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      Artificial Intelligence in Coronary Computed Tomography Angiography: From Anatomy to Prognosis

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          Abstract

          Cardiac computed tomography angiography (CCTA) is widely used as a diagnostic tool for evaluation of coronary artery disease (CAD). Despite the excellent capability to rule-out CAD, CCTA may overestimate the degree of stenosis; furthermore, CCTA analysis can be time consuming, often requiring advanced postprocessing techniques. In consideration of the most recent ESC guidelines on CAD management, which will likely increase CCTA volume over the next years, new tools are necessary to shorten reporting time and improve the accuracy for the detection of ischemia-inducing coronary lesions. The application of artificial intelligence (AI) may provide a helpful tool in CCTA, improving the evaluation and quantification of coronary stenosis, plaque characterization, and assessment of myocardial ischemia. Furthermore, in comparison with existing risk scores, machine-learning algorithms can better predict the outcome utilizing both imaging findings and clinical parameters. Medical AI is moving from the research field to daily clinical practice, and with the increasing number of CCTA examinations, AI will be extensively utilized in cardiac imaging. This review is aimed at illustrating the state of the art in AI-based CCTA applications and future clinical scenarios.

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          2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes

          Juhani Knuuti, William Wijns, Antti Saraste … (2019)
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            Quantification of coronary artery calcium using ultrafast computed tomography

            Arthur S Agatston, Warren Janowitz, Frank Hildner … (1990)
            Article 0 comments Cited 550 times – based on 0 reviews     Review now
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              Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study

              John Zech, Marcus Badgeley, Manway Liu … (2018)
              Background There is interest in using convolutional neural networks (CNNs) to analyze medical imaging to provide computer-aided diagnosis (CAD). Recent work has suggested that image classification CNNs may not generalize to new data as well as previously believed. We assessed how well CNNs generalized across three hospital systems for a simulated pneumonia screening task. Methods and findings A cross-sectional design with multiple model training cohorts was used to evaluate model generalizability to external sites using split-sample validation. A total of 158,323 chest radiographs were drawn from three institutions: National Institutes of Health Clinical Center (NIH; 112,120 from 30,805 patients), Mount Sinai Hospital (MSH; 42,396 from 12,904 patients), and Indiana University Network for Patient Care (IU; 3,807 from 3,683 patients). These patient populations had an age mean (SD) of 46.9 years (16.6), 63.2 years (16.5), and 49.6 years (17) with a female percentage of 43.5%, 44.8%, and 57.3%, respectively. We assessed individual models using the area under the receiver operating characteristic curve (AUC) for radiographic findings consistent with pneumonia and compared performance on different test sets with DeLong’s test. The prevalence of pneumonia was high enough at MSH (34.2%) relative to NIH and IU (1.2% and 1.0%) that merely sorting by hospital system achieved an AUC of 0.861 (95% CI 0.855–0.866) on the joint MSH–NIH dataset. Models trained on data from either NIH or MSH had equivalent performance on IU (P values 0.580 and 0.273, respectively) and inferior performance on data from each other relative to an internal test set (i.e., new data from within the hospital system used for training data; P values both <0.001). The highest internal performance was achieved by combining training and test data from MSH and NIH (AUC 0.931, 95% CI 0.927–0.936), but this model demonstrated significantly lower external performance at IU (AUC 0.815, 95% CI 0.745–0.885, P = 0.001). To test the effect of pooling data from sites with disparate pneumonia prevalence, we used stratified subsampling to generate MSH–NIH cohorts that only differed in disease prevalence between training data sites. When both training data sites had the same pneumonia prevalence, the model performed consistently on external IU data (P = 0.88). When a 10-fold difference in pneumonia rate was introduced between sites, internal test performance improved compared to the balanced model (10× MSH risk P < 0.001; 10× NIH P = 0.002), but this outperformance failed to generalize to IU (MSH 10× P < 0.001; NIH 10× P = 0.027). CNNs were able to directly detect hospital system of a radiograph for 99.95% NIH (22,050/22,062) and 99.98% MSH (8,386/8,388) radiographs. The primary limitation of our approach and the available public data is that we cannot fully assess what other factors might be contributing to hospital system–specific biases. Conclusion Pneumonia-screening CNNs achieved better internal than external performance in 3 out of 5 natural comparisons. When models were trained on pooled data from sites with different pneumonia prevalence, they performed better on new pooled data from these sites but not on external data. CNNs robustly identified hospital system and department within a hospital, which can have large differences in disease burden and may confound predictions.
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                Author and article information

                Contributors
                Gianluca Pontone:
                ORCID: https://orcid.org/0000-0002-1339-6679
                Journal
                Journal ID (nlm-ta): Biomed Res Int
                Journal ID (iso-abbrev): Biomed Res Int
                Journal ID (publisher-id): BMRI
                Title: BioMed Research International
                Publisher: Hindawi
                ISSN (Print): 2314-6133
                ISSN (Electronic): 2314-6141
                Publication date Collection: 2020
                Publication date (Electronic): 16 December 2020
                Volume: 2020
                Electronic Location Identifier: 6649410
                Affiliations
                1Centro Cardiologico Monzino, IRCCS, Milan, Italy
                2Division of Cardiothoracic Imaging, Nuclear Medicine and Molecular Imaging, Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA
                3Department of Cardiology, Munich University Clinic, Ludwig-Maximilians-University, Munich, Germany
                4Department of Internal Medicine, St. Johannes-Hospital, Dortmund, Germany
                5Division of Interventional Structural Cardiology, Cardiothoracovascular Department, Careggi University Hospital, Florence, Italy
                6Institute of Cardiovascular Disease, Department of Emergency and Organ Transplantation, University Hospital “Policlinico Consorziale” of Bari, Bari, Italy
                7Loyola University of Chicago, Chicago, IL, USA
                8Edward Hines Jr. VA Hospital, Hines, IL, USA
                Author notes

                Academic Editor: Luca Liberale

                Author information
                https://orcid.org/0000-0002-1339-6679
                Article
                DOI: 10.1155/2020/6649410
                PMC ID: 7762640
                PubMed ID: 33381570
                SO-VID: 5244d8d9-6285-4bfa-b556-1b2a8662dbaf
                Copyright © Copyright © 2020 Giuseppe Muscogiuri et al.
                License:

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 29 October 2020
                Date revision received : 30 November 2020
                Date accepted : 9 December 2020
                Categories
                Subject: Review Article

                Data availability:

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