The Current Landscape of Artificial Intelligence in Imaging for Transcatheter Aortic Valve Replacement.

IF 1.3 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Current Radiology Reports Pub Date : 2024-01-01 Epub Date: 2024-10-10 DOI:10.1007/s40134-024-00431-w

Shawn Sun, Leslie Yeh, Amir Imanzadeh, Soheil Kooraki, Arash Kheradvar, Arash Bedayat

{"title":"The Current Landscape of Artificial Intelligence in Imaging for Transcatheter Aortic Valve Replacement.","authors":"Shawn Sun, Leslie Yeh, Amir Imanzadeh, Soheil Kooraki, Arash Kheradvar, Arash Bedayat","doi":"10.1007/s40134-024-00431-w","DOIUrl":null,"url":null,"abstract":"Purpose: This review explores the current landscape of AI applications in imaging for TAVR, emphasizing the potential and limitations of these tools for (1) automating the image analysis and reporting process, (2) improving procedural planning, and (3) offering additional insight into post-TAVR outcomes. Finally, the direction of future research necessary to bridge these tools towards clinical integration is discussed.Recent findings: Transcatheter aortic valve replacement (TAVR) has become a pivotal treatment option for select patients with severe aortic stenosis, and its indication for use continues to broaden. Noninvasive imaging techniques such as CTA and MRA have become routine for patient selection, preprocedural planning, and predicting the risk of complications. As the current methods for pre-TAVR image analysis are labor-intensive and have significant inter-operator variability, experts are looking towards artificial intelligence (AI) as a potential solution.Summary: AI has the potential to significantly enhance the planning, execution, and post-procedural follow up of TAVR. While AI tools are promising, the irreplaceable value of nuanced clinical judgment by skilled physician teams must not be overlooked. With continued research, collaboration, and careful implementation, AI can become an integral part in imaging for TAVR, ultimately improving patient care and outcomes.","PeriodicalId":37269,"journal":{"name":"Current Radiology Reports","volume":"12 11-12","pages":"113-120"},"PeriodicalIF":1.3000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11526784/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Radiology Reports","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s40134-024-00431-w","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/10/10 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}

引用次数: 0

Abstract

Purpose: This review explores the current landscape of AI applications in imaging for TAVR, emphasizing the potential and limitations of these tools for (1) automating the image analysis and reporting process, (2) improving procedural planning, and (3) offering additional insight into post-TAVR outcomes. Finally, the direction of future research necessary to bridge these tools towards clinical integration is discussed.

Recent findings: Transcatheter aortic valve replacement (TAVR) has become a pivotal treatment option for select patients with severe aortic stenosis, and its indication for use continues to broaden. Noninvasive imaging techniques such as CTA and MRA have become routine for patient selection, preprocedural planning, and predicting the risk of complications. As the current methods for pre-TAVR image analysis are labor-intensive and have significant inter-operator variability, experts are looking towards artificial intelligence (AI) as a potential solution.

Summary: AI has the potential to significantly enhance the planning, execution, and post-procedural follow up of TAVR. While AI tools are promising, the irreplaceable value of nuanced clinical judgment by skilled physician teams must not be overlooked. With continued research, collaboration, and careful implementation, AI can become an integral part in imaging for TAVR, ultimately improving patient care and outcomes.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

人工智能在经导管主动脉瓣置换术成像中的应用现状。

目的：这篇综述探讨了当前人工智能在 TAVR 成像中的应用，强调了这些工具在以下方面的潜力和局限性：（1）图像分析和报告流程自动化；（2）改善程序规划；以及（3）为 TAVR 术后结果提供更多洞察力。最后，讨论了为将这些工具与临床结合所需的未来研究方向：经导管主动脉瓣置换术（TAVR）已成为重度主动脉瓣狭窄患者的重要治疗选择，其适应症也在不断扩大。CTA 和 MRA 等无创成像技术已成为选择患者、进行术前规划和预测并发症风险的常规方法。摘要：人工智能有可能显著提高 TAVR 的计划、执行和术后随访。虽然人工智能工具大有可为，但也不能忽视技术娴熟的医生团队所做出的细致入微的临床判断所具有的不可替代的价值。通过持续的研究、合作和精心实施，人工智能可以成为 TAVR 成像中不可或缺的一部分，最终改善患者护理和预后。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Current Radiology Reports Medicine-Radiology, Nuclear Medicine and Imaging

CiteScore

1.60

自引率

14.30%

发文量

期刊介绍： Current Radiology Reports aims to offer expert review articles on the most significant recent developments in the field of radiology. By providing clear, insightful, balanced contributions, the journal intends to serve all those who use imaging technologies and related techniques to diagnose and treat disease. We accomplish this aim by appointing international authorities to serve as Section Editors in key subject areas across the field. Section Editors select topics for which leading experts contribute comprehensive review articles that emphasize new developments and recently published papers of major importance, highlighted by annotated reference lists. An Editorial Board of more than 20 internationally diverse members reviews the annual table of contents, ensures that topics include emerging research, and suggests topics of special importance to their country/region. Topics covered may include abdominal imaging (including virtual colonoscopy); cardiac imaging; clinical MRI; dual-source CT; interventional radiology; minimal invasive procedures and high-frequency focused ultrasound; musculoskeletal imaging; neuroimaging; nuclear medicine; pediatric imaging; PET, PET-CT, and PET-MRI; radiation exposure and reduction; translational molecular imaging; and ultrasound.