Prevalence and Diagnosis of Neurological Disorders Using Different Deep Learning Techniques: A Meta-Analysis
This study confers the discipline, frameworks, and methodologies used by different deep learning techniques to diagnose different human neurological disorders. Here, one hundred and thirty-six different articles related to neurological and neuropsychiatric disorders diagnosed using different deep learning techniques are studied. The morbidity and mortality rate of major neuropsychiatric and neurological disorders has also bee n delineated. The performance and publication trend of different deep learning techniques employed in the investigation of these diseases has been examined and analyzed. Different performance metrics like accuracy, specificity, and sensitivity have also been examined. The research implication, chall enges and the future directions related to the study have also been highlighted. Eventually, the research breaches are identified and it is witnessed that there is more scope in the diagnosis of migraine, cerebral palsy and stroke using different deep learning models. Likewise, there is a potential opportunity to use and explore the performance of Restricted Boltzmann Machine, Deep Boltzmann Machine and Deep Belief Network for diagnosis of different human neuropsychiatric and neurological disorders.
Authors: Sabet Sarvestani F, Azarpira N Abstract Heart and cerebral infarctions, as two important ischemic diseases, lead to the death of tissues due to inadequate blood supply and high mortality worldwide. These statuses are started via blockage of vessels and depletion of oxygen and nutrients which affected these areas. After reperfusion and restoration of oxygen supply, more severe injury was mediated by multifaceted cascades of inflammation and oxidative stress. microRNAs (miRNAs) as the regulator of biological and pathological pathways can adjust these conditions by interaction with their targets. Also, miRNAs...
Publication date: Available online 9 October 2020Source: NeuropsychologiaAuthor(s): Erin L. Meier, Shannon M. Sheppard, Emily B. Goldberg, Catherine R. Head, Delaney M. Ubellacker, Alexandra Walker, Argye E. Hillis
Publication date: Available online 9 October 2020Source: Multiple Sclerosis and Related DisordersAuthor(s): Brenda Bertado-Cortés, Claudia Venzor-Mendoza, Daniel Rubio-Ordoñez, José Renán Pérez-Pérez, Lucy Andrea Novelo-Manzano, Lyda Viviana Villamil-Osorio, María de Jesús Jiménez-Ortega, María de la Luz Villalpando-Gueich, Nayeli Alejandra Sánchez-Rosales, Verónica García-Talavera
Publication date: Available online 9 October 2020Source: Neurología (English Edition)Author(s): N. Morollón, R. Belvís, A. De Dios, N. Pagès, C. González-Oria, G. Latorre, S. Santos-Lasaosa
Publication date: Available online 9 October 2020Source: Neurología (English Edition)Author(s): J.P. Martínez-Barbero, P. Tomás-Muñoz, R. Martínez-Moreno
Authors: Kim JS, Hong SH, Kim WS PMID: 33029988 [PubMed]
Authors: Mantero V, Rigamonti A, Basilico P, Sangalli D, Scaccabarozzi C, Salmaggi A PMID: 33029982 [PubMed]
Authors: Kargiotis O, Safouris A, Psychogios K, Chondrogianni M, Andrikopoulou A, Theodorou A, Magoufis G, Stamboulis E, Tsivgoulis G PMID: 33029978 [PubMed]
CONCLUSIONS: In addition to bilateral HA, CNS infection alone was not a poor prognostic factor for the CNS infection-related epilepsy with HA group compared with the conventional MTLE with HA group. Based on these negative results, HA is a plausible and relevant lesion with similar clinical characteristics to HA in patients with conventional MTLE. Therefore, CNS infection-related MTLE with isolated HA might represent another subtype of MTLE with HA with a different etiology. PMID: 33029977 [PubMed]
CONCLUSIONS: Neuro-ophthalmologic findings are mostly normal in patients with visual snow syndrome. Retinal or neurological diseases must be excluded as possible causes of visual snow. PMID: 33029971 [PubMed]