Shoeibi Afshin, Khodatars Marjane, Jafari Mahboobeh, Moridian Parisa, Rezaei Mitra, Alizadehsani Roohallah, Khozeimeh Fahime, Gorriz Juan Manuel, Heras Jónathan, Panahiazar Maryam, Nahavandi Saeid, Acharya U Rajendra
Faculty of Electrical Engineering, Biomedical Data Acquisition Lab (BDAL), K. N. Toosi University of Technology, Tehran, Iran.
Faculty of Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
Comput Biol Med. 2021 Sep;136:104697. doi: 10.1016/j.compbiomed.2021.104697. Epub 2021 Jul 31.
Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor problems for people with a detrimental effect on the functioning of the nervous system. In order to diagnose MS, multiple screening methods have been proposed so far; among them, magnetic resonance imaging (MRI) has received considerable attention among physicians. MRI modalities provide physicians with fundamental information about the structure and function of the brain, which is crucial for the rapid diagnosis of MS lesions. Diagnosing MS using MRI is time-consuming, tedious, and prone to manual errors. Research on the implementation of computer aided diagnosis system (CADS) based on artificial intelligence (AI) to diagnose MS involves conventional machine learning and deep learning (DL) methods. In conventional machine learning, feature extraction, feature selection, and classification steps are carried out by using trial and error; on the contrary, these steps in DL are based on deep layers whose values are automatically learn. In this paper, a complete review of automated MS diagnosis methods performed using DL techniques with MRI neuroimaging modalities is provided. Initially, the steps involved in various CADS proposed using MRI modalities and DL techniques for MS diagnosis are investigated. The important preprocessing techniques employed in various works are analyzed. Most of the published papers on MS diagnosis using MRI modalities and DL are presented. The most significant challenges facing and future direction of automated diagnosis of MS using MRI modalities and DL techniques are also provided.
多发性硬化症（MS）是一种脑部疾病，会给患者带来视觉、感觉和运动方面的问题，对神经系统的功能产生不利影响。为了诊断MS，目前已经提出了多种筛查方法；其中，磁共振成像（MRI）在医生中受到了相当大的关注。MRI模态为医生提供有关大脑结构和功能的基本信息，这对于快速诊断MS病变至关重要。使用MRI诊断MS既耗时又繁琐，而且容易出现人工错误。基于人工智能（AI）的计算机辅助诊断系统（CADS）用于诊断MS的研究涉及传统机器学习和深度学习（DL）方法。在传统机器学习中，特征提取、特征选择和分类步骤是通过反复试验来进行的；相反，DL中的这些步骤是基于其值会自动学习的深层。本文对使用DL技术结合MRI神经成像模态进行的自动MS诊断方法进行了全面综述。首先，研究了使用MRI模态和DL技术进行MS诊断所提出的各种CADS中涉及的步骤。分析了各种工作中采用的重要预处理技术。介绍了大多数关于使用MRI模态和DL进行MS诊断的已发表论文。还提供了使用MRI模态和DL技术进行MS自动诊断面临的最重大挑战和未来方向。
