Ruben G A van der Waerden, Rick H J A Volleberg, Thijs J Luttikholt, Pierandrea Cancian, Joske L van der Zande, Gregg W Stone, Niels R Holm, Elvin Kedhi, Javier Escaned, Dario Pellegrini, Giulio Guagliumi, Shamir R Mehta, Natalia Pinilla-Echeverri, Raúl Moreno, Lorenz Räber, Tomasz Roleder, Bram van Ginneken, Clara I Sánchez, Ivana Išgum, Niels van Royen, Jos Thannhauser
{"title":"Artificial intelligence for the analysis of intracoronary optical coherence tomography images: a systematic review.","authors":"Ruben G A van der Waerden, Rick H J A Volleberg, Thijs J Luttikholt, Pierandrea Cancian, Joske L van der Zande, Gregg W Stone, Niels R Holm, Elvin Kedhi, Javier Escaned, Dario Pellegrini, Giulio Guagliumi, Shamir R Mehta, Natalia Pinilla-Echeverri, Raúl Moreno, Lorenz Räber, Tomasz Roleder, Bram van Ginneken, Clara I Sánchez, Ivana Išgum, Niels van Royen, Jos Thannhauser","doi":"10.1093/ehjdh/ztaf005","DOIUrl":null,"url":null,"abstract":"<p><p>Intracoronary optical coherence tomography (OCT) is a valuable tool for, among others, periprocedural guidance of percutaneous coronary revascularization and the assessment of stent failure. However, manual OCT image interpretation is challenging and time-consuming, which limits widespread clinical adoption. Automated analysis of OCT frames using artificial intelligence (AI) offers a potential solution. For example, AI can be employed for automated OCT image interpretation, plaque quantification, and clinical event prediction. Many AI models for these purposes have been proposed in recent years. However, these models have not been systematically evaluated in terms of model characteristics, performances, and bias. We performed a systematic review of AI models developed for OCT analysis to evaluate the trends and performances, including a systematic evaluation of potential sources of bias in model development and evaluation.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 2","pages":"270-284"},"PeriodicalIF":3.9000,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914731/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"European heart journal. Digital health","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/ehjdh/ztaf005","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/3/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Intracoronary optical coherence tomography (OCT) is a valuable tool for, among others, periprocedural guidance of percutaneous coronary revascularization and the assessment of stent failure. However, manual OCT image interpretation is challenging and time-consuming, which limits widespread clinical adoption. Automated analysis of OCT frames using artificial intelligence (AI) offers a potential solution. For example, AI can be employed for automated OCT image interpretation, plaque quantification, and clinical event prediction. Many AI models for these purposes have been proposed in recent years. However, these models have not been systematically evaluated in terms of model characteristics, performances, and bias. We performed a systematic review of AI models developed for OCT analysis to evaluate the trends and performances, including a systematic evaluation of potential sources of bias in model development and evaluation.
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