Same Precision, Quarter the Work Through Statistical Computing in COVID-19 Histopathology

Evaluating COVID-19 lung damage in rhesus macaques requires examining thousands of microscopic fields—a process that can take weeks per study and creates significant bottlenecks in therapeutic development.

Our team at Tulane and UC Davis has revolutionized this process through advanced statistical computing. Using bootstrap simulation with 10,000 iterations and Two One-Sided Tests (TOST) equivalence analysis, we proved that strategic sampling can reduce workload by up to 75% while maintaining full diagnostic accuracy.

The computational framework revealed that optimal sampling varies by lung anatomy: caudal lobes need only 25-30% sampling, while middle lobes require 60-75%. This isn't arbitrary reduction—it's statistically validated optimization. Our approach achieved substantial inter-rater reliability (Cohen's κ = 0.74) and demonstrated robust statistical power across all lung regions.

This breakthrough exemplifies how computational methods can transform traditional pathology. By applying rigorous statistical validation to biological questions, we've created a framework that accelerates COVID-19 research without compromising quality. The methodology and code are publicly available, offering a blueprint for similar optimization in other heterogeneous diseases.

Title: When less is statistically Equivalent: Lobe-Specific optimization of histopathological assessment in SARS-CoV-2 pneumonia

https://doi.org/10.1016/j.compbiomed.2025.110577