Geropathology, within the field of geroscience, focuses on the pathological aspects of aging and its association with diseases [1]. It involves assessing age-related lesions in organs, grading their severity to assign quantitative values. This detailed histopathological analysis helps track how these lesions progress with age and their links to comorbidities. By using geropathology grading scores, researchers can evaluate the effectiveness of drugs in mammalian animal models, providing crucial insights into the biological pathways of aging and the development of age-related diseases. These grading scores serve as critical endpoints in drug studies, allowing for precise measurement of how treatments impact the severity and progression of ageassociated lesions.
Sheehan et al. recently introduced a novel approach, leveraging a supervised machine learning (ML) algorithm to analyze age-related differences in mouse kidneys [2]. Unlike traditional ML methods, weakly supervised learning operates with loosely specified prediction targets, blending aspects of both supervised and unsupervised learning. Essentially, it discerns age-associated histopathology using sample-level chronological age for each whole slide image (WSI), where higher chronological age serves as the label. Analogous to teaching a student with incomplete information, weakly supervised learning provides general hints or clues to the computer, prompting it to discern patterns and make predictions based on these cues. While not as precise as fully supervised learning, which requires detailed labels for every data point, weakly supervised learning empowers the computer to make educated guesses, providing invaluable information for tasks where obtaining precise labels for all data are challenging or time-consuming.
Sheehan et al. validated their model by correlating scores generated by pathologists using the Geropathology Research Network aging grading scheme [3], demonstrating high inter-reliability when compared to independent scoring by board-certified veterinary pathologists [2]. Furthermore, they showed that this classifier can discern not only age-related differences, but also differences resulting from drug treatment, such as a combination of rapamycin, acarbose, and phenylbutyrate, which was shown by our laboratory to be effective in enhancing resilience to aging in C57Bl/6 and HET3 mice [4] (Figure 1). Importantly, the model’s reproducibility allows for repeatable implementation in further studies, and the authors have provided tutorials and step-by-step directions on how to implement the code provided on GitHub. Additionally, the classifier’s throughput and portability make it feasible to run on individual computers.
Nevertheless, implementing this technique presents several significant challenges. As addressed by Sheehan et al., while the evaluation of all pixels in an image ensures unbiased scoring, it also risks over-precision of estimates. Since the algorithm lacks the ability to differentiate lesions from non-lesions, its role is limited to identifying differences between age groups. Thus, ongoing support from trained pathologists is essential to discern subtle details over time before full autonomy can be achieved. Moreover, strict processing of WSIs is imperative to maintain precision. Any mechanical variability in tissue processing, slide preparation or imaging can significantly impact the detection of false positives and negatives [5]. Standardizing these procedures is therefore crucial to minimize variability. Lastly, this technique only detects differences from normal tissue rather than specific abnormalities, unlike supervised ML methods. While valuable for assessing age-related degeneration in organ systems, it cannot replace supervised ML techniques for identifying specific anomalies in tissues.
Further investigations could explore examining additional systemic organs (i.e., brain, heart, lung, liver, pancreas, spleen, skeletal muscle) within and beyond the mouse model, extending to vertebrate models such as cats and invertebrate models like the house cricket. Both hold substantial translational significance in studying human aging and age-related diseases and are of great interest for our laboratory [6, 7]. Through leveraging the genetically preserved similarities in lesion structure and shape across species, machine learning techniques could be employed to discern subtle differences between animal tissue types and histopathological appearances. This approach would not only enhance the precision of grading, but also shed light on variations between animal models. Identifying limitations and similarities in these models would allow for more accurate interpretation of findings and better understanding of translational relevance in histopathology research for humans.

1. Ladiges W, Ikeno Y, Niedernhofer L, McIndoe RA, Ciol MA, Ritchey J, et al. The geropathology research network: an interdisciplinary approach for integrating pathology into research on aging. J Gerontol A Biol Sci Med Sci, 2016, 71(4): 431-434. [Crossref]

2. Sheehan S, Mawe S, Chen M, Klug J, Ladiges W, Korstanje R, et al. A machine learning approach for quantifying age-related histological changes in the mouse kidney. Geroscience, 2024, 46(2): 2571-2581. [Crossref]

3. Snyder JM, Snider TA, Ciol MA, Wilkinson JE, Imai DM, Casey KM, et al. Validation of a geropathology grading system for aging mouse studies. Geroscience, 2019, 41(4): 455-465. [Crossref]

4. Jiang Z, Wang J, Imai D, Snider T, Klug J, Mangalindan R, et al. Short term treatment with a cocktail of rapamycin, acarbose and phenylbutyrate delays aging phenotypes in mice. Sci Rep, 2022, 12(1): 7300-7310. [Crossref]

5. La Perle KMD. Machine learning and veterinary pathology: be not afraid! Vet Pathol, 2019, 56(4): 506-507. [Crossref]

6. Wezeman J, Keely A, & Ladiges W. Resilience to aging drives personalized intervention strategies for Alzheimer’s disease. Aging Pathobiology and Therapeutics, 2023, 5(4): 151-153. [Crossref]

7. Liao GY, Rosenfeld M, Wezeman J, & Ladiges W. The house cricket is an unrecognized but potentially powerful model for aging intervention studies. Aging Pathobiology and Therapeutics, 2024, 6(1): 39-41. [Crossref]