New self-supervised AI models scan X-rays to predict prognosis of COVID-19 patients
The researchers say it forecasts mortality more accurately than radiologists
Predicting COVID-19 deterioration
They used the pre-trained model to build classifiers that predict if a COVID-19 patient’s condition will likely worsen. They then fine-tuned the model with an extended version of the NYU COVID-19 dataset.
This smaller dataset set of around27,000 X-ray images from 5,000 patients was given labels indicating whether the patient’s condition deteriorated within 24, 48, 72, or 96 hours of the scan.
The team built one classifier that predicts patient deterioration based on a single X-ray. Another makes its forecasts using a sequence of X-rays, by aggregating the image features through a Transformer model. A third model estimates how much supplemental oxygen patients might need by analyzing one X-ray.
They say using a sequence of X-rays is particularly valuable, as they’re more accurate for long-term predictions. This approach also accounts for the evolution of infections over time.
Their study showed the models were effective atpredicting ICU needs, mortality forecasts, and overall adverse event predictions in the longer-term (up to 96 hours):
You can readthe study paperon the preprint server Axiv.org.
Story byThomas Macaulay
Thomas is a senior reporter at TNW. He covers European tech, with a focus on AI, cybersecurity, and government policy.Thomas is a senior reporter at TNW. He covers European tech, with a focus on AI, cybersecurity, and government policy.
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