> Essentially all of the research, clinical and computer science, that we would be able to take advantage of is based off of digital ECG recordings and numerica

> Essentially all of the research, clinical and computer science, that we would be able to take advantage of is based off of digital ECG recordings and numerical data not digitized ECG images. This project isn't using any of that. It's what one would call a 'novel' approach. It may upset you that they are not building on the existing body of medical knowledge that you might consider important. They are instead building on the body of knowledge that exists in a different space. > This is the first publication to my knowledge using an image based approach and their innovation is partially validating a deep learning model that is as accurate as explainable models and algorithms described as early as the mid 2000s and can already be clinically implemented. Then it's notable they achieved this result given they did not inherit any of the previous research. > I would love to hear a reason for why it may be a better approach to analyze a noisier and less rich digitized image space representation an ECG rather than the raw digital signals. Once again, transforming data into an "image" representation does not automatically imply a lossy process or a process that introduces noise. There are ML models which operate on the raw bitstream from a CCD camera just as well as ML models which operate on "frames" of image data. Both approaches are valid ways to "see the world."