Researchers at Rochester Institute of Technology developed new artificial intelligence techniques to extract and visualize information from standard-of-care biomedical data, providing a means for clinicians to better diagnose diseases and determine interventions. The new techniques could also improve image-guided therapies, including surgeries, and minimize invasive procedures because of these refined imaging details.

The future of medicine is not necessarily about acquiring more data but rather having access to effective tools to make use of the data, and this is where biomedical computing plays a critical role, said Cristian Linte, professor of biomedical engineering in RIT’s Kate Gleason College of Engineering. Imaging accounts for the majority of biomedical data has transformed diagnostic and interventional medicine from a subjective, perceptual skill based on physicians’ experience to an objective science driven by large-scale, heterogeneous data.

Biomedical visualization has evolved from anatomical drawings to a standard tool to aid diagnosis, plan treatment options, and monitor therapy. Before biomedical data can be visualized, the raw biomedical imaging data needs to be processed. Integration of artificial intelligence (AI) into medical image analysis has led to significant advances, but several challenges still exist, Linte said. 

AI models rely on large amounts of expert-annotated data for training, which requires time and expertise of clinicians to curate data. User variability also poses a significant barrier for accurate AI algorithm development. Internal operations and relevance of test data acquired for training of AI models are also not well understood, making predictions difficult to explain.

Source: https://www.rit.edu/news/biomedical-engineer-integrates-ai-techniques-improve-diagnostic-medicine