Qu Ruowei, Ji Xuan, Wang Shifeng, Wang Zhaonan, Wang Le, Yang Xinsheng, Yin Shaoya, Gu Junhua, Wang Alan, Xu Guizhi
State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, China.
Tianjin Universal Medical Imaging Diagnostic Center, Tianjin 300110, China.
Bioengineering (Basel). 2023 Oct 21;10(10):1234. doi: 10.3390/bioengineering10101234.
Epilepsy is a chronic brain disease with recurrent seizures. Mesial temporal lobe epilepsy (MTLE) is the most common pathological cause of epilepsy. With the development of computer-aided diagnosis technology, there are many auxiliary diagnostic approaches based on deep learning algorithms. However, the causes of epilepsy are complex, and distinguishing different types of epilepsy accurately is challenging with a single mode of examination. In this study, our aim is to assess the combination of multi-modal epilepsy medical information from structural MRI, PET image, typical clinical symptoms and personal demographic and cognitive data (PDC) by adopting a multi-channel 3D deep convolutional neural network and pre-training PET images. The results show better diagnosis accuracy than using one single type of medical data alone. These findings reveal the potential of a deep neural network in multi-modal medical data fusion.
癫痫是一种伴有反复发作性癫痫的慢性脑部疾病。内侧颞叶癫痫（MTLE）是癫痫最常见的病理原因。随着计算机辅助诊断技术的发展，出现了许多基于深度学习算法的辅助诊断方法。然而，癫痫的病因复杂，仅通过单一检查模式准确区分不同类型的癫痫具有挑战性。在本研究中，我们的目的是通过采用多通道3D深度卷积神经网络和预训练PET图像，评估来自结构MRI、PET图像、典型临床症状以及个人人口统计学和认知数据（PDC）的多模态癫痫医学信息的组合。结果显示，其诊断准确性优于单独使用单一类型的医学数据。这些发现揭示了深度神经网络在多模态医学数据融合中的潜力。
