Systems and methods for processing electronic images to identify transplant donor-recipient matches

1 . 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.

2 . The method of claim 1 , wherein the transplant is a fecal matter transplant.

3 . The method of claim 1 , wherein the transplant is a liver transplant.

4 . The method of claim 1 , wherein a salient region detection module is applied to determine a saliency of each region within the received digital medical image, and non-salient image regions are excluded from processing by the trained machine learning system.

5 . The method of claim 1 further including:

receiving metadata corresponding to the subject, the metadata comprising: clinical data, genetic information, microbial composition, and/or life history data; and

providing the metadata into the trained machine learning system.

6 . The method of claim 1 , further including:

notifying, with a notification, donor collection entities of a need for donors with a given profile.

7 . The method of claim 6 , wherein the notification may include metadata of the subject in need, an indication of the donor profile needed that matches the recipient, and/or a request for the donor collection entities to start collecting donors that have a similar profile.

8 . 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.

9 . The system of claim 8 , wherein the transplant is a fecal matter transplant.

10 . The system of claim 8 , wherein the transplant is a liver transplant.

11 . The system of claim 8 , wherein a salient region detection module is applied to determine a saliency of each region within the received digital medical image, and non-salient image regions are excluded from processing by the trained machine learning system.

12 . The system of claim 8 further including:

receiving metadata corresponding to the subject, the metadata comprising: clinical data, genetic information, microbial composition, and/or life history data; and

providing the metadata into the trained machine learning system.

13 . The system of claim 8 further including:

notifying, with a notification, donor collection entities of a need for donors with a given profile.

14 . The system of claim 13 , wherein the notification may include metadata of the subject in need, an indication of the donor profile needed that matches the recipient, and/or a request for the donor collection entities to start collecting donors that have a similar profile.

15 . 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.

16 . The non-transitory computer-readable medium of claim 15 , further including:

receiving metadata corresponding to the subject, the metadata comprising: clinical data, genetic information, microbial composition, and/or life history data; and

providing the metadata into the trained machine learning system.

17 . The non-transitory computer-readable medium of claim 15 , further including:

notifying, with a notification, donor collection entities of a need for donors with a given profile, wherein the notification may include metadata of the subject in need, an indication of the donor profile needed that matches the recipient, and/or a request for the donor collection entities to start collecting donors that have a similar profile.