Machine learning paves the way forward for geropathology assessment

Abstract

Geropathology investigates the pathological aspects of aging, analyzing age-related lesions in organs to track their progression and link to comorbidities. Utilizing geropathology grading scores, researchers evaluate drug efficacy in mammalian models, providing insights into the biological pathways of aging and disease development. Sheehan et al. introduced a novel approach using a weakly supervised machine learning algorithm to analyze age-related differences in mouse kidneys. This method, using chronological age as labels, effectively identifies age-associated histopathology. The model was validated against the Geropathology Research Network grading scheme, demonstrating high reliability and effectiveness in discerning drug treatment effects. The model’s reproducibility and ease of implementation, supported by tutorials and GitHub resources, enhance its utility. However, challenges include potential over-precision, the need for pathologist support, and strict whole slide image processing to prevent false detections. Future research could extend to additional systemic organs and various animal models, leveraging genetic similarities in lesion structures to improve grading precision and translational relevance to human aging and diseases.

Keywords: Geropathology, aging, machine learning, age-related lesions, drug efficacy