Emerging deep learning techniques using magnetic resonance imaging data applied in multiple sclerosis and clinical isolated syndrome patients (Review)
| Autor: | Panagiotis G. Simos, Georgios Z. Papadakis, Eleftherios Kontopodis, Kostas Marias, Aristidis Tsatsakis, Demetrios A. Spandidos, Apostolos H. Karantanas, Eleftherios Trivizakis, Efrosini Papadaki, Thomas G. Maris |
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| Rok vydání: | 2021 |
| Předmět: |
magnetic resonance imaging/diagnosis
Cancer Research medicine.medical_specialty Clinically isolated syndrome medicine.diagnostic_test business.industry Deep learning Multiple sclerosis deep learning Magnetic resonance imaging Diagnostic accuracy Review General Medicine multiple sclerosis medicine.disease clinical isolated syndrome Immunology and Microbiology (miscellaneous) Neuroimaging medicine Automatic segmentation Medical physics In patient Artificial intelligence business |
| Zdroj: | Experimental and Therapeutic Medicine |
| ISSN: | 1792-1015 1792-0981 |
| DOI: | 10.3892/etm.2021.10583 |
| Popis: | Computer-aided diagnosis systems aim to assist clinicians in the early identification of abnormal signs in order to optimize the interpretation of medical images and increase diagnostic precision. Multiple sclerosis (MS) and clinically isolated syndrome (CIS) are chronic inflammatory, demyelinating diseases affecting the central nervous system. Recent advances in deep learning (DL) techniques have led to novel computational paradigms in MS and CIS imaging designed for automatic segmentation and detection of areas of interest and automatic classification of anatomic structures, as well as optimization of neuroimaging protocols. To this end, there are several publications presenting artificial intelligence-based predictive models aiming to increase diagnostic accuracy and to facilitate optimal clinical management in patients diagnosed with MS and/or CIS. The current study presents a thorough review covering DL techniques that have been applied in MS and CIS during recent years, shedding light on their current advances and limitations. |
| Databáze: | OpenAIRE |
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