Emerging Trends in Artificial Intelligence-Based Urological Imaging Technologies and Practical Applications
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Hyun Suh | - |
dc.contributor.author | Kim, Eun Joung | - |
dc.contributor.author | Kim, Jung Yoon | - |
dc.date.accessioned | 2023-12-21T05:30:20Z | - |
dc.date.available | 2023-12-21T05:30:20Z | - |
dc.date.issued | 2023-11 | - |
dc.identifier.issn | 2093-4777 | - |
dc.identifier.issn | 2093-6931 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/89719 | - |
dc.description.abstract | The integration of artificial intelligence (AI) into medical imaging has notably expanded its significance within urology. AI applications offer a broad spectrum of utilities in this domain, ranging from precise diagnosis achieved through image segmentation and anomaly detection to improved procedural assistance in biopsies and surgical interventions. Although challenges persist concerning data security, transparency, and integration into existing clinical workflows, extensive research has been conducted on AI-assisted imaging technologies while recognizing their potential to reshape urological practices. This review paper outlines current AI techniques employed for image analysis to offer an overview of the latest technological trends and applications in the field of urology. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | KOREAN CONTINENCE SOC | - |
dc.title | Emerging Trends in Artificial Intelligence-Based Urological Imaging Technologies and Practical Applications | - |
dc.type | Article | - |
dc.identifier.wosid | 001111259500004 | - |
dc.identifier.doi | 10.5213/inj.2346286.143 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL NEUROUROLOGY JOURNAL, v.27, pp S73 - S81 | - |
dc.identifier.kciid | ART003020637 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.scopusid | 2-s2.0-85179608038 | - |
dc.citation.endPage | S81 | - |
dc.citation.startPage | S73 | - |
dc.citation.title | INTERNATIONAL NEUROUROLOGY JOURNAL | - |
dc.citation.volume | 27 | - |
dc.type.docType | Review | - |
dc.publisher.location | 대한민국 | - |
dc.subject.keywordAuthor | Urology | - |
dc.subject.keywordAuthor | Medical imaging | - |
dc.subject.keywordAuthor | Artificial intelligence | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.subject.keywordAuthor | Applications | - |
dc.subject.keywordPlus | BLADDER-CANCER | - |
dc.subject.keywordPlus | TEXTURE ANALYSIS | - |
dc.subject.keywordPlus | SEGMENTATION | - |
dc.relation.journalResearchArea | Urology & Nephrology | - |
dc.relation.journalWebOfScienceCategory | Urology & Nephrology | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.