Diagnostic performance of artificial intelligence in multiple sclerosis: a systematic review and meta-analysis

• Published in: Neurological sciences Vol. 44; no. 2; pp. 499 - 517
• Main Authors: Nabizadeh, Fardin, Ramezannezhad, Elham, Kargar, Amirhosein, Sharafi, Amir Mohammad, Ghaderi, Ali
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01-02-2023; Springer Nature B.V
• Subjects: Accuracy; Area Under Curve; Artificial Intelligence; Databases, Factual; Deep learning; Diagnosis; Humans; Medicine; Medicine & Public Health; Meta-analysis; Multiple sclerosis; Multiple Sclerosis - diagnostic imaging; Neurology; Neuroradiology; Neurosciences; Neurosurgery; Psychiatry; Review Article; Systematic review; Multiple sclerosis; Diagnosis; Artificial intelligence; Machine learning
• ISSN: 1590-1874; 1590-3478
• DOI: 10.1007/s10072-022-06460-7

Background
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.
Methods
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).
Results
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%).
Conclusion
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.