Crowd‐powered histopathological analysis of brain samples from persons with Alzheimer disease: a Citizen Science pilot study inclusive of Hispanic communities

Through methodological development, including testing of annotation interfaces and consensus methods, we found with sufficient aggregation of non-expert answers, it is possible to consistently achieve a level of agreement with gold standard data meeting or exceeding interrater agreement among expert annotators for both stages of analysis CONCLUSION: Our approaches support the rapid production of high-volume annotation datasets for use in machine learning algorithm development to provide deeper phenotyping of neurodegenerative diseases. These pilot results support the plausibility of moving forward with an openly available online platform, which will be used to execute the first ever de-novo analysis of human brain histopathology images by a crowd of volunteers. This platform will be geared towards impacted populations, such as those of Hispanic heritage, by providing a culturally responsive outreach and inclusion program for such communities to represent themselves in discussions with researchers.

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Publication Details

Journal
Alzheimer s & Dementia
Published
2025-12-01
DOI
https://doi.org/10.1002/alz70855_105964
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
0.00
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article

Crowd‐powered histopathological analysis of brain samples from persons with Alzheimer disease: a Citizen Science pilot study inclusive of Hispanic communities

Michael J. Keiser, J J Lou, Shino Magaki, Brittany N. Dugger et al.
Alzheimer s & Dementia
AI in cancer detection
article

Crowd‐powered histopathological analysis of brain samples from persons with Alzheimer disease: a Citizen Science pilot study inclusive of Hispanic communities

Michael J. Keiser, J J Lou, Shino Magaki, Brittany N. Dugger, Laura Onac, Pietro Michelucci, Margaret Lane, Paz Santander, Lisa Gusman, Ravish Dussaruth, Gretė Vaičaitytė
article en

Abstract

Through methodological development, including testing of annotation interfaces and consensus methods, we found with sufficient aggregation of non-expert answers, it is possible to consistently achieve a level of agreement with gold standard data meeting or exceeding interrater agreement among expert annotators for both stages of analysis CONCLUSION: Our approaches support the rapid production of high-volume annotation datasets for use in machine learning algorithm development to provide deeper phenotyping of neurodegenerative diseases. These pilot results support the plausibility of moving forward with an openly available online platform, which will be used to execute the first ever de-novo analysis of human brain histopathology images by a crowd of volunteers. This platform will be geared towards impacted populations, such as those of Hispanic heritage, by providing a culturally responsive outreach and inclusion program for such communities to represent themselves in discussions with researchers.

Alzheimer s & DementiaVol. 21(S1)
University of California, Los Angeles (US), University of California, San Francisco (US), University of California, Irvine (US), Human Computation Institute, University of California, Davis (US)
Quality Education
Openalex Percentile: Top 18%
AI in cancer detection
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