A new AI-based system, LILAC, can accurately analyze time-series images and predict outcomes, opening up possibilities in medical and scientific research.
A groundbreaking AI-based system, termed LILAC (Learning-based Inference of Longitudinal imAge Changes), has demonstrated remarkable accuracy in detecting changes and predicting outcomes within time-series image datasets. Developed by a team of researchers from Weill Cornell Medicine, Cornell's Ithaca campus, and Cornell Tech, LILAC leverages machine learning principles to analyze longitudinal image sequences, such as those capturing developing IVF embryos, healing tissues, and aging brains.
The system's key advantage lies in its flexibility and sensitivity. Unlike traditional methods that often require extensive customization and preprocessing, LILAC can automatically perform necessary corrections and identify relevant changes across various imaging contexts. This adaptability makes it applicable to a wide range of scenarios, even in situations where the specific type of change is unknown. In a series of proof-of-concept demonstrations, LILAC showcased its capabilities in diverse applications. It accurately determined the temporal order of images within sequences of developing IVF embryos, identified subtle differences in healing tissue, and predicted cognitive scores from brain scans of individuals with mild cognitive impairment. Researchers believe LILAC's ability to highlight image features crucial for detecting changes and group-level differences holds immense potential for advancing both clinical and scientific understanding. The team is currently exploring its application in predicting treatment responses in prostate cancer patients using MRI scans
AI Machine Learning Image Analysis Longitudinal Data Medical Imaging
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