Artificial intelligence in neuropathology: deep learning-based assessment of tauopathy

Hoglinger, 2018, New classification of tauopathies, Rev Neurol (Paris), 10.1016/j.neurol.2018.07.001 Lebouvier, 2017, Update on tauopathies, Curr Opin Neurol, 30, 589, 10.1097/WCO.0000000000000502 Morris, 2011, The many faces of tau, Neuron, 70, 410, 10.1016/j.neuron.2011.04.009 Höglinger, 2011, Identification of common variants influencing risk of the tauopathy progressive supranuclear palsy, Nat Genet, 43, 699, 10.1038/ng.859 Bennett, 2012, Overview and findings from the rush memory and aging project, Curr Alzheimer Res, 9, 646, 10.2174/156720512801322663 Ghetti, 2015, Invited review: Frontotemporal dementia caused by microtubule-associated protein tau gene (MAPT) mutations: a chameleon for neuropathology and neuroimaging, Neuropathol Appl Neurobiol, 41, 24, 10.1111/nan.12213 Cox, 2016, Dietary exposure to an environmental toxin triggers neurofibrillary tangles and amyloid deposits in the brain, Proc R Soc B, 283, 20152397, 10.1098/rspb.2015.2397 McKee, 2016, The first NINDS NIBIB consensus meeting to define neuropathological criteria for the diagnosis of chronic traumatic encephalopathy, Acta Neuropathol, 131, 75, 10.1007/s00401-015-1515-z Ferrer, 2013, Glial and neuronal tau pathology in tauopathies: characterization of disease-specific phenotypes and tau pathology progression, J Neuropathol Exp Neurol, 73, 81, 10.1097/NEN.0000000000000030 Kahlson, 2015, Glial tau pathology in tauopathies: functional consequences, J Exp Neurosci, 9, 43 Kovacs, 2017, Tauopathies Handb Clin Neurol, 145, 355, 10.1016/B978-0-12-802395-2.00025-0 Murray, 2014, Clinicopathologic assessment and imaging of tauopathies in neurodegenerative dementias, Alzheimer's Res Ther, 6, 1, 10.1186/alzrt231 Braak, 1991, Neuropathological staging of Alzheimer-related changes, Acta Neuropathol, 82, 239, 10.1007/BF00308809 Jellinger, 2018, Different patterns of hippocampal tau pathology in Alzheimer's disease and PART, Acta Neuropathol, 10.1007/s00401-018-1894-z Salloway, 2015, Understanding conflicting neuropathological findings in patients clinically diagnosed as having Alzheimer dementia, JAMA Neurol, 72, 1106, 10.1001/jamaneurol.2015.1804 Al-Janabi, 2012, Digital pathology: current status and future perspectives, Histopathology, 61, 1, 10.1111/j.1365-2559.2011.03814.x Litjens, 2017, A survey on deep learning in medical image analysis, Med Image Anal, 42, 60, 10.1016/j.media.2017.07.005 Stead, 2018, Clinical implications and challenges of artificial intelligence and deep learning, JAMA, 320, 1107, 10.1001/jama.2018.11029 Hinton, 2018, Deep learning—a technology with the potential to transform health care, JAMA, 320, 1101, 10.1001/jama.2018.11100 LeCun, 2015, Deep learning, Nature, 28, 436, 10.1038/nature14539 Naylor, 2018, On the prospects for a (deep) learning health care system, JAMA, 320, 1099, 10.1001/jama.2018.11103 Aresta, 2018, BACH: Grand Challenge on Breast Cancer Histology Images, arXiv Prepr, arXiv, 1808.04277 Donovan, 2018, Development and validation of a novel automated Gleason grade and molecular profile that define a highly predictive prostate cancer progression algorithm-based test, Prostate Cancer Prostatic Dis, 10.1038/s41391-018-0067-4 Crary, 2014, Primary age-related tauopathy (PART): a common pathology associated with human aging, Acta Neuropathol, 128, 755, 10.1007/s00401-014-1349-0 Scott R, Khan FM, Zeineh J, Donovan M, Fernandez G. Gland ring morphometry for prostate cancer prognosis in multispectral immunofluorescence images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention 2014 LNCS 8673. Cham: Springer; 2014. p. 585–92. Badrinarayanan, 2017, SegNet:a deep convolutional encoder-decoder architecture for image segmentation, IEEE Trans Pattern Anal Mach Intell, 39, 2481, 10.1109/TPAMI.2016.2644615 Paszke, 2017, Automatic differentiation Esteva, 2017, Dermatologist-level classification of skin cancer with deep neural networks, Nature, 542, 115, 10.1038/nature21056 Gulshan, 2016, Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, JAMA, 316, 2402, 10.1001/jama.2016.17216