Systems and methods for processing electronic images to identify transplant donor-recipient matches
AssigneePaige.AI, Inc.
InventorsJeremy Daniel Kunz, Christopher Kanan
Life of this patent
Estimating the term…
Abstract
Systems and methods are described herein for processing electronic medical images to predict one or more donor recipients for a patient. For example, a digital medical image of the patient may be received, wherein the patient is in need of a transplant. A trained machine learning system may be determined. The digital medical image may be provided into the trained machine learning system, the trained machine learning system determining a patient embedding. Using the patient embedding, a subset of donor recipients may be determined. Based on the subset of donor recipients a recommendation of optimal donors may be determined.
Claims17
A computer-implemented method for processing electronic medical images to predict one or more donors for a subject, comprising:
- receiving a digital medical image of the subject, wherein the subject is in need of a transplant;
- determining a trained machine learning system;
- providing the digital medical image into the trained machine learning system, the trained machine learning system determining a subject embedding;
- determining, using the subject embedding, a subset of donors;
- determining based on the subset of donors a recommendation of optimal donors;
- and
- determining a second trained machine learning system, the second trained machine learning system being capable of determining a dietary, sleep, or exercise suggestion, wherein the second trained machine learning system receives as input the digital medical image of the subject, determines a lifestyle embedding, and determines, based on the lifestyle embedding, a dietary, sleep, or exercise suggestion for the subject.
A system for processing electronic medical images to predict one or more donors for a subject, the system comprising:
- at least one memory storing instructions;
- and
- at least one processor configured to execute the instructions to perform operations comprising:
- receiving a digital medical image of the subject, wherein the subject is in need of a transplant;
- determining a trained machine learning system;
- providing the digital medical image into the trained machine learning system, the trained machine learning system determining a subject embedding;
- determining, using the subject embedding, a subset of donors;
- determining based on the subset of donors a recommendation of optimal donors;
- and
- determining a second trained machine learning system, the second trained machine learning system being capable of determining a dietary, sleep, or exercise suggestion, wherein the second trained machine learning system receives as input the digital medical image of the subject, determines a lifestyle embedding, and determines, based on the lifestyle embedding, a dietary, sleep, or exercise suggestion for the subject.
A non-transitory computer-readable medium storing instructions that, when executed by a processor, perform operations processing electronic medical images to predict one or more donors for a subject, the operations comprising:
- receiving a digital medical image of the subject, wherein the subject is in need of a transplant;
- determining a trained machine learning system;
- providing the digital medical image into the trained machine learning system, the trained machine learning system determining a subject embedding;
- determining, using the subject embedding, a subset of donors recipients;
- and
- determining based on the subset of donors a recommendation of optimal donors;
- and
- determining a second trained machine learning system, the second trained machine learning system being capable of determining a dietary, sleep, or exercise suggestion, wherein the second trained machine learning system receives as input the digital medical image of the subject, determines a lifestyle embedding, and determines, based on the lifestyle embedding, a dietary, sleep, or exercise suggestion for the subject.
Dependencies are inferred from the claim text.