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.
Artificial Intelligence
Computed Tomography Angiography
/ methods
Coronary Angiography
/ methods
Coronary Artery Disease
/ diagnostic imaging
Coronary Vessels
/ diagnostic imaging
Deep Learning
Humans
Neural Networks, Computer
Radiographic Image Interpretation, Computer-Assisted
/ methods
Reproducibility of Results
Retrospective Studies
Time
Vascular Calcification
/ diagnostic imaging
Journal
Journal of thoracic imaging
ISSN: 1536-0237
Titre abrégé: J Thorac Imaging
Pays: United States
ID NLM: 8606160
Informations de publication
Date de publication:
May 2020
May 2020
Historique:
pubmed:
14
3
2020
medline:
9
2
2021
entrez:
14
3
2020
Statut:
ppublish
Résumé
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. 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. 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. The proposed RNN-LSTM demonstrated high diagnostic accuracy for the detection of CAC from CCTA.
Identifiants
pubmed: 32168163
doi: 10.1097/RTI.0000000000000491
pii: 00005382-202005001-00009
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
S49-S57Références
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