Diagnostic Accuracy and Generalizability of a Deep Learning-Based Fully Automated Algorithm for Coronary Artery Stenosis Detection on CCTA: A Multi-Centre Registry Study

Xu, Lixue; He, Yi; Luo, Nan; Guo, Ning; Hong, Min; Jia, Xibin; Wang, Zhenchang; Yang, Zhenghan

doi:10.3389/fcvm.2021.707508

Cited by 14 publications

(5 citation statements)

References 32 publications

(36 reference statements)

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“…Previous studies have assessed several AI applications for coronary artery assessment. In a retrospective multicenter study including 527 patients, a fully automated AI algorithm was not inferior to experts in the detection of coronary stenosis ≥50% and reduced the post-processing time significantly [17]. An accuracy of 84% and 86% for detecting ≥50% and ≥70% stenoses, respectively, was found in a CREDENCE substudy comparing AI quantitative coronary CTA with core lab quantitative coronary angiography [18].…”

Section: Discussionmentioning

confidence: 93%

Human AI Teaming for Coronary CT Angiography Assessment: Impact on Imaging Workflow and Diagnostic Accuracy

Andre,

Fortner,

Aurich

et al. 2023

Diagnostics

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As the number of coronary computed tomography angiography (CTA) examinations is expected to increase, technologies to optimize the imaging workflow are of great interest. The aim of this study was to investigate the potential of artificial intelligence (AI) to improve clinical workflow and diagnostic accuracy in high-volume cardiac imaging centers. A total of 120 patients (79 men; 62.4 (55.0–72.7) years; 26.7 (24.9–30.3) kg/m2) undergoing coronary CTA were randomly assigned to a standard or an AI-based (human AI) coronary analysis group. Severity of coronary artery disease was graded according to CAD-RADS. Initial reports were reviewed and changes were classified. Both groups were similar with regard to age, sex, body mass index, heart rate, Agatston score, and CAD-RADS. The time for coronary CTA assessment (142.5 (106.5–215.0) s vs. 195.0 (146.0–265.5) s; p < 0.002) and the total reporting time (274.0 (208.0–377.0) s vs. 350 (264.0–445.5) s; p < 0.02) were lower in the human AI than in the standard group. The number of cases with no, minor, or CAD-RADS relevant changes did not differ significantly between groups (52, 7, 1 vs. 50, 8, 2; p = 0.80). AI-based analysis significantly improves clinical workflow, even in a specialized high-volume setting, by reducing CTA analysis and overall reporting time without compromising diagnostic accuracy.

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Section: Discussionmentioning

confidence: 93%

Human AI Teaming for Coronary CT Angiography Assessment: Impact on Imaging Workflow and Diagnostic Accuracy

Andre,

Fortner,

Aurich

et al. 2023

Diagnostics

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“…Recently, the first reports have been published on the AI-based automated detection of coronary stenoses in CCTA ( 31 – 33 ). In a 2021 retrospective, multi-center study, the diagnostic accuracy and generalizability of an established DL-based fully automated algorithm in detecting coronary stenosis on CCTA has been shown to perform non-inferior to expert readers in detecting coronary stenoses ≥50% ( 34 ). In the vessel-based evaluation, the DL algorithm had a higher sensitivity (65.7%) and negative predictive value (NPV) (78.8%) than human expert readers.…”

Section: Clinical Applications Of Machine Learning Artificial Intelli...mentioning

confidence: 99%

Artificial intelligence in coronary computed tomography angiography: Demands and solutions from a clinical perspective

Baeßler

Götz

Antoniades

et al. 2023

Front. Cardiovasc. Med.

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Coronary computed tomography angiography (CCTA) is increasingly the cornerstone in the management of patients with chronic coronary syndromes. This fact is reflected by current guidelines, which show a fundamental shift towards non-invasive imaging - especially CCTA. The guidelines for acute and stable coronary artery disease (CAD) of the European Society of Cardiology from 2019 and 2020 emphasize this shift. However, to fulfill this new role, a broader availability in adjunct with increased robustness of data acquisition and speed of data reporting of CCTA is needed. Artificial intelligence (AI) has made enormous progress for all imaging methodologies concerning (semi)-automatic tools for data acquisition and data post-processing, with outreach toward decision support systems. Besides onco- and neuroimaging, cardiac imaging is one of the main areas of application. Most current AI developments in the scenario of cardiac imaging are related to data postprocessing. However, AI applications (including radiomics) for CCTA also should enclose data acquisition (especially the fact of dose reduction) and data interpretation (presence and extent of CAD). The main effort will be to integrate these AI-driven processes into the clinical workflow, and to combine imaging data/results with further clinical data, thus - beyond the diagnosis of CAD- enabling prediction and forecast of morbidity and mortality. Furthermore, data fusing for therapy planning (e.g., invasive angiography/TAVI planning) will be warranted. The aim of this review is to present a holistic overview of AI applications in CCTA (including radiomics) under the umbrella of clinical workflows and clinical decision-making. The review first summarizes and analyzes applications for the main role of CCTA, i.e., to non-invasively rule out stable coronary artery disease. In the second step, AI applications for additional diagnostic purposes, i.e., to improve diagnostic power (CAC = coronary artery classifications), improve differential diagnosis (CT-FFR and CT perfusion), and finally improve prognosis (again CAC plus epi- and pericardial fat analysis) are reviewed.

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“…A prospective study of patients with chest pain undergoing CCTA found that structured reporting platforms with the automated calculation of the Coronary Artery Disease Reporting and Data System (CAD-RADS) scores outperform manual classification by preventing human errors, improving data quality, and supporting the standardization of clinical decision-making [199]. For CCTA, a retrospective study found that a deep learningbased algorithm helped streamline CCTA reconstruction and interpretation workflows for CAD patients, significantly improving time efficiency and diagnostic consistency [200]. Deep learning algorithms for the automated interpretation of echocardiographic images offer the opportunity to remove the burden for highly trained individuals to conduct manual image analysis [201] and may eliminate some of the intensive training and skill maintenance required of operators [202] and reduce human error [203].…”

Section: Evolving Solutionsmentioning

confidence: 99%

Challenges and Burdens in the Coronary Artery Disease Care Pathway for Patients Undergoing Percutaneous Coronary Intervention: A Contemporary Narrative Review

Kodeboina

Piayda

Jenniskens

et al. 2023

IJERPH

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Clinical and economic burdens exist within the coronary artery disease (CAD) care pathway despite advances in diagnosis and treatment and the increasing utilization of percutaneous coronary intervention (PCI). However, research presenting a comprehensive assessment of the challenges across this pathway is scarce. This contemporary review identifies relevant studies related to inefficiencies in the diagnosis, treatment, and management of CAD, including clinician, patient, and economic burdens. Studies demonstrating the benefits of integration and automation within the catheterization laboratory and across the CAD care pathway were also included. Most studies were published in the last 5–10 years and focused on North America and Europe. The review demonstrated multiple potentially avoidable inefficiencies, with a focus on access, appropriate use, conduct, and follow-up related to PCI. Inefficiencies included misdiagnosis, delays in emergency care, suboptimal testing, longer procedure times, risk of recurrent cardiac events, incomplete treatment, and challenges accessing and adhering to post-acute care. Across the CAD pathway, this review revealed that high clinician burnout, complex technologies, radiation, and contrast media exposure, amongst others, negatively impact workflow and patient care. Potential solutions include greater integration and interoperability between technologies and systems, improved standardization, and increased automation to reduce burdens in CAD and improve patient outcomes.

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Diagnostic Accuracy and Generalizability of a Deep Learning-Based Fully Automated Algorithm for Coronary Artery Stenosis Detection on CCTA: A Multi-Centre Registry Study

Cited by 14 publications

References 32 publications

Human AI Teaming for Coronary CT Angiography Assessment: Impact on Imaging Workflow and Diagnostic Accuracy

Human AI Teaming for Coronary CT Angiography Assessment: Impact on Imaging Workflow and Diagnostic Accuracy

Artificial intelligence in coronary computed tomography angiography: Demands and solutions from a clinical perspective

Challenges and Burdens in the Coronary Artery Disease Care Pathway for Patients Undergoing Percutaneous Coronary Intervention: A Contemporary Narrative Review

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