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

Fischer, Andreas; Eid, Marwen; Cecco, Carlo N. De; Gülsün, Mehmet A.; Assen, Marly van; Nance, John W.; Sahbaee, Pooyan; Santis, Domenico De; Bauer, Maximilian J.; Jacobs, Brian E.; Varga-Szemes, Ákos; Kabakus, Ismail; Sharma, Puneet; Jackson, Logan J.; Schoepf, U. Joseph

doi:10.1097/rti.0000000000000491

Cited by 42 publications

(38 citation statements)

References 39 publications

(46 reference statements)

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“…Several studies have evaluated (semi)automatic methods for CCS in CT using standard cardiac calcium scoring CT [17][18][19] and chest CT [16,20] acquisitions. In this study, a transfer learning approach is used to train the algorithm on both dedicated cardiac CCS acquisitions and on chest CT's, using an additional probability map to exclude non-coronary calcifications.…”

Section: Introductionmentioning

confidence: 99%

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

Assen

Martin

Varga-Szemes

et al. 2021

European Journal of Radiology

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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 R 2 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 R 2 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.

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Section: Introductionmentioning

confidence: 99%

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

Assen

Martin

Varga-Szemes

et al. 2021

European Journal of Radiology

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“…In this paper, cutting-edge models [31][32][33][34][35][36] were compared to our proposed architecture. DenseNet, AlexNet, and GoogleNet architectures were implemented and tested with our newly created dataset.…”

Section: Resultsmentioning

confidence: 99%

Automated Clinical Decision Support for Coronary Plaques Characterization from Optical Coherence Tomography Imaging with Fused Neural Networks

Zafar

Sharif

2022

Optics

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Deep Neural Networks (DNNs) are nurturing clinical decision support systems for the detection and accurate modeling of coronary arterial plaques. However, efficient plaque characterization in time-constrained settings is still an open problem. The purpose of this study is to develop a novel automated classification architecture viable for the real-time clinical detection and classification of coronary artery plaques, and secondly, to use the novel dataset of OCT images for data augmentation. Further, the purpose is to validate the efficacy of transfer learning for arterial plaques classification. In this perspective, a novel time-efficient classification architecture based on DNNs is proposed. A new data set consisting of in-vivo patient Optical Coherence Tomography (OCT) images labeled by three trained experts was created and dynamically programmed. Generative Adversarial Networks (GANs) were used for populating the coronary aerial plaques dataset. We removed the fully connected layers, including softmax and the cross-entropy in the GoogleNet framework, and replaced them with the Support Vector Machines (SVMs). Our proposed architecture limits weight up-gradation cycles to only modified layers and computes the global hyper-plane in a timely, competitive fashion. Transfer learning was used for high-level discriminative feature learning. Cross-entropy loss was minimized by using the Adam optimizer for model training. A train validation scheme was used to determine the classification accuracy. Automated plaques differentiation in addition to their detection was found to agree with the clinical findings. Our customized fused classification scheme outperforms the other leading reported works with an overall accuracy of 96.84%, and multiple folds reduced elapsed time demonstrating it as a viable choice for real-time clinical settings.

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“…Zreik et al (2019) use semi-supervised learning based on CCTA images to evaluate if the patient needs further invasive coronary angiography. Fischer et al (2020) use RNN to predict the calcification fraction of coronary vessels based on CCTA. Shadmi et al (2018) use deep CNN to calculate the calcification fraction based on CCTA.…”

Section: Discussionmentioning

confidence: 99%

Disarranged Zone Learning (DZL): An unsupervised and dynamic automatic stenosis recognition methodology based on coronary angiography

Dai,

Zhu,

Xue

et al. 2021

Preprint

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We proposed a novel unsupervised methodology named Disarranged Zone Learning (DZL) to automatically recognize stenosis in coronary angiography. The methodology firstly disarranges the frames in a video, secondly it generates an effective zone and lastly trains an encoder-decoder GRU model to learn the capability to recover disarranged frames. The breakthrough of our study is to discover and validate the Sequence Intensity (Recover Difficulty) is a measure of Coronary Artery Stenosis Status. Hence, the prediction accuracy of DZL is used as an approximator of coronary stenosis indicator. DZL is an unsupervised methodology and no label engineering effort is needed, the sub GRU model in DZL works as a self-supervised approach. So DZL could theoretically utilize infinitely huge amounts of coronary angiographies to learn and improve performance without laborious data labeling. There is no data preprocessing precondition to run DZL as it dynamically utilizes the whole video, hence it is easy to be implemented and generalized to overcome the data heterogeneity of coronary angiography. The overall average precision score achieves 0.93, AUC achieves 0.8 for this pure methodology. The highest segmented average precision score is 0.98 and the highest segmented AUC is 0.87 for coronary occlusion indicator. Finally, we developed a software demo to implement DZL methodology.

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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

Cited by 42 publications

References 39 publications

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

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

Automated Clinical Decision Support for Coronary Plaques Characterization from Optical Coherence Tomography Imaging with Fused Neural Networks

Disarranged Zone Learning (DZL): An unsupervised and dynamic automatic stenosis recognition methodology based on coronary angiography

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