Artificial intelligence and the detection of pediatric concussion using epigenomic analysis.

Artificial intelligence and the detection of pediatric concussion using epigenomic analysis. Brain Res. 2019 Oct 16;:146510 Authors: Bahado-Singh RO, Vishweswaraiah S, Er A, Aydas B, Turkoglu O, Taskin BD, Duman M, Yilmaz D, Radhakrishna U Abstract Concussion, also referred to as mild traumatic brain injury (mTBI) is the most common type of traumatic brain injury. Currently concussion is an area ofintensescientific interest to better understand the biological mechanisms and for biomarker development. We evaluated whole genome-wide blood DNA cytosine ('CpG') methylation in 17 pediatric concussion isolated cases and 18 unaffected controls using Illumina Infinium MethylationEPIC assay. Pathway analysis was performed using Ingenuity Pathway Analysis to help elucidate the epigenetic and molecular mechanisms of the disorder. Area under the receiver operating characteristics (AUC) curves and FDR p-values were calculated for mTBI detection based on CpG methylation levels . Multiple Artificial Intelligence (AI) platforms including Deep Learning (DL), the newest form of AI, were used to predict concussion based on i) CpG methylation markers alone, and ii) combined epigenetic, clinical and demographic predictors. We found 449 CpG sites (473 genes), those were statistically significantly methylated in mTBI compared to controls. There were four CpGs with excellent accuracy (AUC ≥0.90-1.00) while 119 displayed good accuracy (AUC≥0.80-0.89) for...
Source: Brain Research - Category: Neurology Authors: Tags: Brain Res Source Type: research