| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_fd74383b011cbdd67a1f336f251d1de4 |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-047
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V2201-03
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-044
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61N2005-1074
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61N2005-1041
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61N2005-1061
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61N2005-1052
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61N2005-1055 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61N5-1075
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61N5-103
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61N5-1049
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61N5-1031
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61N5-1064
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61N5-1081
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H20-40
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H30-40
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61N5-1039
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-7784
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61N5-1047
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N7-01 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61N5-10
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H20-40
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N20-00 |
| filingDate |
2020-06-17-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_fbf52a8da179f398038eba8c7ff51892
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_e5b04c1844ac6ad7768fbd9e7a3f9b27
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_5ad19cfbc634ee0072c002ca76124a62 |
| publicationDate |
2022-04-27-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
EP-3986547-A1 |
| titleOfInvention |
Methods and systems for quality-aware continuous learning for radiotherapy treatment planning |
| abstract |
Example methods and systems for quality-aware continuous learning for radiotherapy treatment planning are provided. One example method may comprise: obtaining (210) an artificial intelligence (AI) engine that is trained to perform a radiotherapy treatment planning task. The method may also comprise: based on input data associated with a patient, performing (220) the radiotherapy treatment planning task using the AI engine to generate output data associated with the patient; and obtaining (230) modified output data that includes one or more modifications made by a treatment planner to the output data. The method may further comprise: performing (240) quality evaluation based on (a) first quality indicator data associated with the modified output data, and/or (b) second quality indicator data associated with the treatment planner. In response to a decision to accept, a modified AI engine may be generated (260) by re-training the AI engine based on the modified output data. |
| priorityDate |
2019-06-21-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |