ReviewSensors (Basel, Switzerland)2022
Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities.
Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A bibliometric analysis of studies on artificial intelligence in neuroscience.Frontiers in neurology · 2025Pooled it
- Diagnostic Delay in Multiple Sclerosis in China: A Nationwide Cross-Sectional Study.Advances in therapy · 2026Article
- Synthetic MRI and radiomics in multiple sclerosis: improving precision in diagnosis and prognosis.Radiological physics and technology · 2026Review
- Artificial Intelligence Algorithm Based on Genetics to Predict Responses to Interferon-Beta Treatment in Multiple Sclerosis Patients.Bioengineering (Basel, Switzerland) · 2026Article
- Deep Learning Approaches for Multiple Sclerosis Detection in MRI Images.BioMed research international · 2026Article
- RAUM-GANs: a multi-layer GAN-enhanced framework for accurate multiple sclerosis lesion segmentation in MRI.Scientific reports · 2025Article
- Integrating big data and artificial intelligence to predict progression in multiple sclerosis: challenges and the path forward.Journal of neuroengineering and rehabilitation · 2025Review
- Article
- The Heart-Brain Axis in the Artificial Intelligence Era: Integrating Old and New Insights Towards New Targeting and Innovative Neuro- and Cardio-Therapeutics.International journal of molecular sciences · 2025Review
- OCT in Oncology and Precision Medicine: From Nanoparticles to Advanced Technologies and AI.Bioengineering (Basel, Switzerland) · 2025Review
- The role of AI for MRI-analysis in multiple sclerosis-A brief overview.Frontiers in artificial intelligence · 2025Review
- Machine Learning Radiomics for Predicting Response to MR-Guided Radiotherapy in Unresectable Hepatocellular Carcinoma: A Multicenter Cohort Study.Journal of hepatocellular carcinoma · 2025Article
- A density-based MS disease diagnosis model using the capuchin search algorithm and an ensemble of deep neural networks.Scientific reports · 2024Article
- Time-Dependent Deep Learning Prediction of Multiple Sclerosis Disability.Journal of imaging informatics in medicine · 2024Article
- Towards Transforming Neurorehabilitation: The Impact of Artificial Intelligence on Diagnosis and Treatment of Neurological Disorders.Biomedicines · 2024Review
- Predicting multiple sclerosis disease progression and outcomes with machine learning and MRI-based biomarkers: a review.Journal of neurology · 2024Review
- Oculomics analysis in multiple sclerosis: Current ophthalmic clinical and imaging biomarkers.Eye (London, England) · 2024Review
- Machine learning-based predictive model for the development of thrombolysis resistance in patients with acute ischemic stroke.BMC neurology · 2024Article
- The new era of artificial intelligence in neuroradiology: current research and promising tools.Arquivos de neuro-psiquiatria · 2024Article
- Identification of crucial inflammaging related risk factors in multiple sclerosis.Frontiers in molecular neuroscience · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multiple Sclerosis (MS) is a disease that impacts the central nervous system (CNS), which can lead to brain, spinal cord, and optic nerve problems. A total of 2.8 million are estimated to suffer from MS. Globally, a new case of MS is reported every five minutes. In this review, we discuss the proposed approaches to diagnosing MS using machine learning (ML) published between 2011 and 2022. Numerous models have been developed using different types of data, including magnetic resonance imaging (MRI) and clinical data. We identified the methods that achieved the best results in diagnosing MS. The most implemented approaches are SVM, RF, and CNN. Moreover, we discussed the challenges and opportunities in MS diagnosis to improve AI systems to enable researchers and practitioners to enhance their approaches and improve the automated diagnosis of MS. The challenges faced by automated MS diagnosis include difficulty distinguishing the disease from other diseases showing similar symptoms, protecting the confidentiality of the patients' data, achieving reliable ML models that are also easily understood by non-experts, and the difficulty of collecting a large reliable dataset. Moreover, we discussed several opportunities in the field such as the implementation of secure platforms, employing better AI solutions, developing better disease prognosis systems, combining more than one data type for better MS prediction and using OCT data for diagnosis, utilizing larger, multi-center datasets to improve the reliability of the developed models, and commercialization.
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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 OpenQuestion graph.