GB2587551A - Determining a clinical outcome for a subject suffering from a macular degenerative disease - Google Patents
Determining a clinical outcome for a subject suffering from a macular degenerative disease Download PDFInfo
- Publication number
- GB2587551A GB2587551A GB2017496.7A GB202017496A GB2587551A GB 2587551 A GB2587551 A GB 2587551A GB 202017496 A GB202017496 A GB 202017496A GB 2587551 A GB2587551 A GB 2587551A
- Authority
- GB
- United Kingdom
- Prior art keywords
- macular
- subject
- images
- clinical outcome
- degenerative disease
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
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Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
Abstract
A computer-implemented method of determining a clinical outcome for a subject suffering from a macular degenerative disease comprises: obtaining patient data relating to the subject and a plurality of macular images of the subject, the plurality of macular images comprising a plurality of images of at least a macular region in the subject's eye captured over a time period; inputting the obtained patient data and plurality of macular images to a machine learning algorithm, wherein the machine learning algorithm is trained to determine a clinical outcome for the subject in dependence on a progression of the macular degenerative disease over said time period; and outputting the determined clinical outcome for the subject. Apparatus for performing the method is also disclosed.
Claims (20)
1. A computer-implemented method of determining a clinical outcome for a subject suffering from a macular degenerative disease, the method comprising: obtaining patient data relating to the subject and a plurality of macular images of the subject, the plurality of macular images comprising a plurality of images of at least a macular region in the subjectâ s eye captured over a time period; inputting the obtained patient data and plurality of macular images to a machine learning algorithm, wherein the machine learning algorithm is trained to determine a clinical outcome for the subject in dependence on a progression of the macular degenerative disease over said time period; and outputting the determined clinical outcome for the subject.
2. The method of claim 2, wherein the time period over which the plurality of images are captured is at least one month.
3. The method of claim l or 2, wherein the machine learning algorithm is trained to determine one or more of the following as the clinical outcome: a recommended time at which to schedule a follow-up appointment; a recommended frequency of a plurality of follow-up appointments; an anti vascular endothelial growth factor treatment; a referral to a medical practitioner; and a recommended course of treatment.
4. The method of claim 1, 2 or 3, wherein the plurality of macular images include one or more retinal images and/ or one or more optical coherence tomography OCT images of the subjectâ s eye.
5. The method of any one of the preceding claims, wherein the patient data comprises one or more of: information indicative of the subjectâ s age, gender and/or ethnicity; clinical history information related to a history of the current subject; and/or one or more retinal image parameters obtained from a retinal image of the subject.
6. The method of claim 5, wherein the clinical history information comprises one or more visual acuity test scores of the subject.
7. The method of any one of the preceding claims, further comprising: subsequently obtaining an updated macular image of the subject, after determining said clinical outcome; and inputting the patient data, the updated macular image and one or more of the plurality of macular images to the machine learning algorithm to determine a subsequent clinical outcome in dependence on a progression of the macular degenerative disease since the treatment was applied.
8. The method of any one of the preceding claims, wherein the macular degenerative disease comprises: wet age-related macular degeneration; retinal vein occlusion; diabetic macular oedema; cystoid macular oedema; and/ or choroidal neovascularization.
9. The method of any one of the preceding claims, further comprising: treating the subject according to the determined clinical outcome.
10. The method of any one of the preceding claims, wherein the patient data comprises genomic information indicative of whether the subject has one or more genetic variations associated with the macular degenerative disease.
11. The method of claim 10, wherein the one or more genetic variations comprise a plurality of possible variations within the same gene, each of the plurality of possible variations being associated with different levels of risk of the macular degenerative disease, and wherein the genomic information is indicative of whether the subject has one of the plurality of possible variations within said gene.
12. The method of claim 10 or 11, wherein the genomic information is obtained by: obtaining genetic data indicative of the subjectâ s genome; and searching the genetic data to determine whether the subject has one or more of the genetic variations associated with the macular degenerative disease.
13. A computer program comprising computer program instructions which, when executed, perform a method according to any one of the preceding claims.
14. A non-transitory computer readable storage medium arranged to store a computer program according to claim 13 .
15. Apparatus for determining a clinical outcome for a subject suffering from a macular degenerative disease, the apparatus comprising: an input for receiving patient data relating to the subject and a plurality of macular images of the subject, the plurality of macular images comprising a plurality of images of at least a macular region in the subjectâ s eye captured over a time period; a machine learning algorithm configured to receive the obtained patient data and plurality of macular images as data inputs, wherein the machine learning algorithm is trained to determine a clinical outcome for the subject in dependence on a progression of the macular degenerative disease over said time period; and an output configured to output the determined clinical outcome for the subject.
16. The apparatus of claim 15, wherein the time period over which the plurality of images are captured is at least one month.
17. The apparatus of claim 15 or 16, wherein the machine learning algorithm is trained to determine one or more of the following as the clinical outcome: a recommended time at which to schedule a follow-up appointment; a recommended frequency of a plurality of follow-up appointments; an anti vascular endothelial growth factor treatment; a referral to a medical practitioner; and a recommended course of treatment.
18. The apparatus of claim 15, 16 or 17, wherein the plurality of macular images include one or more retinal images and/or one or more optical coherence tomography OCT images of the subjectâ s eye.
19. The apparatus of any one of claims 15 to 18, wherein the patient data comprises one or more of: information indicative of the subjectâ s age, gender and/or ethnicity; clinical history information related to a history of the current subject; and/or a retinal image parameter obtained from a retinal image of the subject.
20. The apparatus of any one of claims 15 to 19, wherein the macular degenerative disease comprises: wet age-related macular degeneration; retinal vein occlusion; diabetic macular oedema; cystoid macular oedema; and/ or choroidal neovascularization.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| GBGB1805642.4A GB201805642D0 (en) | 2018-04-05 | 2018-04-05 | Determining a clinical outcome for a subject suffering from a macular degenerative disease |
| PCT/GB2019/051003 WO2019193362A2 (en) | 2018-04-05 | 2019-04-05 | Determining a clinical outcome for a subject suffering from a macular degenerative disease |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| GB202017496D0 GB202017496D0 (en) | 2020-12-23 |
| GB2587551A true GB2587551A (en) | 2021-03-31 |
Family
ID=62202950
Family Applications (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| GBGB1805642.4A Ceased GB201805642D0 (en) | 2018-04-05 | 2018-04-05 | Determining a clinical outcome for a subject suffering from a macular degenerative disease |
| GB2017496.7A Withdrawn GB2587551A (en) | 2018-04-05 | 2019-04-05 | Determining a clinical outcome for a subject suffering from a macular degenerative disease |
Family Applications Before (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| GBGB1805642.4A Ceased GB201805642D0 (en) | 2018-04-05 | 2018-04-05 | Determining a clinical outcome for a subject suffering from a macular degenerative disease |
Country Status (2)
| Country | Link |
|---|---|
| GB (2) | GB201805642D0 (en) |
| WO (1) | WO2019193362A2 (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP4143846A1 (en) * | 2020-04-29 | 2023-03-08 | Novartis AG | A computer-implemented system and method for assessing a level of activity of a disease or condition in a patient's eye |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160174830A1 (en) * | 2013-07-31 | 2016-06-23 | The Board Of Trustees Of The Leland Stanford Junior University | Method and System for Evaluating Progression of Age-Related Macular Degeneration |
| WO2016177722A1 (en) * | 2015-05-05 | 2016-11-10 | Medizinische Universität Wien | Computerized device and method for processing image data |
| WO2017096031A1 (en) * | 2015-12-03 | 2017-06-08 | Regeneron Pharmaceuticals, Inc. | Methods of associating genetic variants with a clinical outcome in patients suffering from age-related macular degeneration treated with anti-vegf |
-
2018
- 2018-04-05 GB GBGB1805642.4A patent/GB201805642D0/en not_active Ceased
-
2019
- 2019-04-05 WO PCT/GB2019/051003 patent/WO2019193362A2/en active Application Filing
- 2019-04-05 GB GB2017496.7A patent/GB2587551A/en not_active Withdrawn
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160174830A1 (en) * | 2013-07-31 | 2016-06-23 | The Board Of Trustees Of The Leland Stanford Junior University | Method and System for Evaluating Progression of Age-Related Macular Degeneration |
| WO2016177722A1 (en) * | 2015-05-05 | 2016-11-10 | Medizinische Universität Wien | Computerized device and method for processing image data |
| WO2017096031A1 (en) * | 2015-12-03 | 2017-06-08 | Regeneron Pharmaceuticals, Inc. | Methods of associating genetic variants with a clinical outcome in patients suffering from age-related macular degeneration treated with anti-vegf |
Non-Patent Citations (2)
| Title |
|---|
| CECILIA S. LEE ET AL, "Deep Learning Is Effective for Classifying Normal versus Age-Related Macular Degeneration OCT Images", OPHTHALMOLOGY RETINA 20171101 ELSEVIER INC USA, (20170701), vol. 1, no. 4, doi:10.1016/j.oret.2016.12.009, ISSN 2468-6530, pages 322 - 327, * |
| JOHANNA M. SEDDON ET AL, "Risk Prediction for Progression of Macular Degeneration: 10 Common and Rare Genetic Variants, Demographic, Environmental, and Macular Covariates", INVESTIGATIVE OPTHALMOLOGY & VISUAL SCIENCE, US, (20150410), vol. 56, no. 4, doi:10.1167/iovs.14-15841, ISSN 1552-5783, page 21 * |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2019193362A3 (en) | 2019-11-28 |
| GB202017496D0 (en) | 2020-12-23 |
| GB201805642D0 (en) | 2018-05-23 |
| WO2019193362A2 (en) | 2019-10-10 |
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Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| WAP | Application withdrawn, taken to be withdrawn or refused ** after publication under section 16(1) |