US 2023/0419485 A1 · AUTONOMOUS DIAGNOSIS OF A DISORDER IN A PATIENT FROM IMAGE ANALYSIS

Abstract

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.

Claims20

Dependent claims

A method for training a diagnostic model for diagnosing a disease condition in a patient, the method comprising:

accessing a plurality of input images, each input image including a portion of body of a patient selected from a plurality of patients;
accessing a label for each input image that indicates whether the selected patient in the input image has a disease condition;
obtaining a plurality of training examples, each training example corresponding to a given input image and comprising:
for each respective location in the given input image, an indication that the given input image contains an object of interest at the respective location, wherein the object of interest is indicative of a disease, and
the label of the given input image;
and
for a diagnostic model, the diagnostic model comprising a machine learning model that is configured to output a diagnosis of a disease condition based on an input of indications of whether there is an object of interest at each location within a sample image:
training the diagnostic model by repeatedly applying a training example from the plurality of training examples to the diagnostic model and updating parameters of the diagnostic model to improve an objective performance threshold thereof, and
stopping the training after the objective performance threshold satisfies a condition.

6 more elements

A diagnostic product for diagnosing a disease condition in a patient, wherein the diagnostic product is stored on a non-transitory computer readable medium and is manufactured by a process comprising:

accessing a plurality of input images, each input image including a portion of body of a patient selected from a plurality of patients;
accessing a label for each input image that indicates whether the selected patient in the input image has a disease condition;
obtaining a plurality of training examples, each training example corresponding to a given input image and comprising:
for each respective location in the given input image, an indication that the given input image contains an object of interest at the respective location, wherein the object of interest is indicative of a disease, and
the label of the given input image;
for a diagnostic model, the diagnostic model comprising a machine learning model that is configured to output a diagnosis of a disease condition based on an input of indications of whether there is an object of interest at each location within a sample image:
training the diagnostic model by repeatedly applying a training example from the plurality of training examples to the diagnostic model and updating parameters of the diagnostic model to improve an objective performance threshold thereof, and
stopping the training after the objective performance threshold satisfies a condition;
and
storing the updated parameters for the diagnostic model on the computer readable storage medium.

7 more elements

Dependencies are inferred from the claim text.

Autonomous diagnosis of a disorder in a patient from image analysis

Life of this patent

Autonomous diagnosis of a disorder in a patient from image analysis

Life of this patent

Abstract

Claims20