Versatile AI System Revolutionizes Analysis of Medical Image Series: Study Finds - Video

A new AI-based system for analyzing images taken over time can accurately detect changes and predict outcomes, according to a study led by investigators at Weill Cornell Medicine, Cornell’s Ithaca campus and Cornell Tech. The system’s sensitivity and flexibility could make it useful across a wide range of medical and scientific applications.

The new system, termed LILAC (Learning-based Inference of Longitudinal imAge Changes), is based on an AI approach called machine learning

In the study, the researchers developed the system and demonstrated it on diverse time-series of images—also called “longitudinal” image series—covering developing IVF embryos, healing tissue after wounds and aging brains. The researchers showed that LILAC has a broad ability to identify even very subtle differences between images taken at different times, and to predict related outcome measures such as cognitive scores from brain scans.

This new tool will allow us to detect and quantify clinically relevant changes over time in ways that weren't possible before, and its flexibility means that it can be applied off-the-shelf to virtually any longitudinal imaging dataset,” said study senior author Dr. Mert Sabuncu, vice chair of research and a professor of electrical engineering in radiology at Weill Cornell Medicine and professor in the School of Electrical and Computer Engineering at Cornell University’s Ithaca campus and Cornell Tech.

LILAC also was highly accurate in ordering pairs of images of healing tissue from the same sequences, and in detecting group-level differences in healing rates between untreated tissue and tissue that received an experimental treatment.

The researchers showed in all these cases that LILAC can be adapted easily to highlight the image features that are most relevant for detecting changes in individuals or differences between groups—which could provide new clinical and even scientific insights.

Ref: H. Kim, B.K. Karaman, Q. Zhao, A.Q. Wang, M.R. Sabuncu, & for the Alzheimer’s Disease Neuroimaging Initiative, Learning-based inference of longitudinal image changes: Applications in embryo development, wound healing, and aging brain, Proc. Natl. Acad. Sci. U.S.A. 122 (8) e2411492122, https://doi.org/10.1073/pnas.2411492122 (2025).