ArticleFrontiers in neurology2025
Assessing the effectiveness of machine learning and deep learning in differentiating neuroimmunological diseases: a systematic review and meta-analysis.
Article in Frontiers in neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Current and Future Biomarkers in the Diagnosis of Autoimmune Encephalitis: A Review of Biomarker Detection Techniques and Their Performance.Medicina (Kaunas, Lithuania) · 2026Review
- Multimodal MRI biomarkers and machine learning in neuromyelitis optica spectrum disorder: a review.Frontiers in neurology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: 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. Methods: 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. Results: 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. Conclusion: 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.