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

arXiv: https://arxiv.org/abs/2212.08624 · HTML: https://mlsanity.com/paper/2212.08624/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 16 references that are in the map.
- First posted on arXiv 16 Dec 2022.

## Builds on (most cited first, 9 of 9)

- [FairPrune: Achieving Fairness Through Pruning for Dermatological Disease Diagnosis](https://mlsanity.com/paper/2203.02110/) (2022)
- [ImageCHD: A 3D Computed Tomography Image Dataset for Classification of Congenital Heart Disease](https://mlsanity.com/paper/2101.10799/) (2021)
- [Machine Vision Guided 3D Medical Image Compression for Efficient Transmission and Accurate Segmentation in the Clouds](https://mlsanity.com/paper/1904.08487/) (2019)
- [ICA-UNet: ICA Inspired Statistical UNet for Real-time 3D Cardiac Cine MRI Segmentation](https://mlsanity.com/paper/2007.09455/) (2020)
- [MSU-Net: Multiscale Statistical U-Net for Real-time 3D Cardiac MRI Video Segmentation](https://mlsanity.com/paper/1909.06726/) (2019)
- [EchoCP: An Echocardiography Dataset in Contrast Transthoracic Echocardiography for Patent Foramen Ovale Diagnosis](https://mlsanity.com/paper/2105.08267/) (2021)
- [RT-DNAS: Real-time Constrained Differentiable Neural Architecture Search for 3D Cardiac Cine MRI Segmentation](https://mlsanity.com/paper/2206.04682/) (2022)
- [Segmentation with Multiple Acceptable Annotations: A Case Study of Myocardial Segmentation in Contrast Echocardiography](https://mlsanity.com/paper/2106.15597/) (2021)
- [Hardware-aware Real-time Myocardial Segmentation Quality Control in Contrast Echocardiography](https://mlsanity.com/paper/2109.06909/) (2021)

## Abstract (arXiv)

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.