The COVID-19 pandemic dealt a huge blow to healthcare systems and highlighted their major shortcomings.
Reviewed by Danielle Ellis, B.Sc.Aug 21 2023 As of June 2023, there have been over 760 million confirmed cases of COVID-19, with almost 7 million deaths worldwide. During the major COVID-19 outbreaks, hospitals often had their intensive care units running at full capacity for providing invasive mechanical ventilation to patients who were diagnosed as positive for COVID-19. These ICUs often operated with insufficient staff and intubation equipment.
But what if artificial intelligence gave us a helping hand? In a recent study published in the Journal of Medical Imaging, a team of researchers from the Department of Radiology of the University of Chicago has developed a deep learning-based model that can predict if a patient will need intensive care by analyzing their CXR images.
Utilizing this strategy, the researchers employed a sequential transfer learning process to develop their final model. They first fine-tuned a large model, pretrained on ImageNet with 1.2 million natural images, using CXR images from a National Institutes of Health dataset to detect 14 different diseases. After that, the researchers refined this model using a dataset from the Radiological Society of North America to detect pneumonia.
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