School of Electrical Engineering and Computing, University of Newcastle, Callaghan, NSW 2308, Australia.
Hunter Medical Research Institute, New Lambton Heights, NSW 2305, Australia.
Comput Math Methods Med. 2022 Jul 15;2022:5154896. doi: 10.1155/2022/5154896. eCollection 2022.
Multiple sclerosis (MS) is a chronic neurological disease of the central nervous system (CNS). Early diagnosis of MS is highly desirable as treatments are more effective in preventing MS-related disability when given in the early stages of the disease. The main aim of this research is to predict the occurrence of a second MS-related clinical event, which indicates the conversion of clinically isolated syndrome (CIS) to clinically definite MS (CDMS). In this study, we apply a branch of artificial intelligence known as deep learning and develop a fully automated algorithm primed with convolutional neural network (CNN) that has the ability to learn from MRI scan features. The basic architecture of our algorithm is that of the VGG16 CNN model, but amended such that it can handle MRI DICOM images. A dataset comprised of scans acquired using two different scanners was used for the purposes of verification of the algorithm. A group of 49 patients had volumetric MRI scans taken at onset of the disease and then again one year later using one of the two scanners. In total, this yielded 7360 images which were then used for training, validation, and testing of the algorithm. Initially, these raw images were taken through 4 steps of preprocessing. In order to boost the efficiency of the process, we pretrained our algorithm using the publicly available ADNI dataset used to classify Alzheimer's disease. Finally, we used our preprocessed dataset to train and test the algorithm. Clinical evaluation conducted a year after the first time point revealed that 26 of the 49 patients had converted to CDMS, while the remaining 23 had not. Results of testing showed that our algorithm was able to predict the clinical results with an accuracy of 88.8% and with an area under the curve (AUC) of 91%. A highly accurate algorithm was developed using CNN approach to reliably predict conversion of patients with CIS to CDMS using MRI data from two different scanners.
多发性硬化症 (MS) 是一种中枢神经系统 (CNS) 的慢性神经系统疾病。早期诊断 MS 非常重要，因为在疾病的早期阶段给予治疗可以更有效地预防与 MS 相关的残疾。本研究的主要目的是预测第二个与 MS 相关的临床事件的发生，这表明临床孤立综合征 (CIS) 向临床明确的 MS (CDMS) 的转变。在这项研究中，我们应用了一种称为深度学习的人工智能分支，并开发了一种完全自动化的算法，该算法由卷积神经网络 (CNN) 提供支持，具有从 MRI 扫描特征中学习的能力。我们算法的基本架构是 VGG16 CNN 模型，但进行了修改，使其能够处理 MRI DICOM 图像。该算法的验证使用了由使用两种不同扫描仪采集的扫描组成的数据集。一组 49 名患者在疾病发作时进行了容积 MRI 扫描，然后使用其中一种扫描仪在一年后再次进行扫描。总共获得了 7360 张图像，然后用于算法的训练、验证和测试。最初，这些原始图像经过 4 个预处理步骤。为了提高处理效率，我们使用了可用于分类阿尔茨海默病的公开可用的 ADNI 数据集对算法进行了预训练。最后，我们使用预处理后的数据集对算法进行了训练和测试。在第一次时间点一年后进行的临床评估显示，49 名患者中有 26 名转化为 CDMS，而其余 23 名患者没有。测试结果表明，我们的算法能够以 88.8%的准确率和 91%的曲线下面积 (AUC) 预测临床结果。使用 CNN 方法开发了一种高度准确的算法，能够使用来自两种不同扫描仪的 MRI 数据可靠地预测 CIS 患者向 CDMS 的转化。
