IP Library › Granted Patent US 12,367,578
Granted Patent B2
US 12,367,578 · App. 18/435,214 · Granted Jul 22, 2025

Diagnosis of a disease condition using an automated diagnostic model

Inventors: Michael Abramoff (Iowa City, IA); Gwenole Quellec (Brest, FR)
Assignees: University of Iowa Research Foundation; United States Government as Represented by the Department of Veterans Affairs
G06T7/0012G06F18/00G06T7/75G06V40/193G06T2207/10024G06T2207/20081G06T2207/30041G06T2207/30096G06V2201/03
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Quick Facts
Patent No.
US 12,367,578
App. No.
18/435,214
Granted
Jul 22, 2025
Kind
B2
Abstract

A method of identifying an object of interest can comprise obtaining first samples of an intensity distribution of one or more object of interest, obtaining second samples of an intensity distribution of confounder objects, transforming the first and second samples into an appropriate first space, performing dimension reduction on the transformed first and second samples, whereby the dimension reduction of the transformed first and second samples generates an object detector, transforming one or more of the digital images into the first space, performing dimension reduction on the transformed digital images, whereby the dimension reduction of the transformed digital images generates one or more reduced images, classifying one or more pixels of the one or more reduced images based on a comparison with the object detector, and identifying one or more objects of interest from the classified pixels.

Claims (51)

1. A system for diagnosing a disease condition in a patient, the system comprising:

one or more computer processors on one or more computers;

a computer readable medium on the one or more computers;

an object detection model stored on the computer readable medium, the object detection model comprising a set of parameters of a supervised procedure, wherein the object detection model is configured to output, based on an image, a probability that the image contains an object of interest at each of one or more locations within the image;

an automated diagnostic model stored on the computer readable medium, the automated diagnostic model comprising a set of parameters of a diagnostic model, wherein the automated diagnostic model is configured to output, based on the probability that the image contains the object of interest at each of one or more locations within the image, a probabilistic diagnosis of a disease condition depicted in the image; and

a diagnostic system stored on the computer readable medium, the diagnostic system containing instructions configured to cause the one or more computer processors to perform steps comprising:

receiving the image, the image containing a portion of a body of the patient,

passing the image to the object detection model,

receiving, from the object detection model, the probability that the image contains the object of interest at each of the one or more locations within the image,

passing, to the automated diagnostic model, the probability that the image contains the object of interest at each of the one or more locations within the image,

receiving, from the automated diagnostic model, the probabilistic diagnosis of the disease condition for the image, and

outputting the probabilistic diagnosis of the disease condition.

2. The system of claim 1 , wherein passing the image to the object detection model comprises:

obtaining one or more samples for each of a set of different regions of the image; and

applying each of the one or more samples to the supervised procedure to output a probability that each of the one or more samples contains one or more objects of interest, where the one or more objects of interest are indicative of the disease condition.

3. The system of claim 1 , wherein the supervised procedure comprises a plurality of trained filters, each trained filter configured to identify the probability that the image contains the object of interest.

4. The system of claim 1 , wherein the image passed to the object detection model is of a portion of an eye of the patient, and wherein the disease condition comprises a disorder manifesting in the eye.

5. The system of claim 1 , wherein the image passed to the object detection model is of a portion of an eye of the patient, and wherein the disease condition comprises a disorder manifesting in a retina of the patient.

6. The system of claim 1 , wherein the object of interest at one of the one or more locations within the image comprises an object that has a similar appearance to another object that indicates a disease condition in a patient but that does not itself indicate a disease condition in a patient.

7. The system of claim 1 , wherein the object of interest comprises an object selected from a group consisting of: a microaneurysm, a dot hemorrhage, a flame-shaped hemorrhage, a sub-intimal hemorrhage, a sub-retinal hemorrhage, a pre-retinal hemorrhage, a micro-infarction, a cotton-wool spot, a yellow exudate, and a drusen.

8. The system of claim 1 , wherein the supervised procedure is trained by a method comprising:

receiving a plurality of training examples, each training example comprising a training image and a label indicating whether the training image contains an object of interest at each of one or more locations within the training image; and

fitting the supervised procedure to the plurality of training examples.

9. The system of claim 8 , wherein the training image of at least one of the plurality of training examples is annotated to indicate one or more reference points that correspond to a center of a feature of interest in the training image.

10. The system of claim 1 , wherein the diagnostic model is trained by a method comprising:

receiving a plurality of training examples, each training example comprising a probability that a training image contains an object of interest at each of one or more locations within the training image and a label indicating whether the training image depicts a disease condition; and

fitting the diagnostic model to the plurality of training examples.

11. A computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by one or more computer processors on one or more computers, cause the one or more computer processors to perform steps comprising:

receiving an image of a portion of a body of a patient;

accessing an object detection model, the object detection model comprising a set of parameters of a supervised procedure, wherein the object detection model is configured to output, based on the image, a probability that the image contains an object of interest at each of one or more locations within the image;

passing the image to the object detection model;

receiving, from the object detection model, the probability that the image contains the object of interest at each of the one or more locations within the image;

accessing an automated diagnostic model, the automated diagnostic model comprising a set of parameters of a diagnostic model, wherein the automated diagnostic model is configured to output, based on the probability that the image contains the object of interest at each of one or more locations within the image, a probabilistic diagnosis of a disease condition depicted in the image;

passing, to the automated diagnostic model, the probability that the image contains the object of interest at each of the one or more locations within the image;

receiving, from the automated diagnostic model, the probabilistic diagnosis of the disease condition for the image; and

outputting the probabilistic diagnosis of the disease condition.

12. The computer program product of claim 11 , wherein passing the image to the object detection model comprises:

obtaining one or more samples for each of a set of different regions of the image; and

applying each of the one or more samples to the supervised procedure to output a probability that each of the one or more samples contains one or more objects of interest, where the one or more objects of interest are indicative of the disease condition.

13. The computer program product of claim 11 , wherein the supervised procedure comprises a plurality of trained filters, each trained filter configured to identify the probability that the image contains the object of interest.

14. The computer program product of claim 11 , wherein the image passed to the object detection model is of a portion of an eye of the patient, and wherein the disease condition comprises a disorder manifesting in the eye.

15. The computer program product of claim 11 , wherein the image passed to the object detection model is of a portion of an eye of the patient, and wherein the disease condition comprises a disorder manifesting in a retina of the patient.

16. The computer program product of claim 11 , wherein the object of interest at one of the one or more locations within the image comprises an object that has a similar appearance to another object that indicates a disease condition in a patient but that does not itself indicate a disease condition in a patient.

17. The computer program product of claim 11 , wherein the object of interest comprises an object selected from a group consisting of: a microaneurysm, a dot hemorrhage, a flame-shaped hemorrhage, a sub-intimal hemorrhage, a sub-retinal hemorrhage, a pre-retinal hemorrhage, a micro-infarction, a cotton-wool spot, a yellow exudate, and a drusen.

18. The computer program product of claim 11 , wherein the supervised procedure is trained by a method comprising:

receiving a plurality of training examples, each training example comprising a training image and a label indicating whether the training image contains an object of interest at each of one or more locations within the training image; and

fitting the supervised procedure to the plurality of training examples.

19. The computer program product of claim 18 , wherein the training image of at least one of the plurality of training examples is annotated to indicate one or more reference points that correspond to a center of a feature of interest in the training image.

20. The computer program product of claim 11 , wherein the diagnostic model is trained by a method comprising:

receiving a plurality of training examples, each training example comprising a probability that a training image contains an object of interest at each of one or more locations within the training image and a label indicating whether the training image depicts a disease condition; and

fitting the diagnostic model to the plurality of training examples.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 3, 2025
From: QUELLEC, GWENOLE
To: UNIVERSITY OF IOWA RESEARCH FOUNDATION
Reel/Frame 069734/0406 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 3, 2025
From: ABRAMOFF, MICHAEL D.
To: UNIVERSITY OF IOWA RESEARCH FOUNDATION; UNITED STATES GOVERNMENT AS REPRESENTED BY THE DEPARTMENT OF VETERANS AFFAIRS
Reel/Frame 069734/0575 →
Continuity (5)
Continuation 17901582 · Sep 1, 2022
Continuation 16158093 · Oct 11, 2018
Continuation 13992552
Provisional Application 61420497 · Dec 7, 2010
Related Publication 20240177305A1 · May 30, 2024
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International Preliminary Report on Patentability issued on Mar. 10, 2009 by the International Searching Authority for Patent Application No. PCT/US2006/012191, which was filed on Mar. 31, 2006 and published as WO 2006/… [cited by applicant]
International Search Report and Written Opinion mailed on Sep. 17, 2008 by the International Searching Authority for Patent Application No. PCT/US2007/065862, which was filed on Apr. 3, 2007 and published as WO 2007/118… [cited by applicant]
International Preliminary Report on Patentability issued on Nov. 4, 2008 by the International Searching Authority for Patent Application No. PCT/US2007/065862, which was filed on Apr. 3, 2007 and published as WO 2007/11… [cited by applicant]
International Search Report and Written Opinion mailed on Oct. 15, 2008 by the International Searching Authority for Patent Application No. PCT/US2008/065043, which was filed on May 29, 2008 and published as WO 2008/150… [cited by applicant]
International Preliminary Report on Patentability issued Dec. 1, 2009 by the International Searching Authority for Patent Application No. PCT/US2008/065043, which was filed on May 29, 2008 and published as WO 2008/15084… [cited by applicant]
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International Preliminary Report on Patentability issued on Aug. 30, 2011 by the International Searching Authority for Patent Application No. PCT/US2010/025369, which was filed on Feb. 25, 2010 and published as WO 2010/… [cited by applicant]
International Search Report and Written Opinion was mailed on Apr. 3, 2012 by the International Searching Authority for Application No. PCT/US2011/63537 , which was filed on Dec. 6, 2011 and published as WO 2012/078636 … [cited by applicant]
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Notice of Allowance issued on Jul. 11, 2018 by the U.S. Patent and Trademark Office for U.S. Appl. No. 13/992,552, filed Jul. 25, 2013 and published as US 2015/0379708 on Dec. 31, 2015 (Inventor—Abramoff et al.; Applica… [cited by applicant]
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International Preliminary Report on Patentability issued on Jul. 23, 2013 by the International Searching Authority for Patent Application No. PCT/US2012/022115, which was filed on Jan. 20, 2012 and published as WO 2012/… [cited by applicant]
European Search Report issued on Mar. 16, 2016 by the European Patent Office for Application No. 12736290.3, which was filed on Jan. 20, 2011, and published as 2665406, on Nov. 27, 2013 (Inventor—Abramoff et al.; Applic… [cited by applicant]
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Response to Final Rejection mailed on Aug. 24, 2015 to the U.S. Patent and Trademark Office for U.S. Appl. No. 13/355,386, filed Jan. 20, 2012 and published as US 2012/0236259 on Sep. 20, 2012 (Applicant—University of I… [cited by applicant]
Non Final Rejection issued on Mar. 30, 2016 by the U.S. Patent and Trademark Office for U.S. Appl. No. 13/355,386, filed Jan. 20, 2012 and published as US 2012/0236259 on Sep. 20, 2012 (Applicant—University of Iowa Rese… [cited by applicant]
Response to Non Final Rejection mailed on Sep. 30, 2016 to the U.S. Patent and Trademark Office for U.S. Appl. No. 13/355,386, filed Jan. 20, 2012 and published as US 2012/0236259 on Sep. 20, 2012 (Applicant—University … [cited by applicant]
Final Rejection issued on Nov. 9, 2016 by the U.S. Patent and Trademark Office for U.S. Appl. No. 13/355,386, filed Jan. 20, 2012 and published as US 2012/0236259 on Sep. 20, 2012 (Applicant—University of Iowa Research … [cited by applicant]
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International Preliminary Report on Patentability mailed on Aug. 4, 2015 by the International Searching Authority for Application No. PCT/US2014/014298, which was filed on Jan. 31, 2014 and published as WO/2014/158345 o… [cited by applicant]
Preliminary Amendment mailed on Jul. 30, 2015 to the U.S. Patent and Trademark Office for U.S. Appl. No. 14/764,926, filed Jul. 30, 2015 and published as US 2015/0379708 on Dec. 31, 2015 (Applicant—University of Iowa Re… [cited by applicant]
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International Search Report and Written Opinion mailed on Aug. 15, 2014 by the International Searching Authority for Application No. PCT/US2014/28055, which was filed on Mar. 14, 2014 and published as WO 2014/143891 on … [cited by applicant]
International Preliminary Report on Patentability was mailed on Sep. 15, 2015 by the International Searching Authority for Application No. PCT/US2014/28055, which was filed on Mar. 14, 2014 and published as WO 2014/1438… [cited by applicant]
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