Quick Takeaways
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Introduction of LILAC: A new AI-based system, LILAC (Learning-based Inference of Longitudinal imAge Changes), developed by researchers at Weill Cornell Medicine and Cornell University, can accurately analyze time-series images to detect changes and predict outcomes across various medical fields.
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Flexibility and Sensitivity: LILAC autonomously corrects for common data artifacts and excels at identifying subtle differences in images taken over time, allowing it to handle diverse longitudinal imaging datasets without extensive customization.
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High Accuracy in Applications: In tests, LILAC achieved about 99% accuracy in determining the chronological order of IVF embryo images and effectively detected healing rates and cognitive scores from MRIs, significantly outperforming traditional methods.
- Future Research and Impact: The system is anticipated to provide valuable insights in areas with high variability and unknown processes, with future plans for real-world applications, including predicting treatment responses in prostate cancer patients.
New AI System Revolutionizes Medical Image Analysis
A groundbreaking AI system offers new capabilities for analyzing medical images taken over time. This innovative technology, developed by researchers from Weill Cornell Medicine, Cornell’s Ithaca campus, and Cornell Tech, could transform various fields in healthcare and science. The system is called LILAC, which stands for Learning-based Inference of Longitudinal imAge Changes.
LILAC leverages machine learning, a method that enables the system to learn from vast amounts of data. Researchers published their findings in the Proceedings of the National Academy of Sciences on Feb. 20. They showcased LILAC’s versatility by applying it to several different types of longitudinal images. These included developing IVF embryos, healing tissue post-injury, and aging brains.
The researchers found that LILAC excels in identifying even minor differences between images captured at different times. Furthermore, the system can predict outcomes, such as cognitive scores from MRI scans, with high accuracy. “This new tool will allow us to detect and quantify clinically relevant changes over time in ways that weren’t possible before,” said Dr. Mert Sabuncu, the study’s senior author. He noted the system’s flexibility allows it to be used across various medical and scientific contexts.
Traditionally, analyzing longitudinal image datasets requires significant customization and pre-processing. For instance, brain researchers often have to manually pre-process MRI data to focus on specific areas. This can involve correcting for differences in view angles and resizing images, consuming valuable time and resources. In contrast, LILAC automatically performs these corrections, streamlining the analysis process.
In a proof-of-concept study, the researchers tested LILAC using hundreds of sequences of microscope images of developing embryos. Remarkably, LILAC classified which images were taken earlier with 99% accuracy. The system also demonstrated its prowess by successfully identifying differences in healing rates between untreated tissues and those receiving experimental treatments.
Moreover, LILAC could accurately predict time intervals between MRI images of healthy older adults’ brains and cognitive scores from patients with mild cognitive impairment. These impressive results suggest that LILAC can adapt to highlight key image features, providing valuable insights into individual and group differences.
Dr. Sabuncu believes LILAC will be particularly beneficial in situations where researchers lack prior knowledge about the changes being studied. The team plans to apply LILAC in real-world settings, such as predicting treatment responses from MRI scans of prostate cancer patients. This ongoing work promises to enhance the future of medical image analysis, potentially improving patient outcomes across various conditions.
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