In an April 19 session on chest imaging, Dr. Melina Hosseiny of the University of California, San Diego described first how her group previously developed two single-task convolutional neural network (CNN) algorithms — one that detects pneumonia and another that estimates serum levels of NT-proBNP, a protein released in the blood due to pulmonary edema.

The algorithms were promising in clinical tests, but were beset with false-positive findings, as these conditions often overlap, she said.

“We were thinking maybe we need a new strategy to combine these models in a multi-task CNN that…