AI-based "tissue clocks" measure the biological age of human organs

AI-based "tissue clocks" can estimate the biological age of human organs from histological images, researchers at the CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences and the Ludwig Boltzmann Institute for Network Medicine (LBI-NetMed) at the University of Vienna showed. By analyzing more than 25,000 tissue samples across 40 tissue types, their study reveals that organs age at different rates throughout life and that these changes can even be detected from blood samples. The findings, published in Nature Medicine (DOI: 10.1038/s41591-026-04566-5), provide a new framework for understanding aging and may open new avenues for disease monitoring and early diagnosis.

Some people seem to age slower than others, looking and acting like 45 at 60. Others appear to have gotten ahead of the calendar. But why is that, and what is actually happening inside the body? Does a liver age differently from a brain? And is it possible to measure the gap between the age on a passport and the biological age of each organ?

By combining artificial intelligence with one of the world's largest collections of human tissue images, a new study led by CeMM and LBI-NetMed Principal Investigator André Rendeiro and co-first authored by Ernesto Abila, Iva Buljan, and Yimin Zheng, takes a large step towards answering these questions. While previous studies focused mainly on molecular changes such as DNA methylation or gene expression, the team examines how the architecture of tissues themselves changes over time.

To do this, the researchers turned to the Genotype-Tissue Expression Project (GTEx), which collected tissue samples from 983 individuals across 40 different tissue types, ranging from the brain and heart to the lung, pancreas, skin, and intestine. These were transformed into high-resolution digital photographs of tissue slices, each revealing the microscopic architecture of the organ in question. The scale is staggering: 25,712 images, representing ~480 million individual image tiles, analyzed with state-of-the-art vision models.