CORONARY ARTERY STENT PREDICTION METHOD, DEVICE, AND RECORDING MEDIUM USING DEEP LEARNING BASED ON ULTRASOUND IMAGE
A deep learning-based stent prediction method according to an embodiment of the present disclosure includes: setting, as a region of interest, a region in which a procedure is to be performed, among blood vessel regions of a target patient, and obtaining a first intravascular ultrasound (IVUS) image...
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| creator | MIN, Hyun Seok CHO, Hyung Joo KANG, Soo Jin LEE, June Goo |
| description | A deep learning-based stent prediction method according to an embodiment of the present disclosure includes: setting, as a region of interest, a region in which a procedure is to be performed, among blood vessel regions of a target patient, and obtaining a first intravascular ultrasound (IVUS) image, which is a preprocedural IVUS image of the region of interest; obtaining a plurality of first IVUS cross-sectional images into which the first IVUS image is divided at predetermined intervals; extracting feature information about procedure information about the target patient; obtaining mask image information in which a blood vessel boundary and an inner wall boundary are distinguished from each other, with respect to the plurality of first IVUS cross-sectional images; and predicting progress of a stent procedure including a postprocedural area of a stent for the target patient, by inputting, into an artificial intelligence model, the plurality of first IVUS cross-sectional images, the feature information, and the mask image information. |
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obtaining a plurality of first IVUS cross-sectional images into which the first IVUS image is divided at predetermined intervals; extracting feature information about procedure information about the target patient; obtaining mask image information in which a blood vessel boundary and an inner wall boundary are distinguished from each other, with respect to the plurality of first IVUS cross-sectional images; and predicting progress of a stent procedure including a postprocedural area of a stent for the target patient, by inputting, into an artificial intelligence model, the plurality of first IVUS cross-sectional images, the feature information, and the mask image information.</description><language>eng ; fre ; ger</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; DIAGNOSIS ; HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA ; HUMAN NECESSITIES ; HYGIENE ; IDENTIFICATION ; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ; INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS ; MEDICAL OR VETERINARY SCIENCE ; PHYSICS ; SURGERY</subject><creationdate>2024</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20241023&DB=EPODOC&CC=EP&NR=4257056A4$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20241023&DB=EPODOC&CC=EP&NR=4257056A4$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>MIN, Hyun Seok</creatorcontrib><creatorcontrib>CHO, Hyung Joo</creatorcontrib><creatorcontrib>KANG, Soo Jin</creatorcontrib><creatorcontrib>LEE, June Goo</creatorcontrib><title>CORONARY ARTERY STENT PREDICTION METHOD, DEVICE, AND RECORDING MEDIUM USING DEEP LEARNING BASED ON ULTRASOUND IMAGE</title><description>A deep learning-based stent prediction method according to an embodiment of the present disclosure includes: setting, as a region of interest, a region in which a procedure is to be performed, among blood vessel regions of a target patient, and obtaining a first intravascular ultrasound (IVUS) image, which is a preprocedural IVUS image of the region of interest; obtaining a plurality of first IVUS cross-sectional images into which the first IVUS image is divided at predetermined intervals; extracting feature information about procedure information about the target patient; obtaining mask image information in which a blood vessel boundary and an inner wall boundary are distinguished from each other, with respect to the plurality of first IVUS cross-sectional images; and predicting progress of a stent procedure including a postprocedural area of a stent for the target patient, by inputting, into an artificial intelligence model, the plurality of first IVUS cross-sectional images, the feature information, and the mask image information.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>DIAGNOSIS</subject><subject>HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA</subject><subject>HUMAN NECESSITIES</subject><subject>HYGIENE</subject><subject>IDENTIFICATION</subject><subject>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</subject><subject>INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS</subject><subject>MEDICAL OR VETERINARY SCIENCE</subject><subject>PHYSICS</subject><subject>SURGERY</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNjEEKwjAQRbtxIeod5gAVRFtdj5mxDbRJmSSCq1IkrkQL9f6Yggdw9Xn8__4ym5QVa1BugOI5hfNsPHTCpJXX1kDLvraUA_FVK84BDYFw0kibKrWkQwvBzUDMHTSMYmY6o2OC9BAaL-hsSKJuseJ1tngMzylufrnK4MJe1ds4vvs4jcM9vuKn567Yl6ddecTi8MfkC_kyOAM</recordid><startdate>20241023</startdate><enddate>20241023</enddate><creator>MIN, Hyun Seok</creator><creator>CHO, Hyung Joo</creator><creator>KANG, Soo Jin</creator><creator>LEE, June Goo</creator><scope>EVB</scope></search><sort><creationdate>20241023</creationdate><title>CORONARY ARTERY STENT PREDICTION METHOD, DEVICE, AND RECORDING MEDIUM USING DEEP LEARNING BASED ON ULTRASOUND IMAGE</title><author>MIN, Hyun Seok ; CHO, Hyung Joo ; KANG, Soo Jin ; LEE, June Goo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_EP4257056A43</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng ; fre ; ger</language><creationdate>2024</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>DIAGNOSIS</topic><topic>HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA</topic><topic>HUMAN NECESSITIES</topic><topic>HYGIENE</topic><topic>IDENTIFICATION</topic><topic>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</topic><topic>INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS</topic><topic>MEDICAL OR VETERINARY SCIENCE</topic><topic>PHYSICS</topic><topic>SURGERY</topic><toplevel>online_resources</toplevel><creatorcontrib>MIN, Hyun Seok</creatorcontrib><creatorcontrib>CHO, Hyung Joo</creatorcontrib><creatorcontrib>KANG, Soo Jin</creatorcontrib><creatorcontrib>LEE, June Goo</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>MIN, Hyun Seok</au><au>CHO, Hyung Joo</au><au>KANG, Soo Jin</au><au>LEE, June Goo</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>CORONARY ARTERY STENT PREDICTION METHOD, DEVICE, AND RECORDING MEDIUM USING DEEP LEARNING BASED ON ULTRASOUND IMAGE</title><date>2024-10-23</date><risdate>2024</risdate><abstract>A deep learning-based stent prediction method according to an embodiment of the present disclosure includes: setting, as a region of interest, a region in which a procedure is to be performed, among blood vessel regions of a target patient, and obtaining a first intravascular ultrasound (IVUS) image, which is a preprocedural IVUS image of the region of interest; obtaining a plurality of first IVUS cross-sectional images into which the first IVUS image is divided at predetermined intervals; extracting feature information about procedure information about the target patient; obtaining mask image information in which a blood vessel boundary and an inner wall boundary are distinguished from each other, with respect to the plurality of first IVUS cross-sectional images; and predicting progress of a stent procedure including a postprocedural area of a stent for the target patient, by inputting, into an artificial intelligence model, the plurality of first IVUS cross-sectional images, the feature information, and the mask image information.</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING DIAGNOSIS HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA HUMAN NECESSITIES HYGIENE IDENTIFICATION IMAGE DATA PROCESSING OR GENERATION, IN GENERAL INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS MEDICAL OR VETERINARY SCIENCE PHYSICS SURGERY |
| title | CORONARY ARTERY STENT PREDICTION METHOD, DEVICE, AND RECORDING MEDIUM USING DEEP LEARNING BASED ON ULTRASOUND IMAGE |
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