Maryam Hussain, Nick J Davis, Yael Benn
A single tDCS session can enhance numerical competence
Neuropsychologia
myelodysplastic syndrome mds myeloproliferative neoplasm mpn bone marrow bm histopathology assessed identify dysplastic cellular morphology cellularity blast excess yet morphologic findings may elude human eye used convolutional neural networks extract morphologic features 236 mds 87 mdsmpn 11 control bm biopsies features predicted genetic cytogenetic aberrations prognosis age gender multivariate regression models highest prediction accuracy found tet2 area receiver operating curve auroc 094 spliceosome mutations 089 chromosome 7 monosomy 089 mutation prediction probability correlated variant allele frequency number affected genes per pathway demonstrating algorithms ability identify relevant morphologic patterns converting regression models texture cellular composition reproduced classical del5q mds morphology consisting hypolobulated megakaryocytes summary study highlights potential linking deep bm histopathology genetics clinical variables histopathology elementary diagnostics patients mds highdimensional data underused elucidating association morphologic features clinical variables molecular genetics study highlights vast potential convolutional neural networks understanding mds pathology genetics reflected bm morphology see related commentary elemento p 195