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Role of Artificial Intelligence Interpretation of Colposcopic Images in Cervical Cancer Screening

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dc.contributor.authorKim, Seongmin-
dc.contributor.authorLee, Hwajung-
dc.contributor.authorLee, Sanghoon-
dc.contributor.authorSong, Jae-Yun-
dc.contributor.authorLee, Jae-Kwan-
dc.contributor.authorLee, Nak-Woo-
dc.date.accessioned2022-04-19T00:40:21Z-
dc.date.available2022-04-19T00:40:21Z-
dc.date.created2022-04-18-
dc.date.issued2022-03-
dc.identifier.urihttps://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/55589-
dc.description.abstractThe accuracy of colposcopic diagnosis depends on the skill and proficiency of physicians. This study evaluated the feasibility of interpreting colposcopic images with the assistance of artificial intelligence (AI) for the diagnosis of high-grade cervical intraepithelial lesions. This study included female patients who underwent colposcopy-guided biopsy in 2020 at two institutions in the Republic of Korea. Two experienced colposcopists reviewed all images separately. The Cerviray AI (R) system (AIDOT, Seoul, Korea) was used to interpret the cervical images. AI demonstrated improved sensitivity with comparable specificity and positive predictive value when compared with the colposcopic impressions of each clinician. The areas under the curve were greater with combined impressions (both AI and that of the two colposcopists) of high-grade lesions, when compared with the individual impressions of each colposcopist. This study highlights the feasibility of the application of an AI system in cervical cancer screening. AI interpretation can be utilized as an assisting tool in combination with human colposcopic evaluation of exocervix.-
dc.language영어-
dc.language.isoen-
dc.publisherMDPI AG-
dc.titleRole of Artificial Intelligence Interpretation of Colposcopic Images in Cervical Cancer Screening-
dc.typeArticle-
dc.contributor.affiliatedAuthorLee, Hwajung-
dc.contributor.affiliatedAuthorLee, Sanghoon-
dc.contributor.affiliatedAuthorSong, Jae-Yun-
dc.contributor.affiliatedAuthorLee, Jae-Kwan-
dc.contributor.affiliatedAuthorLee, Nak-Woo-
dc.identifier.doi10.3390/healthcare10030468-
dc.identifier.scopusid2-s2.0-85125940469-
dc.identifier.wosid000775307100001-
dc.identifier.bibliographicCitationHealthcare, v.10, no.3-
dc.relation.isPartOfHealthcare-
dc.citation.titleHealthcare-
dc.citation.volume10-
dc.citation.number3-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassssci-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaHealth Care Sciences & Services-
dc.relation.journalWebOfScienceCategoryHealth Care Sciences & Services-
dc.relation.journalWebOfScienceCategoryHealth Policy & Services-
dc.subject.keywordPlusINTRAEPITHELIAL NEOPLASIA-
dc.subject.keywordPlusCYTOLOGY-
dc.subject.keywordPlusCLASSIFICATION-
dc.subject.keywordPlusREGRESSION-
dc.subject.keywordPlusDIAGNOSIS-
dc.subject.keywordPlusSMEAR-
dc.subject.keywordAuthorartificial intelligence-
dc.subject.keywordAuthorcervical cancer screening-
dc.subject.keywordAuthorcolposcopy-
dc.subject.keywordAuthordeep learning-
dc.subject.keywordAuthormachine learning-
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Lee, Nak Woo
Ansan Hospital (Department of Obstetrics and Gynecology, Ansan Hospital)
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