1. Accuracy of an Artificial Intelligence Deep Learning Algorithm Implementing a Recurrent Neural Network With Long Short-term Memory for the Automated Detection of Calcified Plaques From Coronary Computed Tomography Angiography
- Author
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Brian E. Jacobs, Mehmet Akif Gulsun, Ismail Kabakus, Pooyan Sahbaee, Marwen Eid, U. Joseph Schoepf, Domenico De Santis, Marly van Assen, Logan J. Jackson, Akos Varga-Szemes, Puneet Sharma, John W. Nance, Andreas Fischer, Maximilian J. Bauer, and Carlo N. De Cecco
- Subjects
Pulmonary and Respiratory Medicine ,Computed Tomography Angiography ,ARTERY CALCIUM ,NONCONTRAST ,SOCIETY ,convolutional neural network ,Diagnostic accuracy ,Coronary Artery Disease ,030204 cardiovascular system & hematology ,CHEST-PAIN ,Chest pain ,Coronary Angiography ,DISEASE ,030218 nuclear medicine & medical imaging ,Time ,coronary artery calcium score ,03 medical and health sciences ,Long short term memory ,0302 clinical medicine ,Deep Learning ,Artificial Intelligence ,Medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,Circumflex ,CARDIOVASCULAR RISK-ASSESSMENT ,Vascular Calcification ,Retrospective Studies ,business.industry ,Coronary computed tomography angiography ,Reproducibility of Results ,AMERICAN ,QUANTIFICATION ,Coronary Vessels ,Confidence interval ,Coronary arteries ,Coronary artery calcium ,medicine.anatomical_structure ,machine learning ,ATHEROSCLEROSIS ,Radiographic Image Interpretation, Computer-Assisted ,recurrent neural network ,Neural Networks, Computer ,coronary computed tomography angiography ,medicine.symptom ,business ,long short-term memory ,Algorithm ,CARDIAC CT - Abstract
Purpose: The purpose of this study was to evaluate the accuracy of a novel fully automated deep learning (DL) algorithm implementing a recurrent neural network (RNN) with long short-term memory (LSTM) for the detection of coronary artery calcium (CAC) from coronary computed tomography angiography (CCTA) data. Materials and Methods: Under an IRB waiver and in HIPAA compliance, a total of 194 patients who had undergone CCTA were retrospectively included. Two observers independently evaluated the image quality and recorded the presence of CAC in the right (RCA), the combination of left main and left anterior descending (LM-LAD), and left circumflex (LCx) coronary arteries. Noncontrast CACS scans were allowed to be used in cases of uncertainty. Heart and coronary artery centerline detection and labeling were automatically performed. Presence of CAC was assessed by a RNN-LSTM. The algorithm's overall and per-vessel sensitivity, specificity, and diagnostic accuracy were calculated. Results: CAC was absent in 84 and present in 110 patients. As regards CCTA, the median subjective image quality, signal-to-noise ratio, and contrast-to-noise ratio were 3.0, 13.0, and 11.4. A total of 565 vessels were evaluated. On a per-vessel basis, the algorithm achieved a sensitivity, specificity, and diagnostic accuracy of 93.1% (confidence interval [CI], 84.3%-96.7%), 82.76% (CI, 74.6%-89.4%), and 86.7% (CI, 76.8%-87.9%), respectively, for the RCA, 93.1% (CI, 86.4%-97.7%), 95.5% (CI, 88.77%-98.75%), and 94.2% (CI. 90.2%-94.6%), respectively, for the LM-LAD, and 89.9% (CI, 80.2%-95.8%), 90.0% (CI, 83.2%-94.7%), and 89.9% (CI, 85.0%-94.1%), respectively, for the LCx. The overall sensitivity, specificity, and diagnostic accuracy were 92.1% (CI, 92.1%-95.2%), 88.9% (CI. 84.9%-92.1%), and 90.3% (CI, 88.0%-90.0%), respectively. When accounting for image quality, the algorithm achieved a sensitivity, specificity, and diagnostic accuracy of 76.2%, 87.5%, and 82.2%, respectively, for poor-quality data sets and 93.3%, 89.2% and 90.9%, respectively, when data sets rated adequate or higher were combined. Conclusion: The proposed RNN-LSTM demonstrated high diagnostic accuracy for the detection of CAC from CCTA.
- Published
- 2020
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