Artificial intelligence system for comprehensive medical diagnosis, prognosis, and treatment optimization through medical imaging

A method for training a neural network to make comprehensive medical predictions, the method comprising:

• receiving a dataset of non-invasive medical images of a plurality of anatomical structures of a plurality of patients, the non-invasive medical images labeled with ground truth diagnoses of a plurality of medical conditions;
• classifying the non-invasive medical images according to views or features included within the non-invasive medical images;
• filtering the non-invasive medical images to remove images:
• (i) that lack the views or the features that have been determined to have determinative value;
• and
• (ii) that are of insufficient quality or resolution;
• partitioning the dataset into a training set, a validation set, and an independent test set;
• training the neural network using the training set to predict the plurality of medical conditions;
• fine-tuning the neural network using the validation set to optimize its performance and generalization;
• and
• evaluating final performance characteristics of the neural network using the independent test set, by comparing quantitative predictions based on the independent test set provided by the neural network to ground truth diagnosis labels associated with the independent test set.

A method for making a comprehensive medical determination using a trained neural network, the method comprising:

• acquiring one or more non-invasive medical images of a plurality of anatomical structures of a patient using an image acquisition device, the image acquisition device configured to adapt generation of the one or more non-invasive medical images based on feedback received from image analysis logic by fine-tuning at least one of (i) image generation, (ii) focus, or (iii) image processing parameters;
• pre-processing the acquired non-invasive medical images to optimize the acquired non-invasive medical images for input into the trained neural network;
• providing the pre-processed non-invasive medical images to the trained neural network, which has been optimized to predict a plurality of medical conditions;
• receiving from the trained neural network a quantitative prediction, and a confidence score associated with the quantitative prediction, related to the presence, absence, severity, or risk of a medical condition for the patient;
• and
• presenting the quantitative prediction to a healthcare professional via a user interface to support clinical decision-making.