| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_ee602c630e57f59c6cfc1c1634e36064
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_bc7693dae06e6b3ff39c820ec91bf5db |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N2800-324
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N2800-347
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N2800-2871
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V2201-03
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20061
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30101
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B50-30
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30104 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H30-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H30-40
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-0012
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-12
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06K9-00147
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T5-50
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V20-698
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-30
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-443
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H20-00 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H50-30
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H30-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H30-40
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T5-50
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-00 |
| filingDate |
2019-05-17-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_518cff538c8937df796f00a887362b09
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_b3a12774e80264ec243b39b35c18a0e5
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_8d497df2682fe8b08c9dde77124221e7 |
| publicationDate |
2020-01-23-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
US-2020027208-A1 |
| titleOfInvention |
Hough transform-based vascular network disorder features on baseline fluorescein angiography scans predict response to anti-vegf therapy in diabetic macular edema |
| abstract |
Embodiments facilitate prediction of anti-vascular endothelial growth (anti-VEGF) therapy response in DME or RVO patients. A first set of embodiments discussed herein relates to training of a machine learning classifier to determine a prediction for response to anti-VEGF therapy based on a vascular network organization via Hough transform (VaNgOGH) descriptor generated based on FA images of tissue demonstrating DME or RVO. A second set of embodiments discussed herein relates to determination of a prediction of response to anti-VEGF therapy for a DME or RVO patient (e.g., non-rebounder vs. rebounder, response vs. non-response) based on a VaNgOGH descriptor generated based on FA imagery of the patient. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-112442534-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-111612856-A |
| priorityDate |
2018-07-18-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |