Biomedical engineer integrates AI techniques to improve diagnostic medicine
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.” Computer-integrated diagnosis and therapy is an emerging field dedicated to improving disease detection and treatment. Linte and members of his research team, including Imaging science doctoral students Bipasha Kundu, Bidur Khanal, Zixin Yang, Nakul Poudel, and Richard Simon, detailed results of this work in several publications, including the April 2025 proceedings of SPIE Medical Imaging 2025. 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. “Many physics-based biomedical models are hampered by their computational expense, which constitutes a major setback to clinical adoption, limiting their use as interactive simulation tools for therapy planning or monitoring,” Linte said. “AI techniques, on the other hand, can learn from large patient-specific datasets, so combining data science with physics-based models has the potential to yield more accurate and more computationally efficient simulations.” Researchers in Linte’s lab have effectively combined biomedical imaging, computing, modeling, and visualization for computer-integrated diagnosis and therapy. They contributed to the development and validation of robust AI computational imaging informatics tools to advance computer-integrated diagnosis and interventional data science by addressing a broad range of diseases, organ systems, and minimally invasive therapy applications. “We believe that effective utilization of biomedical informatics to develop versatile biomedical computing and visualization tools will lead to solutions that enable more accurate and timely disease diagnosis and less invasive therapies. These tools will help lay a foundation for advances in computer-aided diagnosis and therapy across a wide spectrum of diseases and organ systems that can impact a larger patient population,” said Linte, who has a background in mechanical and biomedical engineering as well as imagining science. He teaches in RIT’s engineering college as well as the Chester F. Carlson Center, specifically in the areas of biomechanics and biomedical thermo-fluids and conducts research at the intersection of biomedical imaging, computing, and visualization. Research in Linte’s Biomedical Imaging, Modeling, Visualization and Image-guided Navigation Laboratory is supported by grants from the National Institutes of Health (NIH) and the National Science Foundation. Its research focus remains on biomedical artificial intelligence tools for diagnostic and intervention data science. Most recently, he was awarded nearly $2.4 million by the NIH for a five-year competitive renewal of the research grant on Biomedical Computing and Visualization Tools for Computer-integrated Diagnostic and Therapeutic Data Science to support innovation, training, and mentorship of graduate and undergraduate students in the lab, many of whom have gone on to serve in prestigious national labs, hospitals, and research organizations. “Mentoring and training high caliber students who will join tomorrow’s biomedical and academic workforce constitutes by far the greatest impact of our careers as academics and scientists and we’re thrilled to see them succeed,” said Linte.
