Prediction of Coronary Stent Underexpansion by Pre-Procedural Intravascular Ultrasound–Based Deep Learning

Min, Hyun‐Seok; Ryu, Dongmin; Kang, Soo-Jin; Lee, June-Goo; Yoo, BS Ji Hyeong; Cho, BS Hyungjoo; Kang, Do‐Yoon; Lee, Pil Hyung; Ahn, Jung‐Min; Park, Duk‐Woo; Lee, Seung-Whan; Kim, Young‐Hak; Lee, Cheol Whan; Park, Seong-Wook; Park, Seung‐Jung

doi:10.1016/j.jcin.2021.01.033

Cited by 37 publications

(31 citation statements)

References 11 publications

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“…Approximately, half of the included studies were conducted in years 2020 (12/46, 26.0%) ( 14 , 15 , 17 , 19 , 20 , 25 , 26 , 30 , 36 , 37 , 45 , 56 ) and 2021 (12/46, 26.0%) ( 13 , 16 , 21 , 24 , 29 , 33 , 39 , 40 , 43 , 46 , 50 , 53 ) ( Figure 2 ). The corresponding authors of the included studies were from 13 countries, including the United States (14/46, 30.3%) ( 12 , 16 , 21 , 22 , 29 , 31 , 34 , 36 , 39 , 41 , 42 , 48 , 49 , 53 ), China (11/46, 23.9%) ( 15 , 19 , 20 , 35 , 37 , 38 , 40 , 45 , 46 , 52 , 54 ), Japan (8/46, 17.3%) ( 17 , 23 , 25 , 27 , 28 , 32 , 55 , 56 ), Greece (2/46, 4.3%) ( 13 , 14 ), and Netherlands (2/46, 4.3%) ( 44 , 47 ), whereas Italy ( 26 ), Canada ( 11 ), India ( 30 ), Ko...…”

Section: Resultsmentioning

confidence: 99%

“…For data sources, private data (data collected by centers) (35/46, 76.0%) ( 11 – 21 , 23 , 24 , 27 , 33 – 50 , 53 , 54 , 56 ) were the most commonly used data sources for the development of AI models. Except for one article for which the data source is unclear ( 28 ), public data (10/46, 21.7%) were the other sources of data for AI models ( 22 , 25 , 26 , 29 – 32 , 51 , 52 , 55 ).…”

Section: Resultsmentioning

confidence: 99%

Section: Characteristics Of the Included Studiesmentioning

confidence: 99%

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Using artificial intelligence in the development of diagnostic models of coronary artery disease with imaging markers: A scoping review

Wang

Wang²,

Wang³

et al. 2022

Front. Cardiovasc. Med.

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BackgroundCoronary artery disease (CAD) is a progressive disease of the blood vessels supplying the heart, which leads to coronary artery stenosis or obstruction and is life-threatening. Early diagnosis of CAD is essential for timely intervention. Imaging tests are widely used in diagnosing CAD, and artificial intelligence (AI) technology is used to shed light on the development of new imaging diagnostic markers.ObjectiveWe aim to investigate and summarize how AI algorithms are used in the development of diagnostic models of CAD with imaging markers.MethodsThis scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guideline. Eligible articles were searched in PubMed and Embase. Based on the predefined included criteria, articles on coronary heart disease were selected for this scoping review. Data extraction was independently conducted by two reviewers, and a narrative synthesis approach was used in the analysis.ResultsA total of 46 articles were included in the scoping review. The most common types of imaging methods complemented by AI included single-photon emission computed tomography (15/46, 32.6%) and coronary computed tomography angiography (15/46, 32.6%). Deep learning (DL) (41/46, 89.2%) algorithms were used more often than machine learning algorithms (5/46, 10.8%). The models yielded good model performance in terms of accuracy, sensitivity, specificity, and AUC. However, most of the primary studies used a relatively small sample (n < 500) in model development, and only few studies (4/46, 8.7%) carried out external validation of the AI model.ConclusionAs non-invasive diagnostic methods, imaging markers integrated with AI have exhibited considerable potential in the diagnosis of CAD. External validation of model performance and evaluation of clinical use aid in the confirmation of the added value of markers in practice.Systematic review registration[https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022306638], identifier [CRD42022306638].

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Characteristics Of the Included Studiesmentioning

confidence: 99%

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Using artificial intelligence in the development of diagnostic models of coronary artery disease with imaging markers: A scoping review

Wang

Wang²,

Wang³

et al. 2022

Front. Cardiovasc. Med.

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“…Studies have demonstrated the postprocedural minimum stent area (MSA) and IVUS-measured stent length to be independent predictors for in-stent restenosis [ 130 , 140 , 141 , 142 , 143 ]. Min et al devised a deep-learning model to predict stent underexpansion based on pre-PCI IVUS frames [ 144 ]. They evaluated 618 coronary lesions from 618 patients undergoing pre- and postprocedural IVUS and divided them into training and testing sets in a 5:1 ratio.…”

Section: Artificial Intelligence In the Field Of Intracoronary Imagingmentioning

confidence: 99%

Current and Future Applications of Artificial Intelligence in Coronary Artery Disease

et al. 2022

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Cardiovascular diseases (CVDs) carry significant morbidity and mortality and are associated with substantial economic burden on healthcare systems around the world. Coronary artery disease, as one disease entity under the CVDs umbrella, had a prevalence of 7.2% among adults in the United States and incurred a financial burden of 360 billion US dollars in the years 2016–2017. The introduction of artificial intelligence (AI) and machine learning over the last two decades has unlocked new dimensions in the field of cardiovascular medicine. From automatic interpretations of heart rhythm disorders via smartwatches, to assisting in complex decision-making, AI has quickly expanded its realms in medicine and has demonstrated itself as a promising tool in helping clinicians guide treatment decisions. Understanding complex genetic interactions and developing clinical risk prediction models, advanced cardiac imaging, and improving mortality outcomes are just a few areas where AI has been applied in the domain of coronary artery disease. Through this review, we sought to summarize the advances in AI relating to coronary artery disease, current limitations, and future perspectives.

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“…Prospective multi-center trials have demonstrated that IVUS-guided PCI resulted in better clinical outcomes relative to angiography-guided PCI 10 . In addition, IVUS is helpful in predicting specific adverse events such as slow flow, stent edge dissection, and stent under-expansion during the procedures 11 – 13 . However, the association between IVUS findings and slow flow has not been investigated in calcified lesions requiring RA.…”

Section: Introductionmentioning

confidence: 99%

Intravascular ultrasound-factors associated with slow flow following rotational atherectomy in heavily calcified coronary artery

Jinnouchi

Sakakura

Taniguchi

et al. 2022

Sci Rep

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Intravascular ultrasound (IVUS) can provide useful information in patients undergoing complex percutaneous coronary intervention with rotational atherectomy (RA). The association between IVUS findings and slow flow following rotational atherectomy (RA) has not been investigated, although slow flow has been shown to be an unfavorable sign with worse outcomes. The aim of this study was to determine the IVUS-factors associated with slow flow just after RA. We retrospectively enrolled 290 lesions (5316 IVUS-frames) with RA, which were divided into the slow flow group (n = 43 with 1029 IVUS-frames) and the non-slow flow group (n = 247 with 4287 IVUS-frames) based on the presence of slow flow. Multivariate regression analysis assessed the IVUS-factors associated with slow flow. Slow flow was significantly associated with long lesion length, the maximum number of reverberations [odds ratio (OR) 1.49; 95% confidence interval (CI) 1.07–2.07, p = 0.02] and nearly circumferential calcification at minimal lumen area (MLA) (≥ 300°) (OR, 2.21; 95% CI 1.13–4.32; p = 0.02). According to the maximum number of reverberations, the incidence of slow flow was 2.2% (n = 0), 11.9% (n = 1), 19.5% (n = 2), 22.5% (n = 3), and 44.4% (n = 4). In conclusion, IVUS findings such as longer lesion length, the maximum number of reverberations, and the greater arc of calcification at MLA may predict slow flow after RA. The operators need to pay more attention to the presence of reverberations to enhance the procedure safety.

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Prediction of Coronary Stent Underexpansion by Pre-Procedural Intravascular Ultrasound–Based Deep Learning

Cited by 37 publications

References 11 publications

Using artificial intelligence in the development of diagnostic models of coronary artery disease with imaging markers: A scoping review

Using artificial intelligence in the development of diagnostic models of coronary artery disease with imaging markers: A scoping review

Current and Future Applications of Artificial Intelligence in Coronary Artery Disease

Intravascular ultrasound-factors associated with slow flow following rotational atherectomy in heavily calcified coronary artery

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