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

TL;DR

This paper presents a real-time AI system designed to assess image quality and guide probe positioning in POCUS, aiming to improve training efficiency for novices in clinical settings.

Contribution

It introduces a novel framework for real-time AI-assisted quality assessment and guidance in POCUS to enhance novice training and reduce reliance on experienced sonographers.

Findings

Developed a real-time AI system for POCUS quality assessment.

Demonstrated improved training efficiency for novices.

Reduced dependency on expert sonographers.

Abstract

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.