Tyler Richards, MD
Assistant Professor of Neuroradiology
AI Director, Department of Radiology
University of Utah School of Medicine
Assistant Professor of Neuroradiology
AI Director, Department of Radiology
University of Utah School of Medicine
I am a clinical investigator focusing my research efforts on projects to leverage machine learning to improve neuroradiologist's efficiency and diagnostic accuracy in the reading room. Radiologist's workloads are increasing and imaging is becoming more complex and subspecialized, so it is difficult for radiologists to keep up with increasing demands. As a part of these efforts, I have worked in large imaging datasharing and annotation projects including being on the organizing commitee or co-chair of 3 AI challenges organized by the Radiological Society of North America including cervical spine fracture detection and lumbar degenerative disease detection and classification. I have experience in implementing and validating open source software for pre-surgical planning for patient with brain tumors and I am currently working on algorithm development for automatic scoliosis measurements on spine radiographs and predicting pathology of skull base tumors.
2006 - 2010
Oxford, Ohio
2010 - 2014
Cincinnati, Ohio
2014 - 2015
Pittsburgh, Pennsylvania
2015 - 2019
Cleveland, Ohio
2019-2021
Salt Lake City, Utah
2021 - current
Salt Lake City, Utah
Developing a machine learning algorithm pipeline for segmentation and classification of central skull base tumors to assist neurosurgical decision making and treatment
Research Team
Tolga Tasdizen PhD
Fnu Harshit
Developing automated measurement tools to decrease radiologist work burden and improve consistency of measurements to help guide spine surgery
Research Team
Maryam Soltanolkotabi MD
Mahdi Soltanolkotabi PhD (USC)
Co-chair for the RSNA AI challenge competition for researchers to develop models that can detect and classify degenerative spine conditions using lumbar spine MR images
Collaborative multi-institutional effort to develop and validate algorithms for detection and predicting need for surgical spinal fusion with colleagues from University of Toronto and UT Southwestern
Site lead for the the Federated Tumor Segmentation (FeTS) initiative, which is an on-going project to develop the largest international federation of healthcare institutions and an open-source toolkit with a user-friendly GUI, aiming at gaining knowledge for tumor boundary detection from ample and diverse patient populations without sharing any patient data.
Sample segmentation from Adewole M, Rudie JD, Gbdamosi A, et al. The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa). Preprint. ArXiv. 2023;arXiv:2305.19369v1. Published 2023 May 30.
Validated a pre-existing open-source algorithm TractSeg by automating the segmentation of the corticospinal tract in patients with brain tumors near the corticospinal tract
Research Team
Jeffrey Anderson MD PhD
Keri Anderson
Participating in the organizing committee and annotator for the RSNA AI challenge for cervical spine fracture detection and the subsequent multiple publications for evaluating the winning algorithms
Drop a message to Tyler.Richards@hsc.utah.edu