Fully automated deep learning powered calcium scoring in patients undergoing myocardial perfusion imaging

Sartoretti, Thomas; Gennari, Antonio Giulio; Sartoretti, Elisabeth; Skawran, Stephan; Maurer, Alexander; Buechel, Ronny R.; Messerli, Michael

doi:10.1007/s12350-022-02940-7

Cited by 12 publications

(9 citation statements)

References 24 publications

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“…Our study has the following limitations: First, this was a retrospective single-center study with a limited number of subjects. Nonetheless, despite the specific and selective inclusion criteria, we achieved a sample size comparable to that of similar studies [ 18 , 25 ].…”

Section: Discussionmentioning

confidence: 91%

“…In brief, the software was developed based on a 3-dimensional U-net architecture using non-enhanced cardiac CT scans acquired from multiple vendors and scanners. A more detailed description of the network architecture and the algorithm, including information on initial training datasets and validation procedures, can be found elsewhere [ 17 , 18 ]. No training data were included in this current study [ 14 , 17 ].…”

Section: Methodsmentioning

confidence: 99%

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Diagnostic Value of Fully Automated Artificial Intelligence Powered Coronary Artery Calcium Scoring from 18F-FDG PET/CT

et al. 2022

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Objectives: The objective of this study was to assess the feasibility and accuracy of a fully automated artificial intelligence (AI) powered coronary artery calcium scoring (CACS) method on ungated CT in oncologic patients undergoing 18F-FDG PET/CT. Methods: A total of 100 oncologic patients examined between 2007 and 2015 were retrospectively included. All patients underwent 18F-FDG PET/CT and cardiac SPECT myocardial perfusion imaging (MPI) by 99mTc-tetrofosmin within 6 months. CACS was manually performed on non-contrast ECG-gated CT scans obtained from SPECT-MPI (i.e., reference standard). Additionally, CACS was performed using a cloud-based, user-independent tool (AI-CACS) on ungated CT scans from 18F-FDG-PET/CT examinations. Agatston scores from the manual CACS and AI-CACS were compared. Results: On a per-patient basis, the AI-CACS tool achieved a sensitivity and specificity of 85% and 90% for the detection of CAC. Interscore agreement of CACS between manual CACS and AI-CACS was 0.88 (95% CI: 0.827, 0.918). Interclass agreement of risk categories was 0.8 in weighted Kappa analysis, with a reclassification rate of 44% and an underestimation of one risk category by AI-CACS in 39% of cases. On a per-vessel basis, interscore agreement of CAC scores ranged from 0.716 for the circumflex artery to 0.863 for the left anterior descending artery. Conclusions: Fully automated AI-CACS as performed on non-contrast free-breathing, ungated CT scans from 18F-FDG-PET/CT examinations is feasible and provides an acceptable to good estimation of CAC burden. CAC load on ungated CT is, however, generally underestimated by AI-CACS, which should be taken into account when interpreting imaging findings.

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Section: Discussionmentioning

confidence: 91%

Section: Methodsmentioning

confidence: 99%

Diagnostic Value of Fully Automated Artificial Intelligence Powered Coronary Artery Calcium Scoring from 18F-FDG PET/CT

et al. 2022

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“…For development of the software, the spatial information of coronary and non-coronary regions manually labeled on coronary CT angiography was transfer to non-enhanced CAC scoring CT images using image registration. Then, DL algorithm was developed based on a 3-dimensional U-net architecture for segmentation of coronary and non-coronary regions on CAC scoring CTs ( 2 , 12 , 13 ). Calcium was detected when the potential lesion was in contact with the coronary region, and it did not belong to other structures.…”

Section: Methodsmentioning

confidence: 99%

Validation of deep learning-based fully automated coronary artery calcium scoring using non-ECG-gated chest CT in patients with cancer

et al. 2022

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This study aimed to demonstrate clinical feasibility of deep learning (DL)-based fully automated coronary artery calcium (CAC) scoring software using non-electrocardiogram (ECG)-gated chest computed tomography (CT) from patients with cancer. Overall, 913 patients with colorectal or gastric cancer who underwent non-contrast-enhanced chest CT between 2013 and 2015 were included. Agatston scores obtained by manual segmentation of CAC on chest CT were used as reference. Reliability of automated CAC score acquisition was evaluated using intraclass correlation coefficients (ICCs). The agreement for cardiovascular disease (CVD) risk stratification was assessed with linearly weighted k statistics. ICCs between the manual and automated CAC scores were 0.992 (95% CI, 0.991 and 0.993, p<0.001) for total Agatston scores, 0.863 (95% CI, 0.844 and 0.880, p<0.001) for the left main, 0.964 (95% CI, 0.959 and 0.968, p<0.001) for the left anterior descending, 0.962 (95% CI, 0.956 and 0.966, p<0.001) for the left circumflex, and 0.980 (95% CI, 0.978 and 0.983, p<0.001) for the right coronary arteries. The agreement for cardiovascular risk was excellent (k=0.946, p<0.001). Current DL-based automated CAC software showed excellent reliability for Agatston score and CVD risk stratification using non-ECG gated CT scans and might allow the identification of high-risk cancer patients for CVD.

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“…No training data were included in our study. A detailed description of this DL tool can be found elsewhere 5 , 8 , 10 .…”

Section: Methodsmentioning

confidence: 99%

“…Recently, deep-learning (DL)-based CAC scoring tools have been developed 4 – 10 . These AI-backed tools enable an accurate estimation of the coronary calcium load, as measured on dedicated non-contrast ECG-gated cardiac CT scans.…”

Section: Introductionmentioning

confidence: 99%

Opportunistic deep learning powered calcium scoring in oncologic patients with very high coronary artery calcium (≥ 1000) undergoing 18F-FDG PET/CT

Sartoretti

Gennari

Maurer

et al. 2022

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Our aim was to identify and quantify high coronary artery calcium (CAC) with deep learning (DL)-powered CAC scoring (CACS) in oncological patients with known very high CAC (≥ 1000) undergoing 18F-FDG-PET/CT for re-/staging. 100 patients were enrolled: 50 patients with Agatston scores ≥ 1000 (high CACS group), 50 patients with Agatston scores < 1000 (negative control group). All patients underwent oncological 18F-FDG-PET/CT and cardiac SPECT myocardial perfusion imaging (MPI) by 99mTc-tetrofosmin within 6 months. CACS was manually performed on dedicated non-contrast ECG-gated CT scans obtained from SPECT-MPI (reference standard). Additionally, CACS was performed fully automatically with a user-independent DL-CACS tool on non-contrast, free-breathing, non-gated CT scans from 18F-FDG-PET/CT examinations. Image quality and noise of CT scans was assessed. Agatston scores obtained by manual CACS and DL tool were compared. The high CACS group had Agatston scores of 2200 ± 1620 (reference standard) and 1300 ± 1011 (DL tool, average underestimation of 38.6 ± 26%) with an intraclass correlation of 0.714 (95% CI 0.546, 0.827). Sufficient image quality significantly improved the DL tool’s capability of correctly assigning Agatston scores ≥ 1000 (p = 0.01). In the control group, the DL tool correctly assigned Agatston scores < 1000 in all cases. In conclusion, DL-based CACS performed on non-contrast free-breathing, non-gated CT scans from 18F-FDG-PET/CT examinations of patients with known very high (≥ 1000) CAC underestimates CAC load, but correctly assigns an Agatston scores ≥ 1000 in over 70% of cases, provided sufficient CT image quality. Subgroup analyses of the control group showed that the DL tool does not generate false-positives.

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Fully automated deep learning powered calcium scoring in patients undergoing myocardial perfusion imaging

Cited by 12 publications

References 24 publications

Diagnostic Value of Fully Automated Artificial Intelligence Powered Coronary Artery Calcium Scoring from 18F-FDG PET/CT

Diagnostic Value of Fully Automated Artificial Intelligence Powered Coronary Artery Calcium Scoring from 18F-FDG PET/CT

Validation of deep learning-based fully automated coronary artery calcium scoring using non-ECG-gated chest CT in patients with cancer

Opportunistic deep learning powered calcium scoring in oncologic patients with very high coronary artery calcium (≥ 1000) undergoing 18F-FDG PET/CT

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