Project Summary / Abstract
Computer-integrated diagnostic and interventional data science encompasses the processing, analysis, and
visualization of images and signals, with the goal to better diagnose disease, better plan, deliver and monitor
therapy, and advance training and simulation for medical education. The emergence of multi-modality medical
imaging and its ability to “see” inside the human body has transformed disease diagnosis and enables less
invasive therapies.
Moreover, artificial intelligence (AI) has led to significant improvements in biomedical
image computing and modeling. However, the adoption of
large-scale data science techniques into diagnostic
and especially interventional workflows has been hampered by the limited availability of tools that can robustly
handle the size, diversity, and dimensionality of the that must be manipulated, often in real time. To mitigate
these barriers, my vision has been to establish and grow a multi-disciplinary research program that integrates
image computing, modeling, visualization, and validation to help elucidate biological complexity by developing
intelligent imaging informatics tools to interact with biomedical data. The program’s long-term goal has been
guided by the premise that effective utilization of biomedical informatics to develop versatile computing and
visualization tools will lead to solutions that enable more accurate and timely disease diagnosis and less
invasive therapies. Projects to date have focused on the development and validation of image computing,
modeling, and visualization techniques that 1) quantify and track imaging biomarkers to diagnose and monitor
disease progression, 2) identify and plan optimal therapeutic routes, and 3) guide, monitor, and deliver therapy
under less invasive conditions. These tools have been developed and demonstrated in the context of cardiac,
orthopedic, lung, brain, and spine applications, in collaborations with clinicians and industry partners. The
work heretofore builds on a decade of research in computer-assisted diagnosis and therapy and will help
answer the key questions in biomedical computing and visualization. We will research novel AI-based image
computing algorithms that robustly handle label noise, out of distribution detection, and class imbalance while
yielding high performance on limited training data; build and validate hybrid AI/physics-based computational
models for interactive biomedical simulation; research uncertainty modeling, propagation, and visualization
techniques; objectively validate the developed tools; and evaluate different display paradigms for information
presentation. This research will yield innovative imaging informatics tools that have the potential to advance
computer-integrated diagnosis and interventional data science by catering to a broad range of diseases, organ
systems, and minimally invasive therapy applications. We will make the developed techniques available to the
biomedical research community and to clinician scientists to promote their clinical translation to impact a
larger patient population. Lastly, this research program will continue to foster opportunities to train and
mentor skilled workforce for biomedical research from diverse backgrounds and education streams.
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