The COVID-19 pandemic dealt a huge blow to health care systems and highlighted their major shortcomings. 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 (ICU) 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.
One way to mitigate such problems is to accurately predict the prognosis of patients who test positive for COVID-19. Doctors generally use chest X-ray radiography images to assess the condition of patients. By analyzing signs of pneumonia in these images, they can infer whether the patient is likely to need admission to the ICU soon. In turn, this can help with optimal allocation of hospital resources.
The idea is to fine-tune the receiving model using a dedicated data set so that the"expertise" of the previous model can be leveraged for a new task. For example, with transfer learning, a model trained to detect a specific disease in magnetic resonance images can serve as the basis for another model aimed at detecting a different disease.
Utilizing this strategy, the researchers employed a sequential transfer learning process to develop their final model. They first fine-tuned a large model, pre-trained on ImageNet with 1.2 million natural images, using CXR images from a National Institutes of Health data set to detect 14 different diseases. After that, the researchers refined this model using a data set from the Radiological Society of North America to detect pneumonia.
The developed AI model could predict with a good degree of accuracy whether a patient with COVID-19 would need intensive care within 24, 48, 72, and 96 hours of the CXR exam. In an independent in-house test set , it achieved an area under the receiver operating characteristic curve of 0.78 when predicting the need for intensive care 24 hours in advance, and at least 0.
Belgique Dernières Nouvelles, Belgique Actualités
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