Automatic coronary calcium scoring in chest CT using a deep neural network in direct comparison with non-contrast cardiac CT: A validation study.

PURPOSE: To evaluate deep-learning based calcium quantification on Chest CT scans compared with manual evaluation, and to enable interpretation in terms of the traditional Agatston score on dedicated Cardiac CT. METHODS: Automated calcium quantification was performed using a combination of deep-learning convolution neural networks with a ResNet-architecture for image features and a fully connected neural network for spatial coordinate features. Calcifications were identified automatically, after which the algorithm automatically excluded all non-coronary calcifications using coronary probability maps and aortic segmentation. The algorithm was first trained on cardiac-CTs and refined on non-triggered chest-CTs. This study used on 95 patients (cohort 1), who underwent both dedicated calcium scoring and chest-CT acquisitions using the Agatston score as reference standard and 168 patients (cohort 2) who underwent chest-CT only using qualitative expert assessment for external validation. Results from the deep-learning model were compared to Agatston-scores(cardiac-CTs) and manually determined calcium volumes(chest-CTs) and risk classifications. RESULTS: In cohort 1, the Agatston score and AI determined calcium volume shows high correlation with a correlation coefficient of 0.921(p < 0.001) and R2 of 0.91. According to the Agatston categories, a total of 67(70 %) were correctly classified with a sensitivity of 91 % and specificity of 92 % in detecting presence of coronary calcifications. Manual determined calcium volume on chest-CT showed excellent correlation with the AI volumes with a correlation coefficient of 0.923(p < 0.001) and R2 of 0.96, no significant difference was found (p = 0.247). According to qualitative risk classifications in cohort 2, 138(82 %) cases were correctly classified with a k-coefficient of 0.74, representing good agreement. All wrongly classified scans (30(18 %)) were attributed to an adjacent category. CONCLUSION: Artificial intelligence based calcium quantification on chest-CTs shows good correlation compared to reference standards. Fully automating this process may reduce evaluation time and potentially optimize clinical calcium scoring without additional acquisitions.

Machine Learning and Coronary Artery Calcium Scoring.

PURPOSE OF REVIEW: To summarize current artificial intelligence (AI)-based applications for coronary artery calcium scoring (CACS) and their potential clinical impact. RECENT FINDINGS: Recent evolution of AI-based technologies in medical imaging has accelerated progress in CACS performed in diverse types of CT examinations, providing promising results for future clinical application in this field. CACS plays a key role in risk stratification of coronary artery disease (CAD) and patient management. Recent emergence of AI algorithms, particularly deep learning (DL)-based applications, have provided considerable progress in CACS. Many investigations have focused on the clinical role of DL models in CACS and showed excellent agreement between those algorithms and manual scoring, not only in dedicated coronary calcium CT but also in coronary CT angiography (CCTA), low-dose chest CT, and standard chest CT. Therefore, the potential of AI-based CACS may become more influential in the future.

Assessing the value of coronary artery computed tomography as the first-line anatomical test for stable patients with indications for invasive angiography due to suspected coronary artery disease. Initial cost analysis in the CAT-CAD randomized trial.

BACKGROUND: Clinical and safety outcomes of the strategy employing coronary computed tomography angiography (CCTA) as the first-choice imaging test have recently been demonstrated in the recently published CAT-CAD randomized, prospective, single-center study. Based on prospectively collected data in this patient population, we aimed to perform an initial cost analysis of this approach. METHODS: 120 participants of the CAT-CAD trial (age:60.6 ±â€¯7.9 years, 35% female) were included in the analysis. We analyzed medical resource use during the diagnostic and therapeutic episode of care. We prospectively estimated the cumulative cost for each strategy by multiplying the number of resources by standardized costs in accordance to medical databases and the 2015 Procedural Reimbursement Payment Guide. RESULTS: The total cost of coronary artery disease (CAD) diagnosis was significantly lower in the CCTA group as compared to the direct invasive coronary angiography (ICA) group ($50,176 vs $137,032) with corresponding per-patient cost of $836 vs $2,284, respectively. Similarly, the entire diagnostic and therapeutic episode of care was significantly less expensive in the CCTA group ($227,622 vs $502,827) with corresponding per-patient cost of $4630 vs $8,380, respectively. Overall, the application of CCTA as a first-line diagnostic test in stable patients with indications to ICA resulted in a 63% reduction of CAD diagnosis costs and a 55% reduction composite of diagnosis and treatment costs during 90-days follow-up. CONCLUSIONS: Application of CCTA as the first-line anatomic test in patients with suspected significant CAD decreased the total costs of diagnosis. This is likely attributable to reduced numbers of invasive tests and hospitalisations. Initial cost analysis of the CAT-CAD randomized trial suggests that this approach may provide significant cost savings for the entire health system.