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Nasal Brushing Sampling and Processing Using Digital High Speed Ciliary Videomicroscopy – Adaptation for the COVID-19 Pandemic

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COVID-Net L2C-ULTRA: An Explainable Linear-Convex Ultrasound Augmentation Learning Framework to Improve COVID-19

E Zhixuan Zeng¹, Ashkan Ebadi^1,2, Adrian Florea³

¹Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada.

Sensors (Basel, Switzerland)

|March 13, 2024

Summary

Artificial intelligence aids COVID-19 detection using point-of-care ultrasound (POCUS) by overcoming probe differences. This AI framework enhances diagnostic accuracy and recall for COVID-19 screening with POCUS images.

Keywords:

COVID-19 assessment deep explainable architecture linear–convex augmentation lung ultrasonic imaging

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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

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Published on: December 19, 2020

Area of Science:

Medical Imaging
Artificial Intelligence
Infectious Disease Diagnostics

Background:

Point-of-care ultrasound (POCUS) is crucial for COVID-19 assessment but faces interpretation challenges.
Heterogeneity in ultrasound probe types (linear vs. convex) hinders AI model development for COVID-19 screening.
Artificial intelligence (AI) offers a solution to support clinical decision-making in POCUS-based COVID-19 diagnostics.

Purpose of the Study:

To develop an AI framework, COVID-Net L2C-ULTRA, for COVID-19 assessment using heterogeneous POCUS images.
To investigate the impact of extended linear-convex ultrasound augmentation learning on deep neural network performance.
To enhance the utility of linear probe images in AI-driven COVID-19 detection.

Main Methods:

Proposed an analytic framework, COVID-Net L2C-ULTRA, designed for linear and convex probe ultrasound images.
Implemented extended linear-convex ultrasound augmentation learning, transforming linear probe data to resemble convex probe data.
Utilized a machine-driven design exploration strategy for an efficient deep columnar anti-aliased convolutional neural network.

Main Results:

Achieved significant performance gains: 3.9% increase in test accuracy, 3.2% in AUC, 10.9% in recall, and 4.4% in precision.
Demonstrated improved utilization of linear probe images, with a 5.1% recall improvement when added to the training set.
Showcased superior performance compared to other methods when trained on a combined linear-convex dataset.

Conclusions:

Extended linear-convex ultrasound augmentation learning effectively enhances AI performance for COVID-19 assessment via POCUS.
The proposed COVID-Net L2C-ULTRA framework successfully addresses probe heterogeneity, improving diagnostic accuracy.
The study validates the model's clinical relevance by identifying critical image regions for interpretation.