When less is statistically Equivalent: Lobe-Specific optimization of histopathological assessment in SARS-CoV-2 pneumonia
This first anatomically-stratified, statistically-validated methodology significantly enhances histopathological assessment efficiency in the rhesus macaque COVID-19 model. By establishing lobe-specific minimum sampling thresholds that preserve statistical equivalence, our approach optimizes resource utilization while maintaining sensitivity to detect intervention effects, potentially accelerates preclinical therapeutic evaluations.
Authors
- J. Rachel Reader (ORCID: https://orcid.org/0000-0003-3864-6731)
- Lincoln Hopkins (ORCID: https://orcid.org/0000-0002-3874-3585)
- Katherine J. Olstad (ORCID: https://orcid.org/0000-0003-3894-0981)
- Koen K. A. Van Rompay (ORCID: https://orcid.org/0000-0002-7375-1337)
- Amir Ardeshir (ORCID: https://orcid.org/0000-0002-4330-4659)
Institutions
- Tulane University (US)
- University of California, Davis (US)
Publication Details
- Journal
- Computers in Biology and Medicine
- Published
- 2025-06-24
- DOI
- https://doi.org/10.1016/j.compbiomed.2025.110577
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- California National Primate Research Center
- Office of Research Infrastructure Programs, National Institutes of Health