Dataset Information

Serial analysis of coronary artery disease progression by artificial intelligence assisted coronary computed tomography angiography: early clinical experience.

ABSTRACT:

Background

Studies have shown that quantitative evaluation of coronary artery plaque on Coronary Computed Tomography Angiography (CCTA) can identify patients at risk of cardiac events. Recent demonstration of artificial intelligence (AI) assisted CCTA shows that it allows for evaluation of CAD and plaque characteristics. Based on publications to date, we are the first group to perform AI augmented CCTA serial analysis of changes in coronary plaque characteristics over 13 years. We evaluated whether AI assisted CCTA can accurately assess changes in coronary plaque progression, which has potential clinical prognostic value in CAD management.

Case presentation

51-year-old male with hypertension, hyperlipidemia and family history of myocardial infarction, underwent CCTA exams for anginal symptom evaluation and CAD assessment. 5 CCTAs were performed between 2008 and 2021. Quantitative atherosclerosis plaque characterization (APC) using an AI platform (Cleerly), was performed to assess CAD burden. Total plaque volume (TPV) change-over-time demonstrated decreasing low-density non-calcified plaque (LD-NCP) with increasing overall NCP and calcified-plaque (CP). Examination of individual segments revealed a proximal-LAD lesion with decreasing NCP over-time and increasing CP. In contrast, although the D2/D1/ramus lesions showed increasing stenosis, CP, and total plaque, there were no significant differences in NCP over-time, with stable NCP and increased CP. Remarkably, we also consistently visualized small plaques, which typically readers may interpret as false positives due to artifacts. But in this case, they reappeared each study in the same locations, generally progressing in size and demonstrating expected plaque transformation over-time.

Conclusions

We performed the first AI augmented CCTA based serial analysis of changes in coronary plaque characteristics over 13 years. We were able to consistently assess progression of plaque volumes, stenosis, and APCs with this novel methodology. We found a significant increase in TPV composed of decreasing LD-NCP, and increasing NCP and CP, with variations in the evolution of APCs between vessels. Although the significance of evolving APCs needs to be investigated, this case demonstrates AI-based CCTA analysis can serve as valuable clinical tool to accurately define unique CAD characteristics over time. Prospective trails are needed to assess whether quantification of APCs provides prognostic capabilities to improve clinical care.

SUBMITTER: Cho GW

PROVIDER: S-EPMC9701371 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Publications

Serial analysis of coronary artery disease progression by artificial intelligence assisted coronary computed tomography angiography: early clinical experience.

Cho Geoffrey W GW Anderson Lauren L Quesada Carlos G CG Jennings Robert S RS Min James K JK Earls James P JP Karlsberg Ronald P RP

BMC cardiovascular disorders 20221126 1

<h4>Background</h4>Studies have shown that quantitative evaluation of coronary artery plaque on Coronary Computed Tomography Angiography (CCTA) can identify patients at risk of cardiac events. Recent demonstration of artificial intelligence (AI) assisted CCTA shows that it allows for evaluation of CAD and plaque characteristics. Based on publications to date, we are the first group to perform AI augmented CCTA serial analysis of changes in coronary plaque characteristics over 13 years. We evalua ...[more]

PMID: 36435762

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Project description:ObjectivesCoronary artery calcium (CAC) scores derived from computed tomography (CT) scans are used for cardiovascular risk stratification. Artificial intelligence (AI) can assist in CAC quantification and potentially reduce the time required for human analysis. This study aimed to develop and evaluate a fully automated model that identifies and quantifies CAC.MethodsFully convolutional neural networks for automated CAC scoring were developed and trained on 2439 cardiac CT scans and validated using 771 scans. The model was tested on an independent set of 1849 cardiac CT scans. Agatston CAC scores were further categorised into five risk categories (0, 1-10, 11-100, 101-400, and > 400). Automated scores were compared to the manual reference standard (level 3 expert readers).ResultsOf 1849 scans used for model testing (mean age 55.7 ± 10.5 years, 49% males), the automated model detected the presence of CAC in 867 (47%) scans compared with 815 (44%) by human readers (p = 0.09). CAC scores from the model correlated very strongly with the manual score (Spearman's r = 0.90, 95% confidence interval [CI] 0.89-0.91, p < 0.001 and intraclass correlation coefficient = 0.98, 95% CI 0.98-0.99, p < 0.001). The model classified 1646 (89%) into the same risk category as human observers. The Bland-Altman analysis demonstrated little difference (1.69, 95% limits of agreement: -41.22, 44.60) and there was almost excellent agreement (Cohen's κ = 0.90, 95% CI 0.88-0.91, p < 0.001). Model analysis time was 13.1 ± 3.2 s/scan.ConclusionsThis artificial intelligence-based fully automated CAC scoring model shows high accuracy and low analysis times. Its potential to optimise clinical workflow efficiency and patient outcomes requires evaluation.Key points• Coronary artery calcium (CAC) scores are traditionally assessed using cardiac computed tomography and require manual input by human operators to identify calcified lesions. • A novel artificial intelligence (AI)-based model for fully automated CAC scoring was developed and tested on an independent dataset of computed tomography scans, showing very high levels of correlation and agreement with manual measurements as a reference standard. • AI has the potential to assist in the identification and quantification of CAC, thereby reducing the time required for human analysis.

Project description:BackgroundThe commercial coronary computed tomographic angiography artificial intelligence (CCTA-AI) platform has made great progress in clinical application. However, research is needed to elucidate the current stage of commercial AI platforms and the role of radiologists. This study compared the diagnostic performance of the commercial CCTA-AI platform with that of a reader based on a multicenter and multidevice sample.MethodsA total of 318 patients with suspected coronary artery disease (CAD) who underwent both CCTA and invasive coronary angiography (ICA) were included in a multicenter and multidevice validation cohort between 2017 and 2021. The commercial CCTA-AI platform was used to automatically assess coronary artery stenosis by using ICA findings as the gold standard. The CCTA reader was completed by radiologists. The diagnostic performance of the commercial CCTA-AI platform and CCTA reader was evaluated at the patient and segment levels. The cutoff values of models 1 and 2 were 50% and 70% stenosis, respectively.ResultsIt took 20.4 seconds to accomplish post-processing per patient when using the CCTA-AI platform, which was significantly shorter than the time taken to complete this task with the CCTA reader (1,112.1 s). In the patient-based analysis, the area under the curve (AUC) was 0.85 using the CCTA-AI platform and 0.61 using the CCTA reader in model 1 (stenosis ratio: 50%). In contrast, the AUC was 0.78 using the CCTA-AI platform and 0.64 using the CCTA reader in model 2 (stenosis ratio: 70%). In the segment-based analysis, the AUCs of CCTA-AI were slightly better than those of the readers. The negative predictive value (NPV) increased from model 1 to model 2. Furthermore, the diagnostic performance was better for larger-diameter arteries.ConclusionsThe commercial CCTA-AI platform may provide a feasible solution for the diagnosis of coronary artery stenosis, and it has a diagnostic performance that is slightly better than that of a radiologist with a moderate level of experience (5-10 years of experience).

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Dataset Information

Serial analysis of coronary artery disease progression by artificial intelligence assisted coronary computed tomography angiography: early clinical experience.

Background

Case presentation

Conclusions

Publications

Serial analysis of coronary artery disease progression by artificial intelligence assisted coronary computed tomography angiography: early clinical experience.

Similar Datasets

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