Tuesday, April 23, 2024
4/23/2024
PROGRAM SUMMARY Radiological imaging is often the first step of the diagnostic pathway for many devastating diseases; thus, an erroneous assessment of “normal” can lead to death. Whereas a grayscale object in an image can be described by its first-order image statistics—such as contrast, spatial frequency, position, entropy, and orientation—none of these dimensions, by itself, indicates abnormal vs normal radiological findings. We are a highly diverse team proposing an empirical approach to determine the mixtures of the first-order statistics—the “visual textures”— that radiology experts explicitly and implicitly use to identify the locations of potential abnormalities in medical images. Our innovative approach does not rely on assumptions about which textures may or may not be im-portant to abnormality detection. Instead, we will track the oculomotor behavior of expert radiologists to deter-mine their conscious and unconscious targeting choices, and thus ascertain which textures are empirically in-formative. The ability of expert radiologists to rapidly find abnormalities suggests that they may be able to first identify them in their retinal periphery. Peripheral visual analysis skills are therefore potentially critical to radio-logic performance, despite being understudied. We will measure these skills and leverage the results to develop perceptual learning heuristics to improve peripheral abnormality texture detection. By comparing novices to ex-perts we will determine whether the first are inexpert due to a lack of sensitivity to diagnostically relevant textures (texture informativeness), or to a lack of knowledge about which textures are abnormal, or to a combined lack of both sensitivity and knowledge. Radiology also requires the acquisition of oculomotor skills through practice and optimization. Radiologic expertise thus changes the oculomotor system in predictable and detectable ways, in much the same way that an athlete’s body and brain change as a function of expertise acquisition in their sport. We will therefore analyze both the consistency between experts’ fixation choices in medical images, and the eye movement performance characteristics of experts vs novice radiologists, to create an objective oculomotor bi-omarker of radiological expertise. The differences between novices and experts will train a deep learning (DL) system, which will have human visual and oculomotor performance characteristics. Training the DL with the abnormalities identified by a panel of expert radiologists will allow it to pinpoint the possible solutions in the manner of a simulated human radiologist performing at peak accuracy, precision, and speed. The resulting rank-ordered list of possible optimal and suboptimal image-reading strategies will serve as a benchmarking tool to quantify the performance of actual clinicians and residents who read the same images, rested vs fatigued. Meas-uring the effects of both training and fatigue on radiology expertise will be a major interdisciplinary cross-cutting advance in performance assessment. Our proposal to quantify fatigue in terms of erosion of expertise represents a transformational advance towards objective fitness-for-duty and expertise measures in medicine and beyond.
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