The top section illustrates LILAC’s prediction of changes in the Clinical Dementia Rating Scale Sum of Boxes, a dementia scoring system, while the bottom section accounts for age and sex as additional factors. The differences in the highlighted regions suggest that the model relies on different parts of the image depending on whether these factors are considered. 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, which appears Feb. 20 in the Proceedings of the National Academy of Sciences, 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. Traditional methods for analyzing longitudinal image datasets tend to require extensive customization and pre-processing.

For example, researchers studying the brain may take raw brain MRI data and pre-process the image data to focus on just one brain area, also correcting for different view angles, sizing differences and other artifacts in the data—all before performing the main analysis. The researchers designed LILAC to work much more flexibly, in effect automatically performing such corrections and finding relevant changes.This enables LILAC to be useful not just across different imaging contexts but also in situations where you aren’t sure what kind of change to expect,” said Dr. Kim, LILAC’s principal designer.

Source: https://tech.cornell.edu/news/ai-system-medical-images/