For the image-only DL model, we implemented a deep convolutional neural network (ResNet18 [13]) with PyTorch (version 0.31; pytorch.org). Given a 1664 × 2048 pixel view of a breast, the DL model was trained to predict whether or not that breast would develop breast cancer within 5 years.

The only "innovation" here is feeding full view mammograms to a ResNet18(2016 model). The traditional risk factors regression is nothing special (barely machine learning). They don't go in depth about how they combine the two for the hybrid model, ~~so it's probably safe to assume it is something simple (merely combining the results, so nothing special in the training step).~~ edit: I stand corrected, commenter below pointed out the appendix, and the regression does in fact come into play in the training step

As a different commenter mentioned, the data collection is largely the interesting part here.

I'll admit I was wrong about my first guess as to the network topology used though, I was thinking they used something like auto encoders (but that is mostly used in cases where examples of bad samples are rare)

I skimmed the paper. As you said, they made a ML model that takes images and traditional risk factors (TCv8).

I would love to see comparison against risk factors + human image evaluation.

Nevertheless, this is the AI that will really help humanity.