Development of A Real-time POCUS Image Quality Assessment and Acquisition Guidance System
This addresses the problem of high inter-observer variability and training inefficiencies in POCUS for novice learners in clinical settings like emergency departments and pediatric intensive care units, but it appears incremental as it builds on prior AI-assisted guidance methods.
The paper tackles the challenge of training novice learners to acquire diagnostic-quality point-of-care ultrasound (POCUS) images by developing a real-time AI-assisted system for quality assessment and probe position guidance, aiming to expedite training and reduce dependency on experienced sonographers.
Point-of-care ultrasound (POCUS) is one of the most commonly applied tools for cardiac function imaging in the clinical routine of the emergency department and pediatric intensive care unit. The prior studies demonstrate that AI-assisted software can guide nurses or novices without prior sonography experience to acquire POCUS by recognizing the interest region, assessing image quality, and providing instructions. However, these AI algorithms cannot simply replace the role of skilled sonographers in acquiring diagnostic-quality POCUS. Unlike chest X-ray, CT, and MRI, which have standardized imaging protocols, POCUS can be acquired with high inter-observer variability. Though being with variability, they are usually all clinically acceptable and interpretable. In challenging clinical environments, sonographers employ novel heuristics to acquire POCUS in complex scenarios. To help novice learners to expedite the training process while reducing the dependency on experienced sonographers in the curriculum implementation, We will develop a framework to perform real-time AI-assisted quality assessment and probe position guidance to provide training process for novice learners with less manual intervention.