Neuroscience Research Group (NRG), Universal Scientific Education and Research Network (USERN), Tehran, Iran.
School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
Neurol Sci. 2023 Feb;44(2):499-517. doi: 10.1007/s10072-022-06460-7. Epub 2022 Oct 27.
The expansion of the availability of advanced imaging methods needs more time, expertise, and resources which is in contrast to the primary goal of the imaging techniques. To overcome most of these difficulties, artificial intelligence (AI) can be used. A number of studies used AI models for multiple sclerosis (MS) diagnosis and reported diverse results. Therefore, we aim to perform a comprehensive systematic review and meta-analysis study on the role of AI in the diagnosis of MS.
We performed a systematic search using four databases including PubMed, Scopus, Web of Science, and IEEE. Studies that applied deep learning or AI to the diagnosis of MS based on any modalities were considered eligible in our study. The accuracy, sensitivity, specificity, precision, and area under curve (AUC) were pooled with a random-effects model and 95% confidence interval (CI).
After the screening, 41 articles with 5989 individuals met the inclusion criteria and were included in our qualitative and quantitative synthesis. Our analysis showed that the overall accuracy among studies was 94% (95%CI: 93%, 96%). The pooled sensitivity and specificity were 92% (95%CI: 90%, 95%) and 93% (95%CI: 90%, 96%), respectively. Furthermore, our analysis showed 92% precision in MS diagnosis for AI studies (95%CI: 88%, 97%). Also, the overall pooled AUC was 93% (95%CI: 89%, 96%).
Overall, AI models can further improve our diagnostic practice in MS patients. Our results indicate that the use of AI can aid the clinicians in accurate diagnosis of MS and improve current diagnostic approaches as most of the parameters including accuracy, sensitivity, specificity, precision, and AUC were considerably high, especially when using MRI data.
先进成像方法的可用性的扩大需要更多的时间、专业知识和资源，这与成像技术的主要目标形成了对比。为了克服这些困难，人工智能（AI）可以被应用。许多研究都使用 AI 模型对多发性硬化症（MS）进行诊断，并报告了不同的结果。因此，我们旨在对 AI 在 MS 诊断中的作用进行全面的系统评价和荟萃分析研究。
我们使用包括 PubMed、Scopus、Web of Science 和 IEEE 在内的四个数据库进行了系统搜索。在我们的研究中，应用深度学习或 AI 基于任何模态对 MS 进行诊断的研究都被认为是合格的。使用随机效应模型和 95%置信区间（CI）对准确性、敏感度、特异性、精密度和曲线下面积（AUC）进行汇总。
筛选后，41 篇文章共 5989 人符合纳入标准，纳入我们的定性和定量综合分析。我们的分析表明，研究中整体准确率为 94%（95%CI：93%，96%）。汇总的敏感度和特异性分别为 92%（95%CI：90%，95%）和 93%（95%CI：90%，96%）。此外，我们的分析表明，AI 研究中 MS 诊断的准确率为 92%（95%CI：88%，97%）。此外，总体汇总 AUC 为 93%（95%CI：89%，96%）。
总体而言，AI 模型可以进一步提高我们对 MS 患者的诊断实践。我们的结果表明，AI 的使用可以帮助临床医生对 MS 进行准确诊断，并改进当前的诊断方法，因为大多数参数，包括准确性、敏感度、特异性、精密度和 AUC 都相当高，尤其是在使用 MRI 数据时。
