A research team from the University of California, San Diego (UCSD) used chest radiographs annotated by their radiologists to perform additional training of a previously developed convolutional neural network (CNN) for localizing and quantifying severity of COVID-19 pneumonia on chest x-rays. The group found that this radiologist-supervised transfer learning approach significantly boosted the model’s detection accuracy.

What’s more, the algorithm’s quantitative analysis of lung involvement correlated well with a semiquantitative method for scoring the extent and degree of lung opacities…