The emerging role of artificial intelligence in multiple sclerosis imaging.

H M Rehan Afzal, Suhuai Luo, Saadallah Ramadan, Jeannette Lechner-Scott
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引用次数: 27

Abstract

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.

人工智能在多发性硬化症成像中的新兴作用。
背景:计算机辅助诊断可以促进多发性硬化症(MS)的早期发现和诊断,从而使早期干预和减少与MS相关的长期残疾成为可能。人工智能(AI)领域的最新进展导致了对一系列疾病(特别是多发性硬化症)的医学图像诊断模式的分类、量化和识别的改进。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是有利的。目的:本综述旨在帮助多发性硬化症研究人员了解基于人工智能的多发性硬化症诊断和预后的现状和未来发展。方法:我们将研究各种人工智能方法和各种分类器,并比较目前最先进的技术在病变分割/检测和疾病预后方面的应用。在简要描述了常用的磁共振成像(MRI)技术之后,我们将描述用于检测病变和MS预后的AI技术。结果:我们随后评估了这些人工智能技术与ms相关的临床成熟度。结论:最后,确定了未来研究的挑战,以鼓励进一步改进这些方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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