Robichaud, Elizabeth

AbstractBackgroundDigital pathology and machine learning (ML) workflows have paradigm shifting potential especially in the field of Alzheimer’s disease and related disorders. However, many institutions/centers may not have access to these technologies. To provide benchmark data, a survey was developed and distributed to National Institutes of Health’s Alzheimer’s Disease Centers (ADCs) in the United States.MethodSurvey questions covered topics such as: infrastructure (i.e. type of digital slide scanners used), funding sources (i.e. how the scanner was funded), and data management/storage (i.e. size of digital files). After development, review, and approval by the ADC digital pathology working group, the survey was distributed via email to 35 past and current ADC directors and/or ADC neuropathology core leaders. Participation in the survey was completely voluntary and answers did not contain any personal data.ResultThe survey generated over a 90% response rate, and the majority of those who completed the survey were neuropathology core leaders; 81% stated their ADC had access to a digital slide scanner, most common brand being Aperio/Leica. One third of respondents stated there was a fee for service to utilize the scanner. For digital pathology and/or ML resources, 40% of respondents stated none are supported in any way by their ADC. To cover the purchase and operation of the scanner, 50% stated they had institutional support. Many were unsure of the approximate average scanned file size of digital images (37%) and total amount of storage space that files occupied (50%). Many (75%) were aware of other departments at their institution working with digital pathology and/or ML, but a similar percentage was unaware of multi‐university or industry partnerships.ConclusionThese results demonstrate many ADCs have access to a digital slide scanner and had institutional support to cover the purchase. However, further investigation is needed to understand hurdles and barriers for implementing both digital pathology and ML workflows aiding in standardized methods across ADCs.

Introduction: In 2015, the US Alzheimer’s Disease Centers (ADC) implemented Version 3 of the Uniform Data Set (UDS). This paper describes the history of Version 3 development and the UDS data that are freely available to researchers. Methods: UDS Version 3 was developed after years of coordination between the National Institute on Aging-appointed Clinical Task Force (CTF), clinicians from ∼30 ADCs, and the National Alzheimer’s Coordinating Center (NACC). The CTF recognized the need for updates to align with the state of the science in dementia research, while being flexible to the diverse needs and diseases studied at the ADCs. Version 3 also developed a nonproprietary neuropsychological battery. Results: This paper focuses on the substantial Version 3 changes to the UDS forms related to clinical diagnosis and characterization of clinical symptoms to match updated consensus-based diagnostic criteria. Between March 2015 and March 2018, 4820 participants were enrolled using UDS Version 3. Longitudinal data were available for 25,337 of the 37,568 total participants using all UDS versions. Discussion: The results from utilization of the UDS highlight the possibility for numerous research institutions to successfully collaborate, produce, and use standardized data collection instruments for over a decade.