{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T14:39:21Z","timestamp":1787063961081,"version":"build-2736575974"},"reference-count":42,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T00:00:00Z","timestamp":1768176000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>The differential diagnosis of neuroimmunological disorders remains a significant challenge in clinical practice, even with advancements in diagnostic techniques. Recently, the use of artificial intelligence (AI) for diagnosing and distinguishing between various neuroimmunological disorders has gained traction. Our objective was to conduct a systematic review and meta-analysis to evaluate the diagnostic performance of Machine Learning (ML) and Deep Learning (DL) techniques in differentiating these disorders. We aimed to identify the most effective approaches, compare their diagnostic outcomes, and offer recommendations for improving their applicability across multiple clinical centers and for future research.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Following the PRISMA 2020 guidelines, a systematic search in PubMed and Web of Science was conducted to identify relevant articles published between 2000 and 2024 that fell within the scope of our research. QUADAS-2 tool was assessed to evaluate the risk of bias and applicability concerns. The performed meta-analysis allowed us to estimate the overall accuracy, sensitivity, and specificity of the developed models providing quantitative insights from this analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Of 4,470 articles identified, 19 met inclusion criteria: 9 (47.4%) used ML and 10 (52.6%) used DL. Most models relied on MRI data to differentiate multiple sclerosis from neuromyelitis optica spectrum disorders. Pooled accuracy, sensitivity, and specificity were 0.87, 0.86, and 0.84, respectively. Substantial heterogeneity was observed, which decreased in a sensitivity analysis excluding larger-sample studies and varied between ML and DL models, with ML showing lower heterogeneity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>New AI tools, primarily utilizing MRI data, are emerging and demonstrate the potential to differentiate between various neuroimmunological disorders. While most neuroimmunological conditions have accessible antibody tests with strong diagnostic performance, AI efforts should concentrate on seronegative diseases. This approach should incorporate clinical and epidemiological data into diagnostic algorithms for improved accuracy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fneur.2025.1579206","type":"journal-article","created":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T08:06:06Z","timestamp":1768205166000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Assessing the effectiveness of machine learning and deep learning in differentiating neuroimmunological diseases: a systematic review and 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