In Journal of neuropathology and experimental neurology ; h5-index 36.0
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.
Scalco Rebeca, Hamsafar Yamah, White Charles L, Schneider Julie A, Reichard Robert Ross, Prokop Stefan, Perrin Richard J, Nelson Peter T, Mooney Sean, Lieberman Andrew P, Kukull Walter A, Kofler Julia, Keene Christopher Dirk, Kapasi Alifiya, Irwin David J, Gutman David A, Flanagan Margaret E, Crary John F, Chan Kwun C, Murray Melissa E, Dugger Brittany N
2023-Jan-24
Alzheimer disease, Computational pathology, Deep Learning, Digital pathology, Machine Learning, Quantitative pathology, Slide scanner
