Abstract
Exposure of the lenses to direct ionizing radiation during computed tomography (CT) examinations predisposes patients to cataract formation and should be avoided when possible. Avoiding such exposure requires positioning and other maneuvers by technologists that can be challenging. Continuous feedback has been shown to sustain quality improvement and can remind and encourage technologists to comply with these methods. Previously, for use cases such as this, cumbersome manual techniques were required for such feedback. Modern deep learning methods utilizing convolutional neural networks (CNNs) can be used to develop models that can detect lenses in CT examinations. These models can then be used to facilitate automatic and continuous feedback to sustain technologist performance for this task, thus contributing to higher quality patient care. This continuous evaluation for quality purposes also surfaces other operational or process-based challenges that can be addressed. Given high-performance characteristics, these models could also be used for other tasks such as population health research.