# Visual-search ideal observers for modeling reader variability > **NIH NIH R01** · UNIVERSITY OF HOUSTON · 2022 · $577,298 ## Abstract Project Summary/Abstract The goal of this project is to develop novel methods for predicting human decisions with diagnostic images. Expected project outcomes include new insights into sources of radiologist variability and advanced tools to accelerate imaging trials in clinical research. Such trials with expert readers and known-truth cases are an accepted but burdensome gold standard for evaluating imaging technology. The necessary trial resources are not available to many clinical researchers. Virtual trials with sur- rogate model observers have been proposed, but important limitations, including primarily correlative estimates and persistent model reliance on human data for training, prevent their widespread adop- tion. Quantitative models with minimal dependence on human input will substantially improve clinical access to advanced imaging technology. Our approach to develop such “low-resource” models will explore reader variability in target detection and estimation tasks. Ideal observers (IOs) derived from gist-processing and extreme-value theories will be the starting point. These IOs are optimal for de- cision processes that maximize over sets of extracted feature values, a common premise for tasks involving visual search. The result will be adaptive observer models that produce tighter bounds on human performance compared to existing models. These new models will test if reader variability can be attributed to candidate pooling and cognitive threshold mechanisms that define image struc- ture of interest. Analytic figures of merit for diagnostic visual-search tasks will be developed. We will test model generalizability across radiological modalities, tasks, imaging models (e.g., simula- tion/patient data), and reader classes (lay/clinician), all of relevance for researchers. The tasks will include location-known, localization, and joint detection-estimation formats. The joint task compels more precise information extraction than target detection alone; we hypothesize that detection perfor- mance correlates with estimation skill, with the latter helping to resolve structure. We shall leverage our findings to devise multireader virtual trial protocols for improved statistical rigor. Enhanced stochastic target modeling for studies with 2D and 3D images will be supporting aims. The IO will also allow examination of nonlinear behaviors for individual readers. The project studies relate to dose reduction and reconstruction methods for x-ray and nuclear medicine modalities, but the methods can apply more generally. By accelerating the clinical adoption of advanced imaging technology, our model observers will have a direct and widespread impact on clinical operations and patient care. ## Key facts - **NIH application ID:** 10530899 - **Project number:** 1R01EB032416-01A1 - **Recipient organization:** UNIVERSITY OF HOUSTON - **Principal Investigator:** Howard Carl Gifford - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2022 - **Award amount:** $577,298 - **Award type:** 1 - **Project period:** 2022-08-01 → 2028-04-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10530899 ## Citation > US National Institutes of Health, RePORTER application 10530899, Visual-search ideal observers for modeling reader variability (1R01EB032416-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10530899. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*