METHOD AND DEVICE FOR DETECTING COLON POLYPS THROUGH ARTIFICIAL INTELLIGENCE-BASED VASCULAR LEARNING

A method for detecting colon polyps through artificial intelligence-based vascular learning disclosed in the present disclosure, implemented by a device, comprises the steps of: (a) receiving an image taken from an endoscope in real time; (b) recognizing each section image including the colonic muco...

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description A method for detecting colon polyps through artificial intelligence-based vascular learning disclosed in the present disclosure, implemented by a device, comprises the steps of: (a) receiving an image taken from an endoscope in real time; (b) recognizing each section image including the colonic mucosa and colonic blood vessels in the image; (c) determining whether or not the colonic blood vessel phase is discontinued for each section image; (d) displaying a first visual effect accounting for a blood vessel phase in which a colonic blood vessel is discontinued in each section image; and (e) displaying a second visual effect accounting for a blood vessel phase in which a colonic blood vessel is continued, wherein the recognizing of each section image in step (b) is achieved through a deep learning model, the deep learning model being a machine-learned model based on blood vessel data in a plurality of colon images obtained from external annotators, the degree of discontinuity of blood vessel phases, and blood vessel patterns. Un procédé de détection de polypes du côlon par un apprentissage vasculaire basé sur l'intelligence artificielle décrit dans la présente invention, mis en œuvre par un dispositif, comprend les étapes suivantes : (a) recevoir en temps réel une image prise par un endoscope ; (b) reconnaître chaque image de section comprenant la muqueuse du côlon et les vaisseaux sanguins du côlon dans l'image ; (c) déterminer si la phase de vaisseau sanguin du côlon est interrompue ou non pour chaque image de section ; (d) afficher un premier effet visuel représentant une phase de vaisseau sanguin dans laquelle un vaisseau sanguin du côlon est interrompu dans chaque image de section ; et (e) afficher un second effet visuel représentant une phase de vaisseau sanguin dans laquelle un vaisseau sanguin du côlon est continu, la reconnaissance de chaque image de section dans l'étape (b) étant réalisée au moyen d'un modèle d'apprentissage profond, le modèle d'apprentissage profond étant un modèle d'apprentissage automatique sur la base des données de vaisseaux sanguins dans une pluralité d'images du côlon obtenues à partir d'annotateurs externes, le degré de discontinuité des phases de vaisseaux sanguins et des motifs de vaisseaux sanguins. 본 개시에 공개된 인공지능 기반의 혈관 학습을 통한 대장 용종 검출 방법은 장치에 의해 수행되고, (a) 내시경으로부터 촬영된 영상을 실시간으로 수신하는 단계; (b) 영상 내에서 대장 점막 및 대장 혈관이 포함된 각 구간 영상을 인식하는 단계; (c) 각 구간 영상마다 대장 혈관상의 끊어짐 여부를 판단하는 단계; (d) 각 구간 영상 내에서 대장 혈관이 끊어진 혈관상을 나타내는 제1 시각 효과를 표
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fullrecord <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_WO2023140416A1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>WO2023140416A1</sourcerecordid><originalsourceid>FETCH-epo_espacenet_WO2023140416A13</originalsourceid><addsrcrecordid>eNqNjLsKwkAQANNYiPoPC9aBPMR-vdvkDtbbcLlErELQsxINxP_HFH6A1UwxzDq5nykY0YBOg6beKoJK_KKBVLCuBiUsDhrha9NCMF662gD6YCurLDJYF4jZ1uQUpSdsSUOPreoYPTChd8tkm6we43OOux83yb6ioEwap_cQ52m8xVf8DBcpsqLMD9khP2Je_ld9AQ-XNVY</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>METHOD AND DEVICE FOR DETECTING COLON POLYPS THROUGH ARTIFICIAL INTELLIGENCE-BASED VASCULAR LEARNING</title><source>esp@cenet</source><creator>KO, Ji Hwan</creator><creatorcontrib>KO, Ji Hwan</creatorcontrib><description>A method for detecting colon polyps through artificial intelligence-based vascular learning disclosed in the present disclosure, implemented by a device, comprises the steps of: (a) receiving an image taken from an endoscope in real time; (b) recognizing each section image including the colonic mucosa and colonic blood vessels in the image; (c) determining whether or not the colonic blood vessel phase is discontinued for each section image; (d) displaying a first visual effect accounting for a blood vessel phase in which a colonic blood vessel is discontinued in each section image; and (e) displaying a second visual effect accounting for a blood vessel phase in which a colonic blood vessel is continued, wherein the recognizing of each section image in step (b) is achieved through a deep learning model, the deep learning model being a machine-learned model based on blood vessel data in a plurality of colon images obtained from external annotators, the degree of discontinuity of blood vessel phases, and blood vessel patterns. Un procédé de détection de polypes du côlon par un apprentissage vasculaire basé sur l'intelligence artificielle décrit dans la présente invention, mis en œuvre par un dispositif, comprend les étapes suivantes : (a) recevoir en temps réel une image prise par un endoscope ; (b) reconnaître chaque image de section comprenant la muqueuse du côlon et les vaisseaux sanguins du côlon dans l'image ; (c) déterminer si la phase de vaisseau sanguin du côlon est interrompue ou non pour chaque image de section ; (d) afficher un premier effet visuel représentant une phase de vaisseau sanguin dans laquelle un vaisseau sanguin du côlon est interrompu dans chaque image de section ; et (e) afficher un second effet visuel représentant une phase de vaisseau sanguin dans laquelle un vaisseau sanguin du côlon est continu, la reconnaissance de chaque image de section dans l'étape (b) étant réalisée au moyen d'un modèle d'apprentissage profond, le modèle d'apprentissage profond étant un modèle d'apprentissage automatique sur la base des données de vaisseaux sanguins dans une pluralité d'images du côlon obtenues à partir d'annotateurs externes, le degré de discontinuité des phases de vaisseaux sanguins et des motifs de vaisseaux sanguins. 본 개시에 공개된 인공지능 기반의 혈관 학습을 통한 대장 용종 검출 방법은 장치에 의해 수행되고, (a) 내시경으로부터 촬영된 영상을 실시간으로 수신하는 단계; (b) 영상 내에서 대장 점막 및 대장 혈관이 포함된 각 구간 영상을 인식하는 단계; (c) 각 구간 영상마다 대장 혈관상의 끊어짐 여부를 판단하는 단계; (d) 각 구간 영상 내에서 대장 혈관이 끊어진 혈관상을 나타내는 제1 시각 효과를 표시하는 단계; 및 (e) 대장 혈관이 연속된 혈관상을 나타내는 제2 시각 효과를 표시하는 단계; 를 포함하고, (b) 단계는, 딥러닝 모델을 통해 각 구간 영상을 인식하되, 딥러닝 모델은 외부 어노테이터들로부터 획득된 복수의 대장 영상 내의 혈관 데이터와, 혈관상의 끊어짐 정도 및 혈관 패턴을 기반으로 기계 학습된 모델이다.</description><language>eng ; fre ; kor</language><subject>DIAGNOSIS ; HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA ; HUMAN NECESSITIES ; HYGIENE ; IDENTIFICATION ; INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS ; MEDICAL OR VETERINARY SCIENCE ; PHYSICS ; SURGERY</subject><creationdate>2023</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&amp;date=20230727&amp;DB=EPODOC&amp;CC=WO&amp;NR=2023140416A1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20230727&amp;DB=EPODOC&amp;CC=WO&amp;NR=2023140416A1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>KO, Ji Hwan</creatorcontrib><title>METHOD AND DEVICE FOR DETECTING COLON POLYPS THROUGH ARTIFICIAL INTELLIGENCE-BASED VASCULAR LEARNING</title><description>A method for detecting colon polyps through artificial intelligence-based vascular learning disclosed in the present disclosure, implemented by a device, comprises the steps of: (a) receiving an image taken from an endoscope in real time; (b) recognizing each section image including the colonic mucosa and colonic blood vessels in the image; (c) determining whether or not the colonic blood vessel phase is discontinued for each section image; (d) displaying a first visual effect accounting for a blood vessel phase in which a colonic blood vessel is discontinued in each section image; and (e) displaying a second visual effect accounting for a blood vessel phase in which a colonic blood vessel is continued, wherein the recognizing of each section image in step (b) is achieved through a deep learning model, the deep learning model being a machine-learned model based on blood vessel data in a plurality of colon images obtained from external annotators, the degree of discontinuity of blood vessel phases, and blood vessel patterns. Un procédé de détection de polypes du côlon par un apprentissage vasculaire basé sur l'intelligence artificielle décrit dans la présente invention, mis en œuvre par un dispositif, comprend les étapes suivantes : (a) recevoir en temps réel une image prise par un endoscope ; (b) reconnaître chaque image de section comprenant la muqueuse du côlon et les vaisseaux sanguins du côlon dans l'image ; (c) déterminer si la phase de vaisseau sanguin du côlon est interrompue ou non pour chaque image de section ; (d) afficher un premier effet visuel représentant une phase de vaisseau sanguin dans laquelle un vaisseau sanguin du côlon est interrompu dans chaque image de section ; et (e) afficher un second effet visuel représentant une phase de vaisseau sanguin dans laquelle un vaisseau sanguin du côlon est continu, la reconnaissance de chaque image de section dans l'étape (b) étant réalisée au moyen d'un modèle d'apprentissage profond, le modèle d'apprentissage profond étant un modèle d'apprentissage automatique sur la base des données de vaisseaux sanguins dans une pluralité d'images du côlon obtenues à partir d'annotateurs externes, le degré de discontinuité des phases de vaisseaux sanguins et des motifs de vaisseaux sanguins. 본 개시에 공개된 인공지능 기반의 혈관 학습을 통한 대장 용종 검출 방법은 장치에 의해 수행되고, (a) 내시경으로부터 촬영된 영상을 실시간으로 수신하는 단계; (b) 영상 내에서 대장 점막 및 대장 혈관이 포함된 각 구간 영상을 인식하는 단계; (c) 각 구간 영상마다 대장 혈관상의 끊어짐 여부를 판단하는 단계; (d) 각 구간 영상 내에서 대장 혈관이 끊어진 혈관상을 나타내는 제1 시각 효과를 표시하는 단계; 및 (e) 대장 혈관이 연속된 혈관상을 나타내는 제2 시각 효과를 표시하는 단계; 를 포함하고, (b) 단계는, 딥러닝 모델을 통해 각 구간 영상을 인식하되, 딥러닝 모델은 외부 어노테이터들로부터 획득된 복수의 대장 영상 내의 혈관 데이터와, 혈관상의 끊어짐 정도 및 혈관 패턴을 기반으로 기계 학습된 모델이다.</description><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>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>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNjLsKwkAQANNYiPoPC9aBPMR-vdvkDtbbcLlErELQsxINxP_HFH6A1UwxzDq5nykY0YBOg6beKoJK_KKBVLCuBiUsDhrha9NCMF662gD6YCurLDJYF4jZ1uQUpSdsSUOPreoYPTChd8tkm6we43OOux83yb6ioEwap_cQ52m8xVf8DBcpsqLMD9khP2Je_ld9AQ-XNVY</recordid><startdate>20230727</startdate><enddate>20230727</enddate><creator>KO, Ji Hwan</creator><scope>EVB</scope></search><sort><creationdate>20230727</creationdate><title>METHOD AND DEVICE FOR DETECTING COLON POLYPS THROUGH ARTIFICIAL INTELLIGENCE-BASED VASCULAR LEARNING</title><author>KO, Ji Hwan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_WO2023140416A13</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng ; fre ; kor</language><creationdate>2023</creationdate><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>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>KO, Ji Hwan</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>KO, Ji Hwan</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>METHOD AND DEVICE FOR DETECTING COLON POLYPS THROUGH ARTIFICIAL INTELLIGENCE-BASED VASCULAR LEARNING</title><date>2023-07-27</date><risdate>2023</risdate><abstract>A method for detecting colon polyps through artificial intelligence-based vascular learning disclosed in the present disclosure, implemented by a device, comprises the steps of: (a) receiving an image taken from an endoscope in real time; (b) recognizing each section image including the colonic mucosa and colonic blood vessels in the image; (c) determining whether or not the colonic blood vessel phase is discontinued for each section image; (d) displaying a first visual effect accounting for a blood vessel phase in which a colonic blood vessel is discontinued in each section image; and (e) displaying a second visual effect accounting for a blood vessel phase in which a colonic blood vessel is continued, wherein the recognizing of each section image in step (b) is achieved through a deep learning model, the deep learning model being a machine-learned model based on blood vessel data in a plurality of colon images obtained from external annotators, the degree of discontinuity of blood vessel phases, and blood vessel patterns. Un procédé de détection de polypes du côlon par un apprentissage vasculaire basé sur l'intelligence artificielle décrit dans la présente invention, mis en œuvre par un dispositif, comprend les étapes suivantes : (a) recevoir en temps réel une image prise par un endoscope ; (b) reconnaître chaque image de section comprenant la muqueuse du côlon et les vaisseaux sanguins du côlon dans l'image ; (c) déterminer si la phase de vaisseau sanguin du côlon est interrompue ou non pour chaque image de section ; (d) afficher un premier effet visuel représentant une phase de vaisseau sanguin dans laquelle un vaisseau sanguin du côlon est interrompu dans chaque image de section ; et (e) afficher un second effet visuel représentant une phase de vaisseau sanguin dans laquelle un vaisseau sanguin du côlon est continu, la reconnaissance de chaque image de section dans l'étape (b) étant réalisée au moyen d'un modèle d'apprentissage profond, le modèle d'apprentissage profond étant un modèle d'apprentissage automatique sur la base des données de vaisseaux sanguins dans une pluralité d'images du côlon obtenues à partir d'annotateurs externes, le degré de discontinuité des phases de vaisseaux sanguins et des motifs de vaisseaux sanguins. 본 개시에 공개된 인공지능 기반의 혈관 학습을 통한 대장 용종 검출 방법은 장치에 의해 수행되고, (a) 내시경으로부터 촬영된 영상을 실시간으로 수신하는 단계; (b) 영상 내에서 대장 점막 및 대장 혈관이 포함된 각 구간 영상을 인식하는 단계; (c) 각 구간 영상마다 대장 혈관상의 끊어짐 여부를 판단하는 단계; (d) 각 구간 영상 내에서 대장 혈관이 끊어진 혈관상을 나타내는 제1 시각 효과를 표시하는 단계; 및 (e) 대장 혈관이 연속된 혈관상을 나타내는 제2 시각 효과를 표시하는 단계; 를 포함하고, (b) 단계는, 딥러닝 모델을 통해 각 구간 영상을 인식하되, 딥러닝 모델은 외부 어노테이터들로부터 획득된 복수의 대장 영상 내의 혈관 데이터와, 혈관상의 끊어짐 정도 및 혈관 패턴을 기반으로 기계 학습된 모델이다.</abstract><oa>free_for_read</oa></addata></record>
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subjects DIAGNOSIS
HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA
HUMAN NECESSITIES
HYGIENE
IDENTIFICATION
INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS
MEDICAL OR VETERINARY SCIENCE
PHYSICS
SURGERY
title METHOD AND DEVICE FOR DETECTING COLON POLYPS THROUGH ARTIFICIAL INTELLIGENCE-BASED VASCULAR LEARNING
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