> Current clinical cardiac image analysis extracts only simple scar features like volume and mass, severely underutilizing what's demonstrated in this work to be critical data.
> "The images carry critical information that doctors haven't been able to access," said first author Dan Popescu, a former Johns Hopkins doctoral student.
This is, like, the thing that machine learning is really good at--pulling out additional information from complex patterns represented as imagery. It's a very similar reasoning to why neural networks can be really great at classifying malware based on the program structure or doing semantic segmentation on well-defined scenes.
> The team trained a second neural network to learn from 10 years of standard clinical patient data, 22 factors such as patients' age, weight, race, and prescription drug use.
Again, this kind of multi-dimensional pattern-matching is pretty much much a machine learning wheelhouse.
> [They] were validated in tests with an independent patient cohort from 60 health centers across the United States, with different cardiac histories and different imaging data
And apparently has a bunch of independent validation. Neat stuff, I'm curious to see how it will develop/be distributed in the future.
Disclosure: I recently accepted a job offer from JHU, albeit in a different field.