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Point-of-care ultrasound (POCUS) is one of the most commonly applied toolsfor cardiac function imaging in the clinical routine of the emergencydepartment and pediatric intensive care unit. The prior studies demonstratethat AI-assisted software can guide nurses or novices without prior sonographyexperience to acquire POCUS by recognizing the interest region, assessing imagequality, and providing instructions. However, these AI algorithms cannot simplyreplace 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 withvariability, they are usually all clinically acceptable and interpretable. Inchallenging clinical environments, sonographers employ novel heuristics toacquire POCUS in complex scenarios. To help novice learners to expedite thetraining process while reducing the dependency on experienced sonographers inthe curriculum implementation, We will develop a framework to perform real-timeAI-assisted quality assessment and probe position guidance to provide trainingprocess for novice learners with less manual intervention.

Development of A Real-time POCUS Image Quality Assessment and Acquisition Guidance System