covEcho Resource constrained lung ultrasound image analysis tool for faster triaging and active learning

TL;DR

This paper introduces a lightweight, real-time lung ultrasound analysis tool based on YOLO for faster COVID-19 triaging in resource-limited settings, capable of landmark detection, severity prediction, and active learning.

Contribution

It presents a novel active learning-based approach using a lightweight YOLO model for real-time lung ultrasound analysis in resource-constrained environments.

Findings

Achieved 66% mAP for landmark detection

Runs at 123 FPS on a Quadro P4000 GPU

Supports active learning and image quality assessment

Abstract

Lung ultrasound (LUS) is possibly the only medical imaging modality which could be used for continuous and periodic monitoring of the lung. This is extremely useful in tracking the lung manifestations either during the onset of lung infection or to track the effect of vaccination on lung as in pandemics such as COVID-19. There have been many attempts in automating the classification of severity of lung into various classes or automatic segmentation of various LUS landmarks and manifestations. However, all these approaches are based on training static machine learning models which require a significantly clinically annotated large dataset and are computationally heavy and most of the time non-real time. In this work, a real-time light weight active learning-based approach is presented for faster triaging in COVID-19 subjects in resource constrained settings. The tool, based on the you look only once (YOLO) network, has the capability of providing the quality of images based on the identification of various LUS landmarks, artefacts and manifestations, prediction of severity of lung infection, possibility of active learning based on the feedback from clinicians or on the image quality and a summarization of the significant frames which are having high severity of infection and high image quality for further analysis. The results show that the proposed tool has a mean average precision (mAP) of 66% at an Intersection over Union (IoU) threshold of 0.5 for the prediction of LUS landmarks. The 14MB lightweight YOLOv5s network achieves 123 FPS while running in a Quadro P4000 GPU. The tool is available for usage and analysis upon request from the authors.