Downstate Researchers Win Scientific Paper of the Year Award from International News Site
By Office of the President | Nov 8, 2022
Congratulations to the Downstate team of researchers whose paper, “Mandating Limits on Workload, Duty, and Speed in Radiology,” was named Scientific Paper of the Year by the Radiology news site AuntMinnie.com, a comprehensive community internet site for radiologists and related professionals in the medical imaging industry.
The team included first authors Robert Alexander, Ph.D., Research Assistant Professor of Ophthalmology, and Stephen Waite, M.D., Clinical Associate Professor of Radiology, and final authors Stephen L. Macknik, Ph.D., and Susana Martinez-Conde, Ph.D., both professors of Ophthalmology, Neurology, and Physiology & Pharmacology.
The study was published in June in Radiology, the premier radiology publication. It examines the relationship between interpretation speed and error rates in radiology, the potential influence of artificial intelligence on reading speed and error rates, and the possible outcomes of imposed limits on both caseload and duty hours.
The “Minnie” awards are hosted by the primary international radiology news site, AuntMinnie.com, which recognizes the best and brightest in medical imaging. Nominations included over 200 candidates in 14 categories from over 52,000 eligible publications in the field of radiology, ranging from Most Influential Radiology Researcher to Best Educational Mobile App. Downstate‘s study was selected independently by the committee and won against 17 finalists for “Best Paper of the Year.” Finalists are selected and voted upon by an expert panel of radiology luminaries.
The paper builds on research conducted at Downstate as part of a multi-year, $2.8M NIH-funded study, “Novel Perceptual and Oculomotor Heuristics for Enhancing Radiologic Performance,” led by Drs. Martinez-Conde, Macknik, and Waite as Multi-P.I.s. and Dr. Alexander as a co-investigator.
Downstate researchers examined how to reduce detection errors in radiology through four innovative pillars: determining the visual textures used by expert radiologists to identify abnormalities within medical images; determining how expert radiologists use their eyes, especially their peripheral vision, to scan images; developing a perception-learning paradigm to train residents optimally, and, constructing a deep learning model using simulated human visual and oculomotor capabilities to create a normative model of human radiological expertise.
Additional contributors to this work included well-known experts on radiology errors Michael A. Bruno, M.D., FACR, Penn State Milton S. Hershey Medical Center Department of Radiology; Elizabeth A. Krupinski, Ph.D., Emory University Department of Radiology and Imaging Sciences; and Leonard Berlin, M.D., FACR, Rush University Medical College Department of Radiology, and the University of Illinois.
Tags: Radiology