Artificial intelligence can spot COVID-19 in lung ultrasound images much like facial recognition software can spot a face in a crowd, new research shows.
The findings boost AI-driven medical diagnostics and bring health care professionals closer to being able to quickly diagnose patients with COVID-19 and other pulmonary diseases with algorithms that comb through ultrasound images to identify signs of disease.
The tool also holds potential for developing wearables that track such illnesses as congestive heart failure, which can lead to fluid overload in patients' lungs, not unlike COVID-19, said co-author Tiffany Fong, an assistant professor of emergency medicine at Johns Hopkins Medicine. "We had to model the physics of ultrasound and acoustic wave propagation well enough in order to get believable simulated images," Bell said."Then we had to take it a step further to train our computer models to use these simulated data to reliably interpret real scans from patients with affected lungs."
"Early in the pandemic, we didn't have enough ultrasound images of COVID-19 patients to develop and test our algorithms, and as a result our deep neural networks never reached peak performance," said first author Lingyi Zhao, who developed the software while a postdoctoral fellow in Bell's lab and is now working at Novateur Research Solutions.
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