Artificial intelligence in cardiovascular CT: Current status and future implications

Lin, Andrew; Kolossváry, Márton; Motwani, Manish; Išgum, Ivana; Maurovich-Horvat, Pál; Slomka, Piotr J.; Dey, Damini

doi:10.1016/j.jcct.2021.03.006

Cited by 32 publications

(14 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Numerous machine learning algorithms have been developed in the cardiovascular field (15).The majority of them use discrete data such as values from lab report and patient demographic data as inputs to construct the models (16). Recently, deep learning models using medical images such as cardiac MRI and cardiac CT scans have been proposed to predict cardiovascular prognosis (17). However, deep learning models using the NLP technique are scarce and mostly applied to radiological reports.…”

Section: Discussionmentioning

confidence: 99%

Using Text Content From Coronary Catheterization Reports to Predict 5-Year Mortality Among Patients Undergoing Coronary Angiography: A Deep Learning Approach

Lee

Chen

et al. 2022

Front. Cardiovasc. Med.

View full text Add to dashboard Cite

BackgroundCurrent predictive models for patients undergoing coronary angiography have complex parameters which limit their clinical application. Coronary catheterization reports that describe coronary lesions and the corresponding interventions provide information of the severity of the coronary artery disease and the completeness of the revascularization. This information is relevant for predicting patient prognosis. However, no predictive model has been constructed using the text content from coronary catheterization reports before.ObjectiveTo develop a deep learning model using text content from coronary catheterization reports to predict 5-year all-cause mortality and 5-year cardiovascular mortality for patients undergoing coronary angiography and to compare the performance of the model to the established clinical scores.MethodThis retrospective cohort study was conducted between January 1, 2006, and December 31, 2015. Patients admitted for coronary angiography were enrolled and followed up until August 2019. The main outcomes were 5-year all-cause mortality and 5-year cardiovascular mortality. In total, 11,576 coronary catheterization reports were collected. BioBERT (bidirectional encoder representations from transformers for biomedical text mining), which is a BERT-based model in the biomedical domain, was utilized to construct the model. The area under the receiver operating characteristic curve (AUC) was used to assess model performance. We also compared our results to the residual SYNTAX (SYNergy between PCI with TAXUS and Cardiac Surgery) score.ResultsThe dataset was divided into the training (60%), validation (20%), and test (20%) sets. The mean age of the patients in each dataset was 65.5 ± 12.1, 65.4 ± 11.2, and 65.6 ± 11.2 years, respectively. A total of 1,411 (12.2%) patients died, and 664 (5.8%) patients died of cardiovascular causes within 5 years after coronary angiography. The best of our models had an AUC of 0.822 (95% CI, 0.790–0.855) for 5-year all-cause mortality, and an AUC of 0.858 (95% CI, 0.816–0.900) for 5-year cardiovascular mortality. We randomly selected 300 patients who underwent percutaneous coronary intervention (PCI), and our model outperformed the residual SYNTAX score in predicting 5-year all-cause mortality (AUC, 0.867 [95% CI, 0.813–0.921] vs. 0.590 [95% CI, 0.503–0.684]) and 5-year cardiovascular mortality (AUC, 0.880 [95% CI, 0.873–0.925] vs. 0.649 [95% CI, 0.535–0.764]), respectively, after PCI among these patients.ConclusionsWe developed a predictive model using text content from coronary catheterization reports to predict the 5-year mortality in patients undergoing coronary angiography. Since interventional cardiologists routinely write reports after procedures, our model can be easily implemented into the clinical setting.

show abstract

Section: Discussionmentioning

confidence: 99%

Using Text Content From Coronary Catheterization Reports to Predict 5-Year Mortality Among Patients Undergoing Coronary Angiography: A Deep Learning Approach

Lee

Chen

et al. 2022

Front. Cardiovasc. Med.

View full text Add to dashboard Cite

show abstract

“…The main barriers to implementing 3D printing technology in routine cardiovascular practice are the relatively high costs associated with 3D printing (including image post-processing and segmentation) and the slow turnaround time. The first barrier will be addressed by using artificial intelligence, such as machine learning (ML) or deep learning (DL), to enhance the image segmentation process [ 91 , 92 , 93 ]. With printers available at clinical sites, the use of 3D printing technology in daily practice will become possible, and clinicians can incorporate 3D printed models into their diagnosis and decision-making process.…”

Section: Limitations Barriers and Future Directionsmentioning

confidence: 99%

3D Printed Models in Cardiovascular Disease: An Exciting Future to Deliver Personalized Medicine

Sun

Wee

2022

Micromachines

View full text Add to dashboard Cite

3D printing has shown great promise in medical applications with increased reports in the literature. Patient-specific 3D printed heart and vascular models replicate normal anatomy and pathology with high accuracy and demonstrate superior advantages over the standard image visualizations for improving understanding of complex cardiovascular structures, providing guidance for surgical planning and simulation of interventional procedures, as well as enhancing doctor-to-patient communication. 3D printed models can also be used to optimize CT scanning protocols for radiation dose reduction. This review article provides an overview of the current status of using 3D printing technology in cardiovascular disease. Limitations and barriers to applying 3D printing in clinical practice are emphasized while future directions are highlighted.

show abstract

“…It was found that machine learning in conjunction with the coronary artery calcium score resulted in the most significantly accurate assessment of obstructive CAD from CT imaging compared to machine learning or calcium score alone [ 27 ]. Machine learning has been used to identify a variety of different pathologies on CT with accuracy [ 28 ].…”

Section: Ai: Cardiology Imaging Applicationsmentioning

confidence: 99%

Artificial Intelligence Advances in the World of Cardiovascular Imaging

Patel

Makaryus

2022

Healthcare

View full text Add to dashboard Cite

The tremendous advances in digital information and communication technology have entered everything from our daily lives to the most intricate aspects of medical and surgical care. These advances are seen in electronic and mobile health and allow many new applications to further improve and make the diagnoses of patient diseases and conditions more precise. In the area of digital radiology with respect to diagnostics, the use of advanced imaging tools and techniques is now at the center of evaluation and treatment. Digital acquisition and analysis are central to diagnostic capabilities, especially in the field of cardiovascular imaging. Furthermore, the introduction of artificial intelligence (AI) into the world of digital cardiovascular imaging greatly broadens the capabilities of the field both with respect to advancement as well as with respect to complete and accurate diagnosis of cardiovascular conditions. The application of AI in recognition, diagnostics, protocol automation, and quality control for the analysis of cardiovascular imaging modalities such as echocardiography, nuclear cardiac imaging, cardiovascular computed tomography, cardiovascular magnetic resonance imaging, and other imaging, is a major advance that is improving rapidly and continuously. We document the innovations in the field of cardiovascular imaging that have been brought about by the acceptance and implementation of AI in relation to healthcare professionals and patients in the cardiovascular field.

show abstract

Artificial intelligence in cardiovascular CT: Current status and future implications

Cited by 32 publications

References 49 publications

Using Text Content From Coronary Catheterization Reports to Predict 5-Year Mortality Among Patients Undergoing Coronary Angiography: A Deep Learning Approach

Using Text Content From Coronary Catheterization Reports to Predict 5-Year Mortality Among Patients Undergoing Coronary Angiography: A Deep Learning Approach

3D Printed Models in Cardiovascular Disease: An Exciting Future to Deliver Personalized Medicine

Artificial Intelligence Advances in the World of Cardiovascular Imaging

Contact Info

Product

Resources

About