BACKGROUND Computer-aided diagnosis can facilitate the early detection and diagnosis of multiple sclerosis (MS) thus enabling earlier interventions and a reduction in long-term MS-related disability. Recent advancements in the field of artificial intelligence (AI) have led to the improvements in the classification, quantification and identification of diagnostic patterns in medical images for a range of diseases, in particular, for MS. Importantly, data generated using AI techniques are analyzed automatically, which compares favourably with labour-intensive and time-consuming manual methods. OBJECTIVE The aim of this review is to assist MS researchers to understand current and future developments in the AI-based diagnosis and prognosis of MS. METHODS We will investigate a variety of AI approaches and various classifiers and compare the current state-of-the-art techniques in relation to lesion segmentation/detection and prognosis of disease. After briefly describing the magnetic resonance imaging (MRI) techniques commonly used, we will describe AI techniques used for the detection of lesions and MS prognosis. RESULTS We then evaluate the clinical maturity of these AI techniques in relation to MS. CONCLUSION Finally, future research challenges are identified in a bid to encourage further improvements of the methods.

中文翻译：

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人工智能在多发性硬化症成像中的新兴作用

背景技术计算机辅助诊断可以促进多发性硬化症（MS）的早期检测和诊断，从而能够进行早期干预并减少与MS相关的长期残疾。人工智能 (AI) 领域的最新进展导致医学图像中诊断模式的分类、量化和识别方面的改进，用于一系列疾病，特别是 MS。重要的是，使用人工智能技术生成的数据是自动分析的，这与劳动密集型和耗时的手动方法相比具有优势。目的 本综述的目的是帮助 MS 研究人员了解基于 AI 的 MS 诊断和预后的当前和未来发展。方法 我们将研究各种 AI 方法和各种分类器，并比较当前与病灶分割/检测和疾病预后相关的最先进技术。在简要介绍常用的磁共振成像 (MRI) 技术后，我们将介绍用于检测病变和 MS 预后的 AI 技术。结果然后我们评估这些 AI 技术与 MS 相关的临床成熟度。结论 最后，确定了未来的研究挑战，以鼓励进一步改进这些方法。结果然后我们评估这些 AI 技术与 MS 相关的临床成熟度。结论 最后，确定了未来的研究挑战，以鼓励进一步改进这些方法。结果然后我们评估这些 AI 技术与 MS 相关的临床成熟度。结论 最后，确定了未来的研究挑战，以鼓励进一步改进这些方法。