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US 12,721,589 B2Granted
Artificial intelligence system for determining drug use through medical imaging
AssigneeUltrasound AI, Inc.
InventorRobert S Bunn
Life of this patent
Estimating the term…
Abstract
Systems and methods for establishing a patient's pharmaceutical use are provided. A neural network is trained on a dataset of medical images, such as ultrasound images, that are tagged with information concerning the use of the pharmaceutical by people who were imaged to produce the medical images. The trained neural network can then be provided with medical images of a patient, and the neural network can then make a determination as to the patient's pharmaceutical use.
Claims32
A system configured to make a medical determination, the system comprising:
- a medical image generator configured to acquire non-invasive medical images of a body part of a patient during an imaging session, the medical image generator further configured to adapt generation of the 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;
- an image storage storing a set of the medical images of the body part of a the patient, the set of medical images having been acquired during the imaging session;
- image analysis logic comprising a trained neural network in communication with the image storage and configured to:
- (i) process a plurality of the medical images of the set to provide a plurality of image-level determinations indicative of a quantitative estimate of an exposure of the patient to a pharmaceutical,
- (ii) generate an aggregate determination regarding the quantitative estimate by aggregating the image-level determinations, wherein the aggregating comprises excluding at least one anatomical view or other information that is determined to be commonly uninformative and/or weighting a plurality of anatomical views in calculating the aggregate determination, and
- (iii) determine a confidence score associated with the aggregate determination;
- feedback logic configured, based on the confidence score and/or a classification of medical images already acquired during the imaging session, to guide acquisition of at least one additional medical image during the imaging session via a user interface;
- the user interface configured to provide at least the aggregate determination and guidance output by the feedback logic to a user;
- and
- a microprocessor configured to execute at least the image analysis logic and the feedback logic.
7 more elements
A method for training a neural network to make a medical determination, the method comprising:
- receiving a set of non-invasive medical images of body parts of a plurality of patients, the medical images tagged with information concerning exposure by the respective patients to a pharmaceutical;
- classifying the medical images according to views or features included within the medical images;
- filtering the medical images to remove images that lack features or views that have been determined to have little or no determinative value and/or to remove medical images filtered according to quality or resolution;
- dividing the set of medical images into a training set and a validation set;
- providing the medical images of the training set, and their tags, to a neural network to train the neural network to determine a quantitative exposure of the plurality of patients to the pharmaceutical based on images of the body parts;
- and
- providing medical images of a single patient from the validation set to the neural network to make a determination, and comparing the determination to the tags associated with the medical images.
4 more elements
A method for making a medical determination, the method comprising:
- during a medical imaging session, generating, using an image generator comprising a source and a detector, a set of non-invasive medical images of a body part of a patient;
- storing the set of medical images;
- providing a plurality of medical images of the set to a neural network that has been trained to determine, from medical images of the body part, a quantitative estimate of an exposure of the patient to a pharmaceutical;
- receiving, from the neural network, a plurality of image-level determinations and a confidence score associated with the determinations;
- generating an aggregate determination regarding the quantitative estimate by aggregating the image-level determinations, wherein the aggregating comprises excluding at least one anatomical view or other information that is determined to be commonly uninformative and/or weighting a plurality of anatomical views in calculating the aggregate determination;
- when the confidence score indicates inadequate precision, accuracy, and/or reliability, providing feedback during the imaging session via a user interface to guide acquisition of at least one additional medical image and/or to adapt generation of medical images by fine-tuning at least one of (i) image generation, (ii) focus, or (iii) image processing parameters;
- and
- providing the aggregate determination regarding the quantitative estimate.
5 more elements
Dependencies are inferred from the claim text.