Developing artificial intelligence (AI) schemes to assist the clinician towards enabling precision medicine approaches requires development of objective markers that are predictive of disease response to treatment or prognostic of longer-term patient survival. The solutions being developed in my group in this regard involve designing computational imaging features together with histology or molecular data for detailed tissue and disease characterization in vivo as well be associated with patient outcomes. The key innovation in this approach lies in “handcrafting” unique tools that can capture biologically relevant and clinically intuitive measurements from routinely acquired imaging (MRI, CT, PET) or digitized images of tissue specimens. Further, by conducting cross-scale associations between imaging, pathology, and -omics, we can not only “unlock” and integrate the information captured by these different, disparate data modalities but also develop an interpretable and intuitive understanding of what drives their performance. Specific problems addressed via the new computerized imaging markers we have developed include: (a) predicting response to treatment to identify optimal therapeutic pathways, as well as (b) evaluating therapeutic response to guide follow-up procedures. We will further examine how to account for differences between sites, scanners, and acquisition parameters to ensure generalizable performance of AI tools and computational imaging features; crucial for wider clinical translation and widespread adoption. These will be discussed in the context of clinical applications in colorectal and renal cancers, digestive diseases, as well as pediatric conditions.
Dr. Satish E. Viswanath is an Associate Professor in the Department of Biomedical Engineering and co-Director of the Center for AI Enabling Discovery in Disease Biology (AID2B) at Case Western Reserve University; with secondary appointments in the departments of Radiology and Electrical, Computer & Systems Engineering. He is also a Research Scientist & Biomedical Engineer at the Cleveland VA Medical Center. He will be joining Emory and Georgia Tech this Fall 2026 as Associate Professor in Departments of Pediatrics and Biomedical Engineering.
The primary focus of his research has been developing new artificial intelligence, radiomics, and machine learning schemes, applied to problems in computer-aided diagnosis & detection, disease characterization, as well as quantitative evaluation of response to treatment, in gastrointestinal cancers and digestive diseases. He serves as Associate Editor for 4 leading journal publications, as Area Chair in 3 medical imaging conferences, and as leader of the Case Comprehensive Cancer Center Machine Learning Working Group. Notable in his list of awards is his selection for 2023 Fulbright Specialist Award and for 2023 SIIM Imaging Informatics Innovator Award, in addition to multiple best paper and poster awards at international conferences. He has been elected to Senior Member of the National Academy of Inventors, the IEEE society, and the SPIE society. His group's research has been continuously funded since 2016 through the DOD/CDMRP, the NIH (NCI, NHLBI, NINR, NIDDK), as well as the State of Ohio
Areas of expertise: artificial intelligence, medical image analysis, radiomics, pathomics, digital pathology, image filtering and texture, classifiers and pattern recognition, machine learning, multi-modal data fusion.
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