| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_28a89f8dab024e02ae765d1273e716f1 |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30048
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20084
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20081
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10101
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30168
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B2576-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30101
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V2201-03 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-764
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-774
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-40
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-82
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-0012
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-0016
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-0066
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H30-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-766 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-00 |
| filingDate |
2020-04-30-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_6fb816d085dd8d15138200b49ecab39a
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_fb146a44a9a4cfd0e8565fca11a3b4ab |
| publicationDate |
2022-01-11-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
KR-20220004094-A |
| titleOfInvention |
Intraluminal image analysis method and device |
| abstract |
Embodiments of the present technology provide devices and methods for analyzing intraluminal images, for example, to predict symptoms of a disease, disease manifestation or event, and/or to track the performance of a drug or other treatment. The method comprises: for each image in the image set of the coronary artery: classifying the image for the presence or absence of diseased tissue using a first neural network; classifying the image according to the presence or absence of an artifact using a second neural network when the image is classified as having a diseased tissue; determining whether to analyze the image based on the classification; when the image is analyzed, analyzing the image by identifying one or more features of interest in the coronary tissue using a third neural network; and measuring each identified characteristic of interest. |
| priorityDate |
2019-05-01-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |