Point of Care Image Analysis for COVID-19

TL;DR

This paper develops deep learning models to improve COVID-19 detection and severity assessment using chest X-rays and lung ultrasound, providing a fast, cost-effective point-of-care diagnostic tool.

Contribution

It introduces neural network models trained on large datasets of CXRs and LUS for COVID-19 detection and severity grading, enhancing point-of-care diagnostics.

Findings

Achieved over 90% detection accuracy for COVID-19 in CXRs.

Developed models for automatic severity grading of lung ultrasound.

Demonstrated feasibility of AI-based point-of-care COVID-19 assessment.

Abstract

Early detection of COVID-19 is key in containing the pandemic. Disease detection and evaluation based on imaging is fast and cheap and therefore plays an important role in COVID-19 handling. COVID-19 is easier to detect in chest CT, however, it is expensive, non-portable, and difficult to disinfect, making it unfit as a point-of-care (POC) modality. On the other hand, chest X-ray (CXR) and lung ultrasound (LUS) are widely used, yet, COVID-19 findings in these modalities are not always very clear. Here we train deep neural networks to significantly enhance the capability to detect, grade and monitor COVID-19 patients using CXRs and LUS. Collaborating with several hospitals in Israel we collect a large dataset of CXRs and use this dataset to train a neural network obtaining above 90% detection rate for COVID-19. In addition, in collaboration with ULTRa (Ultrasound Laboratory Trento, Italy) and hospitals in Italy we obtained POC ultrasound data with annotations of the severity of disease and trained a deep network for automatic severity grading.