Handling Label Uncertainty on the Example of Automatic Detection of Shepherd's Crook RCA in Coronary CT Angiography

Coronary artery disease (CAD) is often treated minimally invasively with a catheter being inserted into the diseased coronary vessel. If a patient exhibits a Shepherd's Crook (SC) Right Coronary Artery (RCA) - an anatomical norm variant of the coronary vasculature - the complexity of this procedure is increased. Automated reporting of this variant from coronary CT angiography screening would ease prior risk assessment. We propose a 1D convolutional neural network which leverages a sequence of residual dilated convolutions to automatically determine this norm variant from a prior extracted vessel centerline. As the SC RCA is not clearly defined with respect to concrete measurements, labeling also includes qualitative aspects. Therefore, 4.23% samples in our dataset of 519 RCA centerlines were labeled as unsure SC RCAs, with 5.97% being labeled as sure SC RCAs. We explore measures to handle this label uncertainty, namely global/model-wise random assignment, exclusion, and soft label assignment. Furthermore, we evaluate how this uncertainty can be leveraged for the determination of a rejection class. With our best configuration, we reach an area under the receiver operating characteristic curve (AUC) of 0.938 on confident labels. Moreover, we observe an increase of up to 0.020 AUC when rejecting 10% of the data and leveraging the labeling uncertainty information in the exclusion process.Comment: Accepted at ISBI 202

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

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 p 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

Simultaneous assessment of heart and lungs with gated high-pitch ultra-low dose chest CT using artificial intelligence-based calcium scoring

Purpose: The combined testing for coronary artery and pulmonary diseases is of clinical interest as risk factors are shared. In this study, a novel ECG-gated tin-filtered ultra-low dose chest CT protocol (GCCT) for integrated heart and lung acquisition and the applicability of artificial intelligence (AI)-based coronary artery calcium scoring were assessed. Methods: In a clinical registry of 10481 patients undergoing heart and lung CT, GCCT was applied in 44 patients on a dual-source CT. Coronary calcium scans (CCS) with 120 kVp, 100 kVp, and tin-filtered 100 kVp (Sn100) of controls, matched with regard to age, sex, and body-mass index, were retrieved from the registry (ntotal=176, 66.5 (59.4–74.0) years, 52 men). Automatic tube current modulation was used in all scans. In 20 patients undergoing GCCT and Sn100 CCS, Agatston scores were measured both semi-automatically by experts and by AI, and classified into six groups (0, <10, <100, <400, <1000, ≥1000). Results: Effective dose decreased significantly from 120 kVp CCS (0.50 (0.41–0.61) mSv) to 100 kVp CCS (0.34 (0.26–0.37) mSv) to Sn100 CCS (0.14 (0.11–0.17) mSv). GCCT showed higher values (0.28 (0.21–0.32) mSv) than Sn100 CCS but lower than 120 kVp and 100 kVp CCS (all p < 0.05) despite greater scan length. Agatston scores correlated strongly between GCCT and Sn100 CCS in semi-automatic and AI-based measurements (both ρ = 0.98, p < 0.001) resulting in high agreement in Agatston score classification (κ = 0.97, 95% CI 0.92–1.00; κ = 0.89, 95% CI 0.79–0.99). Regarding chest findings, further diagnostic steps were recommended in 28 patients. Conclusions: GCCT allows for reliable coronary artery disease and lung cancer screening with ultra-low radiation exposure. GCCT-derived Agatston score shows excellent agreement with standard CCS, resulting in equivalent risk stratification