# Ultrasound-Based AI for COVID-19 Detection: A Comprehensive Review of Public and Private Lung Ultrasound Datasets and Studies

arXiv: https://arxiv.org/abs/2411.05029 · HTML: https://mlsanity.com/paper/2411.05029/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 101 references that are in the map.
- First posted on arXiv 6 Nov 2024.

## Builds on (most cited first, 17 of 17)

- [Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review](https://mlsanity.com/paper/2301.03505/) (2023)
- [POCOVID-Net: Automatic Detection of COVID-19 From a New Lung Ultrasound Imaging Dataset (POCUS)](https://mlsanity.com/paper/2004.12084/) (2020)
- [COVIDx-US -- An open-access benchmark dataset of ultrasound imaging data for AI-driven COVID-19 analytics](https://mlsanity.com/paper/2103.10003/) (2021)
- [POCFormer: A Lightweight Transformer Architecture for Detection of COVID-19 Using Point of Care Ultrasound](https://mlsanity.com/paper/2105.09913/) (2021)
- [Computer Vision For COVID-19 Control: A Survey](https://mlsanity.com/paper/2004.09420/) (2020)
- [Semi-Supervised Active Learning for COVID-19 Lung Ultrasound Multi-symptom Classification](https://mlsanity.com/paper/2009.05436/) (2020)
- [Dense Pixel-Labeling for Reverse-Transfer and Diagnostic Learning on Lung Ultrasound for COVID-19 and Pneumonia Detection](https://mlsanity.com/paper/2201.10166/) (2022)
- [Accelerating COVID-19 Differential Diagnosis with Explainable Ultrasound Image Analysis](https://mlsanity.com/paper/2009.06116/) (2020)
- [Diagnosis of COVID-19 Using Machine Learning and Deep Learning: A review](https://mlsanity.com/paper/2110.14910/) (2021)
- [COVID-Net USPro: An Open-Source Explainable Few-Shot Deep Prototypical Network to Monitor and Detect COVID-19 Infection from Point-of-Care Ultrasound Images](https://mlsanity.com/paper/2301.01679/) (2023)
- [Multi-Feature Multi-Scale CNN-Derived COVID-19 Classification from Lung Ultrasound Data](https://mlsanity.com/paper/2102.11942/) (2021)
- [Adaptive Few-Shot Learning PoC Ultrasound COVID-19 Diagnostic System](https://mlsanity.com/paper/2109.03793/) (2021)
- [Detection of COVID-19 Disease using Deep Neural Networks with Ultrasound Imaging](https://mlsanity.com/paper/2104.01509/) (2021)
- [An Approach Towards Physics Informed Lung Ultrasound Image Scoring Neural Network for Diagnostic Assistance in COVID-19](https://mlsanity.com/paper/2106.06980/) (2021)
- [A semi-automatic ultrasound image analysis system for the grading diagnosis of COVID-19 pneumonia](https://mlsanity.com/paper/2111.02676/) (2021)
- [COVID-Net US-X: Enhanced Deep Neural Network for Detection of COVID-19 Patient Cases from Convex Ultrasound Imaging Through Extended Linear-Convex Ultrasound Augmentation Learning](https://mlsanity.com/paper/2204.13851/) (2022)
- [COVID-Net UV: An End-to-End Spatio-Temporal Deep Neural Network Architecture for Automated Diagnosis of COVID-19 Infection from Ultrasound Videos](https://mlsanity.com/paper/2205.08932/) (2022)

## Abstract (arXiv)

The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings for detecting COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as a low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on AI-driven studies utilizing lung ultrasound (LUS) for COVID-19 detection and analysis. We provide a detailed overview of both publicly available and private LUS datasets and categorize the AI studies according to the dataset they used. Additionally, we systematically analyzed and tabulated the studies across various dimensions, including data preprocessing methods, AI models, cross-validation techniques, and evaluation metrics. In total, we reviewed 60 articles, 41 of which utilized public datasets, while the remaining employed private data. Our findings suggest that ultrasound-based AI studies for COVID-19 detection have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field.