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Sam Payabvash, MD

Assistant Professor of Radiology and Biomedical Imaging

Contact Information

Sam Payabvash, MD

Mailing Address

  • Neuroradiology

    789 Howard Ave

    New Haven, CT 06519

    United States

Payabvash Lab
Yale Medicine

Patient Care

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Research Summary

Most of my research has been focused on application of advanced imaging techniques and development of novel neuroimaging-based models for outcome prediction and treatment triage in stroke patients. However, the scope of my research projects have been expanded to apply radiomics, bioimage texture analysis, machine learning classifiers, and deep learning for development of innovative neuroimaging diagnostic tools. Many of these tools have been successfully helped with prognostication of cerebrovascular disease, identification of children with neurodevelopmental disorders, and differentiation of brain and neck tumors. The mainstay of projects is to combine advanced neuroimaging statistics, machine learning models, and outcome research to devise cutting-edge predictive tools, and provide personalized treatment options for patients.

Extensive Research Description

Stroke imaging: Devising prognostic model for stroke patients based on the location of brain parenchymal damage on initial scans. Application of deep neural networks for identification of acute infarct on head CT, localization of arterial occlusion, and grading of arterial collaterals on admission CT angiography. Outcome prediction based on imaging patterns of hemorrhagic stroke.

Head and neck cancers: Applying texture analysis, radiomics and machine-learning models for differentiation of neoplasms, prediction of molecular subtypes, and prognostication beyond current staging schemes based on CT, MRI, and PET scans in patients with tumors of brain and neck.

Brain connectivity in neurodevelopmental disorders: Examining the functional and microstructural connectivity of the brain in children at risk of autism and neurodevelopmental disorders based on diffusion tensor imaging and tractography. Using machine learning models to devise bioimaging biomarkers for neurodevelopmental disease based on quantitative metrics of brain connectivity.

Coauthors

Research Interests

Artificial Intelligence; Brain Diseases; Brain Neoplasms; Neurology; Neurosciences; Neurosurgery; Brain Hemorrhage, Traumatic; Stroke; Diffusion Magnetic Resonance Imaging; Neuroimaging; Autism Spectrum Disorder; Machine Learning; Neurodevelopmental Disorders

Public Health Interests

Cancer; Cardiovascular Diseases; Perinatal/Prenatal Health

Selected Publications

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