Digital Diagnostics Inc.

Provide are systems methods and devices for diagnosing disease in medical images. In certain aspects, disclosed is a method for training a neural network to detect features in a retinal image including the steps of: a) extracting one or more features images from a Train_0 set, a Test_0 set, a Train_1 set and a Test_1 set; b) combining and randomizing the feature images from Train_0 and Train_1 into a Training data set; c) combining and randomizing the feature images from Test_0 and Test_1 into a testing dataset; d) training a plurality of neural networks having different architectures using a subset of the training dataset while testing on a subset of the testing dataset; e) identifying the best neural network based on each of the plurality of neural networks performance on the testing data set; f) inputting images from Test_0, Train_1, Train_0 and Test_1 to the best neural network and identifying a limited number of false positives and false negative and adding the false positives and false negatives to the training dataset and testing dataset; and g) repeating steps d)-g) until an objective performance threshold is reached.

A device receives an input image of a portion of a patient's body, and applies the input image to a feature extraction model, the feature extraction model comprising a trained machine learning model that is configured to generate an output that comprises, for each respective location of a plurality of locations in the input image, an indication that the input image contains an object of interest that is indicative of a presence of a disease state at the respective location. The device applies the output of the feature extraction model to a diagnostic model, the diagnostic model comprising a trained machine learning model that is configured to output a diagnosis of a disease condition in the patient based on the output of the feature extraction model. The device outputs the determined diagnosis of a disease condition in the patient obtained from the diagnostic model.