Choi Bo Kyu, Choi Yoonhyeok, Jang Sooyoung, Ha Woo-Seok, Cho Soomi, Chang Kimoon, Sohn Beomseok, Kim Kyung Min, Park Yu Rang
Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea.
Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Republic of Korea.
Brain Commun. 2025 May 9;7(3):fcaf179. doi: 10.1093/braincomms/fcaf179. eCollection 2025.
Inflammatory diseases of the CNS impose a substantial disease burden, necessitating prompt and appropriate prognosis prediction. We developed a multimodal deep learning model integrating clinical features and brain MRI data to enhance early prognosis prediction of CNS inflammation. This retrospective study used thin-cut T1-weighted brain MRI scans and the clinical variables of patients with CNS inflammation who were admitted to a tertiary referral hospital between January 2010 and December 2023. Data collected after January 2022 served as the external test set. 3D MRI images were first segmented into 43 brain regions using the FastSurfer library. The segmented images were then processed through a 3D convolutional neural network model for feature extraction and vectorization, after which they were integrated with clinical features for prediction. The performance of each artificial intelligence model was assessed using accuracy, F1 score, area under the receiver operating characteristic curve and area under the precision-recall curve. The internal dataset comprised 413 images from 291 patients (mean age, 45.5 years ± 19.3 [SD]; 151 male patients; 54 with poor prognosis). The external dataset comprised 210 images from 106 patients (mean age, 45.5 years ± 18.9 [SD]; 59 male patients; 31 with poor prognosis). The multimodal deep learning model outperformed unimodal models across all aetiological groups, achieving area under the receiver operating characteristic curve values of 0.8048 for autoimmune, 0.9107 for bacterial, 1.0000 for tuberculosis and 0.9242 for viral infections. Furthermore, artificial intelligence assistance improved clinicians' prognostic accuracy, as demonstrated in comparisons with neurologists, paediatricians and radiologists. Our findings demonstrate that the multimodal deep learning model enhances artificial intelligence-assisted prognosis prediction in CNS inflammation, improving both model performance and clinician decision-making.
中枢神经系统（CNS）的炎症性疾病带来了沉重的疾病负担，因此需要及时且准确地进行预后预测。我们开发了一种整合临床特征和脑部MRI数据的多模态深度学习模型，以加强对CNS炎症的早期预后预测。这项回顾性研究使用了薄层T1加权脑部MRI扫描以及2010年1月至2023年12月期间入住一家三级转诊医院的CNS炎症患者的临床变量。2022年1月之后收集的数据用作外部测试集。首先使用FastSurfer库将3D MRI图像分割为43个脑区。然后将分割后的图像通过一个3D卷积神经网络模型进行特征提取和向量化，之后将其与临床特征整合以进行预测。使用准确率、F1分数、受试者工作特征曲线下面积和精确召回率曲线下面积来评估每个人工智能模型的性能。内部数据集包括来自291名患者的413张图像（平均年龄，45.5岁±19.3[标准差]；151名男性患者；54名预后不良）。外部数据集包括来自106名患者的210张图像（平均年龄，45.5岁±18.9[标准差]；59名男性患者；31名预后不良）。在所有病因组中，多模态深度学习模型的表现均优于单模态模型，自身免疫性疾病的受试者工作特征曲线下面积值为0.8048，细菌性疾病为0.9107，结核病为1.0000，病毒感染为0.9242。此外，与神经科医生、儿科医生和放射科医生的比较表明，人工智能辅助提高了临床医生的预后准确性。我们的研究结果表明，多模态深度学习模型增强了人工智能辅助的CNS炎症预后预测，改善了模型性能和临床医生的决策。
