Pallavi Tiwari, Ph.D., at the Case Comprehensive Cancer Center is working to address this treatment challenge by using machine learning and artificial intelligence to create computational maps of the brain that can help doctors tell the difference between tumor cells and benign radiation effects on MRI scans.
Kayhan Batmanghelich, PhD, from the Department of Biomedical Informatics, University of Pittsburgh, will be presenting, “Incorporating Medical Insight into Machine Learning Algorithms for Learning, Inference, and Model Explanation”.
Most people probably underestimate how much our sense of touch helps us navigate the world around us. New research has made it crystal clear after a robotic arm with the ability to feel was able to halve the time it took for the user to complete tasks.
In the Crain’s Cleveland Business article, “CWRU, University Hospitals are part of a $3 million grant project on lung cancer, immunotherapy”, writer Rachel Abbey McCafferty highlights CCIPD’s role in this research, which will develop AI tools to help predict response to immunotherapy for lung cancer patients.
Under the supervision of Dr. Pallavi Tiwari, Niha’s PhD research focused on developing assistive diagnostic tools in the field of neuro-oncology using data science and machine learning techniques.
Dr. Anant Madabhushi predicts, “2021 will also likely see more innovation in non-black-box (interpretable AI) technologies in order to make them more amenable for use by clinicians and physicians.”
Scientists, medical researchers at Case Western Reserve, NYU Langone Health and University Hospitals using machine-learning to predict response to immunotherapy
In the last month, two patents were awarded to inventors from the Center for Computational Imaging and Personalized Diagnostics (CCIPD): “Predicting recurrence in early stage non-small cell lung cancer (NSCLC) using spatial arrangement of clusters of tumor infiltrating lymphocytes and cancer nuclei”, and “Hough transform-based vascular network disorder features on baseline fluorescein angiography scans predict response to anti-VEGF therapy in diabetic macular edema”.
On April 7th, 2021, Anant Madabushi, PhD, gave the talk, "Prognostic and Predictive Radiomics and Pathomics: Implications for Precision Medicine", at the University of Washington’s Pathology Grand Rounds.
“Predicting response to anti-vascular endothelial growth factor therapy with computer-extracted morphology and spatial arrangement features of leakage patterns on baseline fluorescein angiography in diabetic macular edema” and “Methods and apparatus for predicting benefit from immunotherapy using tumoral and peritumoral radiomic features”
Please join us for a virtual Machine Learning Working Group Meeting on April 5, from 11 am-12 pm EST. Dr. Pingkun Yan, Assistant Professor in the Department of Biomedical Engineering at Rensselaer Polytechnic Institute (RPI) will present “Deep learning Predicts Risks from Chest CT”.
Anant Madabhushi, PhD, is quoted in the article for his insight on the potential impacts AI can have on the optimization of both diagnostic and prognostic clinical decisions.
In a BrainX Talks podcast episode, Dr. Anant Madabhushi discusses CCIPD’s unique approach to globally scalable AI, aspects of social justice while applying AI in healthcare, setting up fruitful partnerships with clinicians, and achieving commercial and academic success.
Members of the Center for Computational Imaging and Personalized Diagnostics took first place with their presentation of RadxTools at the Technical Workshop: Computer-Aided Diagnosis of the Society of Photo-optical Instrumentation Engineers (SPIE) Medical Imaging conference on February 17th.
