炎（AS）是一种在医学影像中可见症状的关节炎。本文提出，据作者所知，这是首次使用基于统计机器学习和深度学习的分类器，通过分析计算机断层扫描（CT）图像来检测侵蚀，这是 AS 的早期症状，并在这一过程中考虑到患者的年龄。我们使用灰度共生矩阵和局部二值模式来生成机器学习算法的输入特征，特别是 K-近邻（k-NN）和随机森林。设计并测试了基于改进的 InceptionV3 架构的深度学习解决方案，其中一个分类器通过使用交叉熵损失函数进行训练，另一个分类器通过额外寻求最小化验证损失进行训练。我们发现随机森林分类器优于 K-近邻分类器，对于侵蚀与年轻对照组患者的 8 倍交叉验证平均准确率、召回率和接收者操作特征曲线（ROC AUC）分别为 96.0%、92.9%和 0.97，对于侵蚀与老年对照组患者的 8 倍交叉验证平均准确率、召回率和 ROC AUC 分别为 82.4%、80.6%和 0.91。我们发现，未最小化验证损失的深度分类器的训练效果最佳，对于侵蚀与所有（年轻和老年合并）对照组患者的 8 倍交叉验证准确率、召回率和 ROC AUC 分别为 99.0%、97.5%和 0.97；该分类器的 raw 灵敏度和特异性分别比具有 9 年经验的肌肉骨骼放射科医生高出 8.4%和 9.5%。尽管我们在训练和交叉验证中使用的数据集相对较小，但我们的结果表明机器学习和深度学习有潜力辅助 AS 诊断，应进一步使用更大的数据集进行研究。
