학술논문

The status of digital pathology and associated infrastructure within Alzheimer’s Disease Centers
Document Type
article
Source
Journal of Neuropathology & Experimental Neurology. 82(3)
Subject
Biomedical and Clinical Sciences
Neurosciences
Clinical Sciences
Neurodegenerative
Alzheimer's Disease
Aging
Acquired Cognitive Impairment
Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD)
Dementia
Brain Disorders
Industry
Innovation and Infrastructure
Humans
Alzheimer Disease
Workflow
Machine Learning
Surveys and Questionnaires
Alzheimer disease
Computational pathology
Deep Learning
Digital pathology
Quantitative pathology
Slide scanner
Neurology & Neurosurgery
Clinical sciences
Language
Abstract
Digital pathology (DP) has transformative potential, especially for Alzheimer disease and related disorders. However, infrastructure barriers may limit adoption. To provide benchmarks and insights into implementation barriers, a survey was conducted in 2019 within National Institutes of Health's Alzheimer's Disease Centers (ADCs). Questions covered infrastructure, funding sources, and data management related to digital pathology. Of the 35 ADCs to which the survey was sent, 33 responded. Most respondents (81%) stated that their ADC had digital slide scanner access, with the most frequent brand being Aperio/Leica (62.9%). Approximately a third of respondents stated there were fees to utilize the scanner. For DP and machine learning (ML) resources, 41% of respondents stated none was supported by their ADC. For scanner purchasing and operations, 50% of respondents stated they received institutional support. Some were unsure of the file size of scanned digital images (37%) and total amount of storage space files occupied (50%). Most (76%) were aware of other departments at their institution working with ML; a similar (76%) percentage were unaware of multiuniversity or industry partnerships. These results demonstrate many ADCs have access to a digital slide scanner; additional investigations are needed to further understand hurdles to implement DP and ML workflows.