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Zhu J, Chen T, Mao X, Fang Y, Sun H, Wei DQ, Ji G. Machine learning of flow cytometry data reveals the delayed innate immune responses correlate with the severity of COVID-19. Front Immunol 2023; 14:974343. [PMID: 36845115 PMCID: PMC9951775 DOI: 10.3389/fimmu.2023.974343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 01/04/2023] [Indexed: 01/27/2023] Open
Abstract
Introduction The COVID-19 pandemic has posed a major burden on healthcare and economic systems across the globe for over 3 years. Even though vaccines are available, the pathogenesis is still unclear. Multiple studies have indicated heterogeneity of immune responses to SARS-CoV-2, and potentially distinct patient immune types that might be related to disease features. However, those conclusions are mainly inferred by comparing the differences of pathological features between moderate and severe patients, some immunological features may be subjectively overlooked. Methods In this study, the relevance scores(RS), reflecting which features play a more critical role in the decision-making process, between immunological features and the COVID-19 severity are objectively calculated through neural network, where the input features include the immune cell counts and the activation marker concentrations of particular cell, and these quantified characteristic data are robustly generated by processing flow cytometry data sets containing the peripheral blood information of COVID-19 patients through PhenoGraph algorithm. Results Specifically, the RS between immune cell counts and COVID-19 severity with time indicated that the innate immune responses in severe patients are delayed at the early stage, and the continuous decrease of classical monocytes in peripherial blood is significantly associated with the severity of disease. The RS between activation marker concentrations and COVID-19 severity suggested that the down-regulation of IFN-γ in classical monocytes, Treg, CD8 T cells, and the not down-regulation of IL_17a in classical monocytes, Tregs are highly correlated with the occurrence of severe disease. Finally, a concise dynamic model of immune responses in COVID-19 patients was generalized. Discussion These results suggest that the delayed innate immune responses in the early stage, and the abnormal expression of IL-17a and IFN-γ in classical monocytes, Tregs, and CD8 T cells are primarily responsible for the severity of COVID-19.
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Affiliation(s)
- Jing Zhu
- National Key Laboratory for Shock Wave and Detonation Physics Research, Institute of Fluid Physics, Chinese Academy of Engineering Physics, Mianyang, China
| | - Tunan Chen
- Department of Neurosurgery and Key Laboratory of Neurotrauma, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqin, China
| | - Xueying Mao
- State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Joint Laboratory of International Cooperation in Metabolic and Developmental, Sciences, Ministry of Education and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Yitian Fang
- State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Joint Laboratory of International Cooperation in Metabolic and Developmental, Sciences, Ministry of Education and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Heqi Sun
- State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Joint Laboratory of International Cooperation in Metabolic and Developmental, Sciences, Ministry of Education and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Joint Laboratory of International Cooperation in Metabolic and Developmental, Sciences, Ministry of Education and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- Peng Cheng Laboratory, Shenzhen, China
| | - Guangfu Ji
- National Key Laboratory for Shock Wave and Detonation Physics Research, Institute of Fluid Physics, Chinese Academy of Engineering Physics, Mianyang, China
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2
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Hampe N, van Velzen SGM, Planken RN, Henriques JPS, Collet C, Aben JP, Voskuil M, Leiner T, Išgum I. Deep learning-based detection of functionally significant stenosis in coronary CT angiography. Front Cardiovasc Med 2022; 9:964355. [PMID: 36457806 PMCID: PMC9705580 DOI: 10.3389/fcvm.2022.964355] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 10/17/2022] [Indexed: 07/20/2023] Open
Abstract
Patients with intermediate anatomical degree of coronary artery stenosis require determination of its functional significance. Currently, the reference standard for determining the functional significance of a stenosis is invasive measurement of the fractional flow reserve (FFR), which is associated with high cost and patient burden. To address these drawbacks, FFR can be predicted non-invasively from a coronary CT angiography (CCTA) scan. Hence, we propose a deep learning method for predicting the invasively measured FFR of an artery using a CCTA scan. The study includes CCTA scans of 569 patients from three hospitals. As reference for the functional significance of stenosis, FFR was measured in 514 arteries in 369 patients, and in the remaining 200 patients, obstructive coronary artery disease was ruled out by Coronary Artery Disease-Reporting and Data System (CAD-RADS) category 0 or 1. For prediction, the coronary tree is first extracted and used to reconstruct an MPR for the artery at hand. Thereafter, the coronary artery is characterized by its lumen, its attenuation and the area of the coronary artery calcium in each artery cross-section extracted from the MPR using a CNN. Additionally, characteristics indicating the presence of bifurcations and information indicating whether the artery is a main branch or a side-branch of a main artery are derived from the coronary artery tree. All characteristics are fed to a second network that predicts the FFR value and classifies the presence of functionally significant stenosis. The final result is obtained by merging the two predictions. Performance of our method is evaluated on held out test sets from multiple centers and vendors. The method achieves an area under the receiver operating characteristics curve (AUC) of 0.78, outperforming other works that do not require manual correction of the segmentation of the artery. This demonstrates that our method may reduce the number of patients that unnecessarily undergo invasive measurements.
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Affiliation(s)
- Nils Hampe
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias, Amsterdam, Netherlands
- Informatics Institute, University of Amsterdam, Amsterdam, Netherlands
| | - Sanne G. M. van Velzen
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias, Amsterdam, Netherlands
- Informatics Institute, University of Amsterdam, Amsterdam, Netherlands
| | - R. Nils Planken
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
| | - José P. S. Henriques
- AMC Heart Center, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
| | - Carlos Collet
- Onze Lieve Vrouwziekenhuis, Cardiovascular Center Aalst, Aalst, Belgium
| | | | - Michiel Voskuil
- Department of Cardiology, University Medical Centre Utrecht, Utrecht, Netherlands
| | - Tim Leiner
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Ivana Išgum
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias, Amsterdam, Netherlands
- Informatics Institute, University of Amsterdam, Amsterdam, Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
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3
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Karpiel I, Starcevic A, Urzeniczok M. Database and AI Diagnostic Tools Improve Understanding of Lung Damage, Correlation of Pulmonary Disease and Brain Damage in COVID-19. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22166312. [PMID: 36016071 PMCID: PMC9414394 DOI: 10.3390/s22166312] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 08/17/2022] [Accepted: 08/18/2022] [Indexed: 05/02/2023]
Abstract
The COVID-19 pandemic caused a sharp increase in the interest in artificial intelligence (AI) as a tool supporting the work of doctors in difficult conditions and providing early detection of the implications of the disease. Recent studies have shown that AI has been successfully applied in the healthcare sector. The objective of this paper is to perform a systematic review to summarize the electroencephalogram (EEG) findings in patients with coronavirus disease (COVID-19) and databases and tools used in artificial intelligence algorithms, supporting the diagnosis and correlation between lung disease and brain damage, and lung damage. Available search tools containing scientific publications, such as PubMed and Google Scholar, were comprehensively evaluated and searched with open databases and tools used in AI algorithms. This work aimed to collect papers from the period of January 2019-May 2022 including in their resources the database from which data necessary for further development of algorithms supporting the diagnosis of the respiratory system can be downloaded and the correlation between lung disease and brain damage can be evaluated. The 10 articles which show the most interesting AI algorithms, trained by using open databases and associated with lung diseases, were included for review with 12 articles related to EEGs, which have/or may be related with lung diseases.
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Affiliation(s)
- Ilona Karpiel
- Łukasiewicz Research Network—Institute of Medical Technology and Equipment, 41-800 Zabrze, Poland
- Correspondence:
| | - Ana Starcevic
- Laboratory for Multimodal Neuroimaging, Institute of Anatomy, Medical Faculty, University of Belgrade, 11000 Belgrade, Serbia
| | - Mirella Urzeniczok
- Łukasiewicz Research Network—Institute of Medical Technology and Equipment, 41-800 Zabrze, Poland
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4
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Towards a Deep-Learning Approach for Prediction of Fractional Flow Reserve from Optical Coherence Tomography. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12146964] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Cardiovascular disease (CVD) is the number one cause of death worldwide, and coronary artery disease (CAD) is the most prevalent CVD, accounting for 42% of these deaths. In view of the limitations of the anatomical evaluation of CAD, Fractional Flow Reserve (FFR) has been introduced as a functional diagnostic index. Herein, we evaluate the feasibility of using deep neural networks (DNN) in an ensemble approach to predict the invasively measured FFR from raw anatomical information that is extracted from optical coherence tomography (OCT). We evaluate the performance of various DNN architectures under different formulations: regression, classification—standard, and few-shot learning (FSL) on a dataset containing 102 intermediate lesions from 80 patients. The FSL approach that is based on a convolutional neural network leads to slightly better results compared to the standard classification: the per-lesion accuracy, sensitivity, and specificity were 77.5%, 72.9%, and 81.5%, respectively. However, since the 95% confidence intervals overlap, the differences are statistically not significant. The main findings of this study can be summarized as follows: (1) Deep-learning (DL)-based FFR prediction from reduced-order raw anatomical data is feasible in intermediate coronary artery lesions; (2) DL-based FFR prediction provides superior diagnostic performance compared to baseline approaches that are based on minimal lumen diameter and percentage diameter stenosis; and (3) the FFR prediction performance increases quasi-linearly with the dataset size, indicating that a larger train dataset will likely lead to superior diagnostic performance.
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5
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Inage H, Tomizawa N, Otsuka Y, Aoshima C, Kawaguchi Y, Takamura K, Matsumori R, Kamo Y, Nozaki Y, Takahashi D, Kudo A, Hiki M, Kogure Y, Fujimoto S, Minamino T, Aoki S. Use of a deep-learning-based lumen extraction method to detect significant stenosis on coronary computed tomography angiography in patients with severe coronary calcification. Egypt Heart J 2022; 74:43. [PMID: 35596813 PMCID: PMC9124254 DOI: 10.1186/s43044-022-00280-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 05/13/2022] [Indexed: 11/10/2022] Open
Abstract
Background Coronary computed tomography angiography examinations are increasingly becoming established as a minimally invasive method for diagnosing coronary diseases. However, although various imaging and processing methods have been developed, coronary artery calcification remains a major limitation in the evaluation of the vascular lumen. Subtraction coronary computed tomography angiography (Sub-CCTA) is a method known to be able to reduce the influence of coronary artery calcification and is therefore feasible for improving the diagnosis of significant stenosis in patients with severe calcification. However, Sub-CCTA still involves some problems, such as the increased radiation dose due to plain (mask) imaging, extended breath-holding time, and misregistration due to differences in the imaging phase. Therefore, we considered using artificial intelligence instead of Sub-CCTA to visualize the coronary lumen with high calcification. Given this background, the present study aimed to evaluate the diagnostic performance of a deep learning-based lumen extraction method (DL-LEM) to detect significant stenosis on CCTA in 99 consecutive patients (891 segments) with severe coronary calcification from November 2015 to March 2018. We also estimated the impact of DL-LEM on the medical economics in Japan. Results The DL-LEM slightly improved the per-segment diagnostic accuracy from 74.5 to 76.4%, and the area under the curve (AUC) slightly improved from 0.752 to 0.767 (p = 0.030). When analyzing the 228 segments that could not be evaluated because of severe calcification on the original CCTA images, the DL-LEM improved the accuracy from 35.5 to 42.5%, and the AUC improved from 0.500 to 0.587 (p = 0.00018). As a result, DL-LEM analysis could have avoided invasive coronary angiography in 4/99 cases (per patient). From the calculated results, it was estimated that the number of exams that can be avoided in Japan in one year is approximately 747 for invasive coronary angiography, 219 for fractional flow reserve, and 248 for nuclear exam. The total amount of medical fee that could be reduced was 225,629,368 JPY. Conclusions These findings suggest that the DL-LEM may improve the diagnostic performance in detecting significant stenosis in patients with severe coronary calcification. In addition, the results suggest that not a small medical economic effect can be expected.
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Affiliation(s)
- Hidekazu Inage
- Department of Radiology, Graduate School of Medicine, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.,Department of Radiological Technology, Juntendo University Hospital, 3-1-3 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Nobuo Tomizawa
- Department of Radiology, Graduate School of Medicine, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.
| | - Yujiro Otsuka
- Department of Radiology, Graduate School of Medicine, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.,Milliman, Inc., Urbannet Kojimachi Bldg, 8F 1-6-2 Kojimachi, Chiyoda-ku, Tokyo, 102-0083, Japan.,Plusman LLC., 2F 1-3-6 Hirakawacho, Chiyoda-ku, Tokyo, 102-0093, Japan
| | - Chihiro Aoshima
- Department of Cardiovascular Biology and Medicine, Graduate School of Medicine, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Yuko Kawaguchi
- Department of Cardiovascular Biology and Medicine, Graduate School of Medicine, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Kazuhisa Takamura
- Department of Cardiovascular Biology and Medicine, Graduate School of Medicine, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Rie Matsumori
- Department of Cardiovascular Biology and Medicine, Graduate School of Medicine, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Yuki Kamo
- Department of Cardiovascular Biology and Medicine, Graduate School of Medicine, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Yui Nozaki
- Department of Cardiovascular Biology and Medicine, Graduate School of Medicine, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Daigo Takahashi
- Department of Cardiovascular Biology and Medicine, Graduate School of Medicine, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Ayako Kudo
- Department of Cardiovascular Biology and Medicine, Graduate School of Medicine, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Makoto Hiki
- Department of Cardiovascular Biology and Medicine, Graduate School of Medicine, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Yosuke Kogure
- Department of Radiological Technology, Juntendo University Hospital, 3-1-3 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Shinichiro Fujimoto
- Department of Cardiovascular Biology and Medicine, Graduate School of Medicine, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Tohru Minamino
- Department of Cardiovascular Biology and Medicine, Graduate School of Medicine, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Shigeki Aoki
- Department of Radiology, Graduate School of Medicine, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
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6
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Lu H, Yao Y, Wang L, Yan J, Tu S, Xie Y, He W. Research Progress of Machine Learning and Deep Learning in Intelligent Diagnosis of the Coronary Atherosclerotic Heart Disease. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:3016532. [PMID: 35516452 PMCID: PMC9064517 DOI: 10.1155/2022/3016532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 02/27/2022] [Accepted: 03/04/2022] [Indexed: 11/17/2022]
Abstract
The coronary atherosclerotic heart disease is a common cardiovascular disease with high morbidity, disability, and societal burden. Early, precise, and comprehensive diagnosis of the coronary atherosclerotic heart disease is of great significance. The rise of artificial intelligence technologies, represented by machine learning and deep learning, provides new methods to address the above issues. In recent years, artificial intelligence has achieved an extraordinary progress in multiple aspects of coronary atherosclerotic heart disease diagnosis, including the construction of intelligent diagnostic models based on artificial intelligence algorithms, applications of artificial intelligence algorithms in coronary angiography, coronary CT angiography, intravascular imaging, cardiac magnetic resonance, and functional parameters. This paper presents a comprehensive review of the technical background and current state of research on the application of artificial intelligence in the diagnosis of the coronary atherosclerotic heart disease and analyzes recent challenges and perspectives in this field.
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Affiliation(s)
- Haoxuan Lu
- The Affiliated Hospital of Medical School, Ningbo University, Ningbo 315020, China
| | - Yudong Yao
- Research Institute of Medical and Biological Engineering, Ningbo University, Ningbo 315211, China
| | - Li Wang
- The Affiliated Hospital of Medical School, Ningbo University, Ningbo 315020, China
| | - Jianing Yan
- The Affiliated Hospital of Medical School, Ningbo University, Ningbo 315020, China
| | - Shuangshuang Tu
- The Affiliated Hospital of Medical School, Ningbo University, Ningbo 315020, China
| | - Yanqing Xie
- The Affiliated Hospital of Medical School, Ningbo University, Ningbo 315020, China
| | - Wenming He
- The Affiliated Hospital of Medical School, Ningbo University, Ningbo 315020, China
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7
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Automatic coronary artery segmentation and diagnosis of stenosis by deep learning based on computed tomographic coronary angiography. Eur Radiol 2022; 32:6037-6045. [PMID: 35394183 DOI: 10.1007/s00330-022-08761-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 03/05/2022] [Accepted: 03/21/2022] [Indexed: 02/05/2023]
Abstract
OBJECTIVES Coronary computed tomography angiography (CCTA) has rapidly developed in the coronary artery disease (CAD) field. However, manual coronary artery tree segmentation and reconstruction are time-consuming and tedious. Deep learning algorithms have been successfully developed for medical image analysis to process extensive data. Thus, we aimed to develop a deep learning tool for automatic coronary artery reconstruction and an automated CAD diagnosis model based on a large, single-centre retrospective CCTA cohort. METHODS Automatic CAD diagnosis consists of two subtasks. One is a segmentation task, which aims to extract the region of interest (ROI) from original images with U-Net. The second task is an identification task, which we implemented using 3DNet. The coronary artery tree images and clinical parameters were input into 3DNet, and the CAD diagnosis result was output. RESULTS We built a coronary artery segmentation model based on CCTA images with the corresponding labelling. The segmentation model had a mean Dice value of 0.771 ± 0.021. Based on this model, we built an automated diagnosis model (classification model) for CAD. The average accuracy and area under the receiver operating characteristic curve (AUC) were 0.750 ± 0.056 and 0.737, respectively. CONCLUSION Herein, using a deep learning algorithm, we realized the rapid classification and diagnosis of CAD from CCTA images in two steps. Our deep learning model can automatically segment the coronary artery quickly and accurately and can deliver a diagnosis of ≥ 50% coronary artery stenosis. Artificial intelligence methods such as deep learning have the potential to elevate the efficiency in CCTA image analysis considerably. KEY POINTS • The deep learning model rapidly achieved a high Dice value (0.771 ± 0.0210) in the autosegmentation of coronary arteries using CCTA images. • Based on the segmentation model, we built a CAD autoclassifier with the 3DNet algorithm, which achieved a good diagnostic performance (AUC) of 0.737. • The deep neural network could be used in the image postprocessing of coronary computed tomography angiography to achieve a quick and accurate diagnosis of CAD.
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8
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Current and Future Applications of Artificial Intelligence in Coronary Artery Disease. Healthcare (Basel) 2022; 10:healthcare10020232. [PMID: 35206847 PMCID: PMC8872080 DOI: 10.3390/healthcare10020232] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 01/19/2022] [Accepted: 01/24/2022] [Indexed: 02/07/2023] Open
Abstract
Cardiovascular diseases (CVDs) carry significant morbidity and mortality and are associated with substantial economic burden on healthcare systems around the world. Coronary artery disease, as one disease entity under the CVDs umbrella, had a prevalence of 7.2% among adults in the United States and incurred a financial burden of 360 billion US dollars in the years 2016–2017. The introduction of artificial intelligence (AI) and machine learning over the last two decades has unlocked new dimensions in the field of cardiovascular medicine. From automatic interpretations of heart rhythm disorders via smartwatches, to assisting in complex decision-making, AI has quickly expanded its realms in medicine and has demonstrated itself as a promising tool in helping clinicians guide treatment decisions. Understanding complex genetic interactions and developing clinical risk prediction models, advanced cardiac imaging, and improving mortality outcomes are just a few areas where AI has been applied in the domain of coronary artery disease. Through this review, we sought to summarize the advances in AI relating to coronary artery disease, current limitations, and future perspectives.
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9
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Torii R, Yacoub MH. CT-based fractional flow reserve: development and expanded application. Glob Cardiol Sci Pract 2021; 2021:e202120. [PMID: 34805378 PMCID: PMC8587224 DOI: 10.21542/gcsp.2021.20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 09/30/2021] [Indexed: 11/28/2022] Open
Abstract
Computations of fractional flow reserve, based on CT coronary angiography and computational fluid dynamics (CT-based FFR) to assess the severity of coronary artery stenosis, was introduced around a decade ago and is now one of the most successful applications of computational fluid dynamic modelling in clinical practice. Although the mathematical modelling framework behind this approach and the clinical operational model vary, its clinical efficacy has been demonstrated well in general. In this review, technical elements behind CT-based FFR computation are summarised with some key assumptions and challenges. Examples of these challenges include the complexity of the model (such as blood viscosity and vessel wall compliance modelling), whose impact has been debated in the research. Efforts made to address the practical challenge of processing time are also reviewed. Then, further application areas—myocardial bridge, renal stenosis and lower limb stenosis—are discussed along with specific challenges expected in these areas.
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Affiliation(s)
- Ryo Torii
- Department of Mechanical Engineering, University College London, London, UK
| | - Magdi H Yacoub
- Department of Surgery and Department of Cardiology, Aswan Heart Centre, Magdi Yacoub Heart Foundation, Aswan, Egypt.,Magdi Yacoub Institute, Harefield Heart Science Centre, Harefield, UK.,National Heart and Lung Institute, Imperial College London, UK
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10
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Yun CH, Hung CL, Wen MS, Wan YL, So A. CT Assessment of Myocardial Perfusion and Fractional Flow Reserve in Coronary Artery Disease: A Review of Current Clinical Evidence and Recent Developments. Korean J Radiol 2021; 22:1749-1763. [PMID: 34431244 PMCID: PMC8546143 DOI: 10.3348/kjr.2020.1277] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 05/11/2021] [Accepted: 05/15/2021] [Indexed: 11/25/2022] Open
Abstract
Coronary computed tomography angiography (CCTA) is routinely used for anatomical assessment of coronary artery disease (CAD). However, invasive measurement of fractional flow reserve (FFR) is the current gold standard for the diagnosis of hemodynamically significant CAD. CT-derived FFRCT and CT perfusion are two emerging techniques that can provide a functional assessment of CAD for risk stratification and clinical decision making. Several clinical studies have shown that the diagnostic performance of concomitant CCTA and functional CT assessment for detecting hemodynamically significant CAD is at least non-inferior to that of other routinely used imaging modalities. This article aims to review the current clinical evidence and recent developments in functional CT techniques.
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Affiliation(s)
- Chun-Ho Yun
- Department of Radiology, MacKay Memorial Hospital, Taipei, Taiwan
| | - Chung-Lieh Hung
- Division of Cardiology, Department of Internal Medicine, MacKay Memorial Hospital, Taipei, Taiwan.,Institute of Biomedical Sciences, Mackay Medical College, New Taipei, Taiwan
| | - Ming-Shien Wen
- Department of Cardiology, Linkou Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Yung-Liang Wan
- Department of Medical Imaging and Intervention, Linkou Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Taoyuan, Taiwan.
| | - Aaron So
- Department of Medical Biophysics, University of Western Ontario, Imaging Program, Lawson Health Research Institute, London, Canada
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11
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Zhang ZZ, Guo Y, Hou Y. Artificial intelligence in coronary computed tomography angiography. Artif Intell Med Imaging 2021; 2:73-85. [DOI: 10.35711/aimi.v2.i3.73] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 06/20/2021] [Accepted: 07/02/2021] [Indexed: 02/06/2023] Open
Abstract
Coronary computed tomography angiography (CCTA) is recommended as a frontline diagnostic tool in the non-invasive assessment of patients with suspected coronary artery disease (CAD) and cardiovascular risk stratification. To date, artificial intelligence (AI) techniques have brought major changes in the way that we make individualized decisions for patients with CAD. Applications of AI in CCTA have produced improvements in many aspects, including assessment of stenosis degree, determination of plaque type, identification of high-risk plaque, quantification of coronary artery calcium score, diagnosis of myocardial infarction, estimation of computed tomography-derived fractional flow reserve, left ventricular myocardium analysis, perivascular adipose tissue analysis, prognosis of CAD, and so on. The purpose of this review is to provide a comprehensive overview of current status of AI in CCTA.
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Affiliation(s)
- Zhe-Zhe Zhang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang 110004, Liaoning Province, China
| | - Yan Guo
- GE Healthcare, Beijing 100176, China
| | - Yang Hou
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang 110004, Liaoning Province, China
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12
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Chao H, Shan H, Homayounieh F, Singh R, Khera RD, Guo H, Su T, Wang G, Kalra MK, Yan P. Deep learning predicts cardiovascular disease risks from lung cancer screening low dose computed tomography. Nat Commun 2021; 12:2963. [PMID: 34017001 PMCID: PMC8137697 DOI: 10.1038/s41467-021-23235-4] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 04/20/2021] [Indexed: 12/14/2022] Open
Abstract
Cancer patients have a higher risk of cardiovascular disease (CVD) mortality than the general population. Low dose computed tomography (LDCT) for lung cancer screening offers an opportunity for simultaneous CVD risk estimation in at-risk patients. Our deep learning CVD risk prediction model, trained with 30,286 LDCTs from the National Lung Cancer Screening Trial, achieves an area under the curve (AUC) of 0.871 on a separate test set of 2,085 subjects and identifies patients with high CVD mortality risks (AUC of 0.768). We validate our model against ECG-gated cardiac CT based markers, including coronary artery calcification (CAC) score, CAD-RADS score, and MESA 10-year risk score from an independent dataset of 335 subjects. Our work shows that, in high-risk patients, deep learning can convert LDCT for lung cancer screening into a dual-screening quantitative tool for CVD risk estimation.
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Affiliation(s)
- Hanqing Chao
- Department of Biomedical Engineering, Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Hongming Shan
- Department of Biomedical Engineering, Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Fatemeh Homayounieh
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ramandeep Singh
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ruhani Doda Khera
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hengtao Guo
- Department of Biomedical Engineering, Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Timothy Su
- Niskayuna High School, Niskayuna, NY, USA
| | - Ge Wang
- Department of Biomedical Engineering, Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, NY, USA.
| | - Mannudeep K Kalra
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Pingkun Yan
- Department of Biomedical Engineering, Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, NY, USA.
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13
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Slart RHJA, Williams MC, Juarez-Orozco LE, Rischpler C, Dweck MR, Glaudemans AWJM, Gimelli A, Georgoulias P, Gheysens O, Gaemperli O, Habib G, Hustinx R, Cosyns B, Verberne HJ, Hyafil F, Erba PA, Lubberink M, Slomka P, Išgum I, Visvikis D, Kolossváry M, Saraste A. Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT. Eur J Nucl Med Mol Imaging 2021; 48:1399-1413. [PMID: 33864509 PMCID: PMC8113178 DOI: 10.1007/s00259-021-05341-z] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 03/25/2021] [Indexed: 12/18/2022]
Abstract
In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.
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Affiliation(s)
- Riemer H J A Slart
- Medical Imaging Centre, Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands.
- Faculty of Science and Technology Biomedical, Photonic Imaging, University of Twente, Enschede, The Netherlands.
| | - Michelle C Williams
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging facility QMRI, Edinburgh, UK
| | - Luis Eduardo Juarez-Orozco
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Christoph Rischpler
- Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Marc R Dweck
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging facility QMRI, Edinburgh, UK
| | - Andor W J M Glaudemans
- Medical Imaging Centre, Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands
| | | | - Panagiotis Georgoulias
- Department of Nuclear Medicine, Faculty of Medicine, University of Thessaly, University Hospital of Larissa, Larissa, Greece
| | - Olivier Gheysens
- Department of Nuclear Medicine, Cliniques Universitaires Saint-Luc and Institute of Clinical and Experimental Research (IREC), Université catholique de Louvain (UCLouvain), Brussels, Belgium
| | | | - Gilbert Habib
- APHM, Cardiology Department, La Timone Hospital, Marseille, France
- IRD, APHM, MEPHI, IHU-Méditerranée Infection, Aix Marseille Université, Marseille, France
| | - Roland Hustinx
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, ULiège, Liège, Belgium
| | - Bernard Cosyns
- Department of Cardiology, Centrum voor Hart en Vaatziekten, Universitair Ziekenhuis Brussel, 101 Laarbeeklaan, 1090, Brussels, Belgium
| | - Hein J Verberne
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location AMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Fabien Hyafil
- Department of Nuclear Medicine, DMU IMAGINA, Georges-Pompidou European Hospital, Assistance Publique - Hôpitaux de Paris, F-75015, Paris, France
- University of Paris, PARCC, INSERM, F-75006, Paris, France
| | - Paola A Erba
- Medical Imaging Centre, Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands
- Department of Nuclear Medicine (P.A.E.), University of Pisa, Pisa, Italy
- Department of Translational Research and New Technology in Medicine (P.A.E.), University of Pisa, Pisa, Italy
| | - Mark Lubberink
- Department of Surgical Sciences/Radiology, Uppsala University, Uppsala, Sweden
- Medical Physics, Uppsala University Hospital, Uppsala, Sweden
| | - Piotr Slomka
- Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ivana Išgum
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location AMC, University of Amsterdam, Amsterdam, The Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam UMC - location AMC, University of Amsterdam, 1105, Amsterdam, AZ, Netherlands
| | | | - Márton Kolossváry
- MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, 68 Városmajor Street, Budapest, Hungary
| | - Antti Saraste
- Turku PET Centre, Turku University Hospital, University of Turku, Turku, Finland
- Heart Center, Turku University Hospital, Turku, Finland
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14
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Ferrari D, Milic J, Tonelli R, Ghinelli F, Meschiari M, Volpi S, Faltoni M, Franceschi G, Iadisernia V, Yaacoub D, Ciusa G, Bacca E, Rogati C, Tutone M, Burastero G, Raimondi A, Menozzi M, Franceschini E, Cuomo G, Corradi L, Orlando G, Santoro A, Digaetano M, Puzzolante C, Carli F, Borghi V, Bedini A, Fantini R, Tabbì L, Castaniere I, Busani S, Clini E, Girardis M, Sarti M, Cossarizza A, Mussini C, Mandreoli F, Missier P, Guaraldi G. Machine learning in predicting respiratory failure in patients with COVID-19 pneumonia-Challenges, strengths, and opportunities in a global health emergency. PLoS One 2020; 15:e0239172. [PMID: 33180787 PMCID: PMC7660476 DOI: 10.1371/journal.pone.0239172] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 09/02/2020] [Indexed: 01/15/2023] Open
Abstract
AIMS The aim of this study was to estimate a 48 hour prediction of moderate to severe respiratory failure, requiring mechanical ventilation, in hospitalized patients with COVID-19 pneumonia. METHODS This was an observational prospective study that comprised consecutive patients with COVID-19 pneumonia admitted to hospital from 21 February to 6 April 2020. The patients' medical history, demographic, epidemiologic and clinical data were collected in an electronic patient chart. The dataset was used to train predictive models using an established machine learning framework leveraging a hybrid approach where clinical expertise is applied alongside a data-driven analysis. The study outcome was the onset of moderate to severe respiratory failure defined as PaO2/FiO2 ratio <150 mmHg in at least one of two consecutive arterial blood gas analyses in the following 48 hours. Shapley Additive exPlanations values were used to quantify the positive or negative impact of each variable included in each model on the predicted outcome. RESULTS A total of 198 patients contributed to generate 1068 usable observations which allowed to build 3 predictive models based respectively on 31-variables signs and symptoms, 39-variables laboratory biomarkers and 91-variables as a composition of the two. A fourth "boosted mixed model" included 20 variables was selected from the model 3, achieved the best predictive performance (AUC = 0.84) without worsening the FN rate. Its clinical performance was applied in a narrative case report as an example. CONCLUSION This study developed a machine model with 84% prediction accuracy, which is able to assist clinicians in decision making process and contribute to develop new analytics to improve care at high technology readiness levels.
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Affiliation(s)
- Davide Ferrari
- Department of Surgical, Medical, Dental and Morphological Sciences, University of Modena and Reggio Emilia, Modena, Italy
- Department of Physical, Computer and Mathematical Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Jovana Milic
- Department of Surgical, Medical, Dental and Morphological Sciences, University of Modena and Reggio Emilia, Modena, Italy
- Clinical and Experimental Medicine PhD Program, University of Modena and Reggio Emilia, Modena, Italy
| | - Roberto Tonelli
- Clinical and Experimental Medicine PhD Program, University of Modena and Reggio Emilia, Modena, Italy
- Respiratory Diseases Unit, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy
| | - Francesco Ghinelli
- Department of Physical, Computer and Mathematical Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Marianna Meschiari
- Department of Infectious Diseases, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy
| | - Sara Volpi
- Department of Infectious Diseases, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy
| | - Matteo Faltoni
- Respiratory Diseases Unit, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy
| | - Giacomo Franceschi
- Department of Infectious Diseases, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy
| | - Vittorio Iadisernia
- Department of Infectious Diseases, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy
| | - Dina Yaacoub
- Department of Infectious Diseases, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy
| | - Giacomo Ciusa
- Department of Infectious Diseases, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy
| | - Erica Bacca
- Department of Infectious Diseases, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy
| | - Carlotta Rogati
- Department of Infectious Diseases, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy
| | - Marco Tutone
- Department of Infectious Diseases, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy
| | - Giulia Burastero
- Department of Infectious Diseases, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy
| | - Alessandro Raimondi
- Department of Infectious Diseases, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy
| | - Marianna Menozzi
- Department of Infectious Diseases, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy
| | - Erica Franceschini
- Department of Infectious Diseases, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy
| | - Gianluca Cuomo
- Department of Infectious Diseases, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy
| | - Luca Corradi
- Department of Infectious Diseases, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy
| | - Gabriella Orlando
- Department of Infectious Diseases, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy
| | - Antonella Santoro
- Department of Infectious Diseases, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy
| | - Margherita Digaetano
- Department of Infectious Diseases, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy
| | - Cinzia Puzzolante
- Department of Infectious Diseases, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy
| | - Federica Carli
- Department of Infectious Diseases, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy
| | - Vanni Borghi
- Department of Infectious Diseases, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy
| | - Andrea Bedini
- Department of Infectious Diseases, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy
| | - Riccardo Fantini
- Respiratory Diseases Unit, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy
| | - Luca Tabbì
- Respiratory Diseases Unit, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy
| | - Ivana Castaniere
- Clinical and Experimental Medicine PhD Program, University of Modena and Reggio Emilia, Modena, Italy
- Respiratory Diseases Unit, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy
| | - Stefano Busani
- Department of Anesthesia and Intensive Care Unit, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy
| | - Enrico Clini
- Respiratory Diseases Unit, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy
- Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, Modena, Italy
| | - Massimo Girardis
- Department of Surgical, Medical, Dental and Morphological Sciences, University of Modena and Reggio Emilia, Modena, Italy
- Department of Anesthesia and Intensive Care Unit, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy
| | - Mario Sarti
- Clinical Microbiology, Ospedale Civile di Baggiovara, Modena, Italy
| | - Andrea Cossarizza
- Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, Modena, Italy
| | - Cristina Mussini
- Department of Surgical, Medical, Dental and Morphological Sciences, University of Modena and Reggio Emilia, Modena, Italy
- Department of Infectious Diseases, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy
| | - Federica Mandreoli
- Department of Physical, Computer and Mathematical Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Paolo Missier
- School of Computing, Newcastle University, Newcastle upon Tyne, United kingdom
| | - Giovanni Guaraldi
- Department of Surgical, Medical, Dental and Morphological Sciences, University of Modena and Reggio Emilia, Modena, Italy
- Department of Infectious Diseases, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy
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15
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Jiang B, Guo N, Ge Y, Zhang L, Oudkerk M, Xie X. Development and application of artificial intelligence in cardiac imaging. Br J Radiol 2020; 93:20190812. [PMID: 32017605 DOI: 10.1259/bjr.20190812] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
In this review, we describe the technical aspects of artificial intelligence (AI) in cardiac imaging, starting with radiomics, basic algorithms of deep learning and application tasks of algorithms, until recently the availability of the public database. Subsequently, we conducted a systematic literature search for recently published clinically relevant studies on AI in cardiac imaging. As a result, 24 and 14 studies using CT and MRI, respectively, were included and summarized. From these studies, it can be concluded that AI is widely applied in cardiac applications in the clinic, including coronary calcium scoring, coronary CT angiography, fractional flow reserve CT, plaque analysis, left ventricular myocardium analysis, diagnosis of myocardial infarction, prognosis of coronary artery disease, assessment of cardiac function, and diagnosis and prognosis of cardiomyopathy. These advancements show that AI has a promising prospect in cardiac imaging.
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Affiliation(s)
- Beibei Jiang
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai 200080, China
| | - Ning Guo
- Shukun (Beijing) Technology Co, Ltd., Jinhui Bd, Qiyang Rd, Beijing 100102, China
| | - Yinghui Ge
- Radiology Department, Central China Fuwai Hospital, Fuwai Avenue 1, Zhengzhou 450046, China
| | - Lu Zhang
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai 200080, China
| | - Matthijs Oudkerk
- Institute for DiagNostic Accuracy, Prof. Wiersma Straat 5, 9713GH Groningen, The Netherlands.,University of Groningen, Faculty of Medical Sciences, 9700AB Groningen, The Netherlands
| | - Xueqian Xie
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai 200080, China
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16
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Peper J, Suchá D, Swaans M, Leiner T. Functional cardiac CT-Going beyond Anatomical Evaluation of Coronary Artery Disease with Cine CT, CT-FFR, CT Perfusion and Machine Learning. Br J Radiol 2020; 93:20200349. [PMID: 32783626 DOI: 10.1259/bjr.20200349] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
The aim of this review is to provide an overview of different functional cardiac CT techniques which can be used to supplement assessment of the coronary arteries to establish the significance of coronary artery stenoses. We focus on cine-CT, CT-FFR, CT-myocardial perfusion and how developments in machine learning can supplement these techniques.
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Affiliation(s)
- Joyce Peper
- Department of Cardiology, St. Antonius Hospital Koekoekslaan 1, Nieuwegein, the Netherlands.,Department of Radiology, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Dominika Suchá
- Department of Radiology, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Martin Swaans
- Department of Cardiology, St. Antonius Hospital Koekoekslaan 1, Nieuwegein, the Netherlands
| | - Tim Leiner
- Department of Radiology, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
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17
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Carson JM, Chakshu NK, Sazonov I, Nithiarasu P. Artificial intelligence approaches to predict coronary stenosis severity using non-invasive fractional flow reserve. Proc Inst Mech Eng H 2020; 234:1337-1350. [PMID: 32741245 PMCID: PMC7675765 DOI: 10.1177/0954411920946526] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Fractional flow reserve is the current reference standard in the assessment of the functional impact of a stenosis in coronary heart disease. In this study, three models of artificial intelligence of varying degrees of complexity were compared to fractional flow reserve measurements. The three models are the multivariate polynomial regression, which is a statistical method used primarily for correlation; the feed-forward neural network; and the long short-term memory, which is a type of recurrent neural network that is suited to modelling sequences. The models were initially trained using a virtual patient database that was generated from a validated one-dimensional physics-based model. The feed-forward neural network performed the best for all test cases considered, which were a single vessel case from a virtual patient database, a multi-vessel network from a virtual patient database, and 25 clinically invasive fractional flow reserve measurements from real patients. The feed-forward neural network model achieved around 99% diagnostic accuracy in both tests involving virtual patients, and a respectable 72% diagnostic accuracy when compared to the invasive fractional flow reserve measurements. The multivariate polynomial regression model performed well in the single vessel case, but struggled on network cases as the variation of input features was much larger. The long short-term memory performed well for the single vessel cases, but tended to have a bias towards a positive fractional flow reserve prediction for the virtual multi-vessel case, and for the patient cases. Overall, the feed-forward neural network shows promise in successfully predicting fractional flow reserve in real patients, and could be a viable option if trained using a large enough data set of real patients.
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Affiliation(s)
- Jason M Carson
- Zienkiewicz Centre for Computational Engineering, College of Engineering, Swansea University, Swansea, UK.,Swansea University Medical School, Swansea University, Swansea, UK.,HDR UK Wales and Northern Ireland, Health Data Research UK, London, UK
| | - Neeraj Kavan Chakshu
- Zienkiewicz Centre for Computational Engineering, College of Engineering, Swansea University, Swansea, UK
| | - Igor Sazonov
- Zienkiewicz Centre for Computational Engineering, College of Engineering, Swansea University, Swansea, UK
| | - Perumal Nithiarasu
- Zienkiewicz Centre for Computational Engineering, College of Engineering, Swansea University, Swansea, UK
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18
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Updates on Fractional Flow Reserve Derived by CT (FFRCT). CURRENT TREATMENT OPTIONS IN CARDIOVASCULAR MEDICINE 2020. [DOI: 10.1007/s11936-020-00816-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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19
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From CT to artificial intelligence for complex assessment of plaque-associated risk. Int J Cardiovasc Imaging 2020; 36:2403-2427. [PMID: 32617720 DOI: 10.1007/s10554-020-01926-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Accepted: 06/25/2020] [Indexed: 02/07/2023]
Abstract
The recent technological developments in the field of cardiac imaging have established coronary computed tomography angiography (CCTA) as a first-line diagnostic tool in patients with suspected coronary artery disease (CAD). CCTA offers robust information on the overall coronary circulation and luminal stenosis, also providing the ability to assess the composition, morphology, and vulnerability of atherosclerotic plaques. In addition, the perivascular adipose tissue (PVAT) has recently emerged as a marker of increased cardiovascular risk. The addition of PVAT quantification to standard CCTA imaging may provide the ability to extract information on local inflammation, for an individualized approach in coronary risk stratification. The development of image post-processing tools over the past several years allowed CCTA to provide a significant amount of data that can be incorporated into machine learning (ML) applications. ML algorithms that use radiomic features extracted from CCTA are still at an early stage. However, the recent development of artificial intelligence will probably bring major changes in the way we integrate clinical, biological, and imaging information, for a complex risk stratification and individualized therapeutic decision making in patients with CAD. This review aims to present the current evidence on the complex role of CCTA in the detection and quantification of vulnerable plaques and the associated coronary inflammation, also describing the most recent developments in the radiomics-based machine learning approach for complex assessment of plaque-associated risk.
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20
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Zhao FJ, Fan SQ, Ren JF, von Deneen KM, He XW, Chen XL. Machine learning for diagnosis of coronary artery disease in computed tomography angiography: A survey. Artif Intell Med Imaging 2020; 1:31-39. [DOI: 10.35711/aimi.v1.i1.31] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 06/12/2020] [Accepted: 06/16/2020] [Indexed: 02/06/2023] Open
Abstract
Coronary artery disease (CAD) has become a major illness endangering human health. It mainly manifests as atherosclerotic plaques, especially vulnerable plaques without obvious symptoms in the early stage. Once a rupture occurs, it will lead to severe coronary stenosis, which in turn may trigger a major adverse cardiovascular event. Computed tomography angiography (CTA) has become a standard diagnostic tool for early screening of coronary plaque and stenosis due to its advantages in high resolution, noninvasiveness, and three-dimensional imaging. However, manual examination of CTA images by radiologists has been proven to be tedious and time-consuming, which might also lead to intra- and interobserver errors. Nowadays, many machine learning algorithms have enabled the (semi-)automatic diagnosis of CAD by extracting quantitative features from CTA images. This paper provides a survey of these machine learning algorithms for the diagnosis of CAD in CTA images, including coronary artery extraction, coronary plaque detection, vulnerable plaque identification, and coronary stenosis assessment. Most included articles were published within this decade and are found in the Web of Science. We wish to give readers a glimpse of the current status, challenges, and perspectives of these machine learning-based analysis methods for automatic CAD diagnosis.
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Affiliation(s)
- Feng-Jun Zhao
- School of Information Science and Technology, Northwest University, Xi’an 710069, Shaanxi Province, China
- Xi’an Key Lab of Radiomics and Intelligent Perception, Northwest University, Xi’an 710069, Shaanxi Province, China
| | - Si-Qi Fan
- School of Information Science and Technology, Northwest University, Xi’an 710069, Shaanxi Province, China
- Xi’an Key Lab of Radiomics and Intelligent Perception, Northwest University, Xi’an 710069, Shaanxi Province, China
| | - Jing-Fang Ren
- School of Information Science and Technology, Northwest University, Xi’an 710069, Shaanxi Province, China
- Xi’an Key Lab of Radiomics and Intelligent Perception, Northwest University, Xi’an 710069, Shaanxi Province, China
| | - Karen M von Deneen
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an 710126, Shaanxi Province, China
| | - Xiao-Wei He
- School of Information Science and Technology, Northwest University, Xi’an 710069, Shaanxi Province, China
- Xi’an Key Lab of Radiomics and Intelligent Perception, Northwest University, Xi’an 710069, Shaanxi Province, China
| | - Xue-Li Chen
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an 710126, Shaanxi Province, China
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21
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Sommer KN, Shepard LM, Mitsouras D, Iyer V, Angel E, Wilson MF, Rybicki FJ, Kumamaru KK, Sharma UC, Reddy A, Fujimoto S, Ionita CN. Patient-specific 3D-printed coronary models based on coronary computed tomography angiography volumes to investigate flow conditions in coronary artery disease. Biomed Phys Eng Express 2020; 6:045007. [PMID: 33444268 DOI: 10.1088/2057-1976/ab8f6e] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND 3D printed patient-specific coronary models have the ability to enable repeatable benchtop experiments under controlled blood flow conditions. This approach can be applied to CT-derived patient geometries to emulate coronary flow and related parameters such as Fractional Flow Reserve (FFR). METHODS This study uses 3D printing to compare such benchtop FFR results with a non-invasive CT-FFR research software algorithm and catheter based invasive FFR (I-FFR) measurements. Fifty-two patients with a clinical indication for I-FFR underwent a research Coronary CT Angiography (CCTA) prior to catheterization. CT images were used to measure CT-FFR and to generate patient-specific 3D printed models of the aortic root and three main coronary arteries. Each patient-specific model was connected to a programmable pulsatile pump and benchtop FFR (B-FFR) was derived from pressures measured proximal and distal to coronary stenosis using pressure transducers. B-FFR was measured for two coronary outflow rates ('normal', 250 ml min-1; and 'hyperemic', 500 ml min-1) by adjusting the model's distal coronary resistance. RESULTS Pearson correlations and ROC AUC were calculated using invasive I-FFR as reference. The Pearson correlation factor of CT-FFR and B-FFR-500 was 0.75 and 0.71, respectively. Areas under the ROCs for CT-FFR and B-FFR-500 were 0.80 (95%CI: 0.70-0.87) and 0.81 (95%CI: 0.64-0.91) respectively. CONCLUSION Benchtop flow simulations with 3D printed models provide the capability to measure pressure changes at any location in the model, for ultimately emulating the FFR at several simulated physiological blood flow conditions. CLINICAL TRIAL REGISTRATION https://clinicaltrials.gov/show/NCT03149042.
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Affiliation(s)
- Kelsey N Sommer
- Department of Biomedical Engineering, University at Buffalo, Buffalo, NY 14228, United States of America. Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, NY, United States of America
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Hampe N, Wolterink JM, van Velzen SGM, Leiner T, Išgum I. Machine Learning for Assessment of Coronary Artery Disease in Cardiac CT: A Survey. Front Cardiovasc Med 2019; 6:172. [PMID: 32039237 PMCID: PMC6988816 DOI: 10.3389/fcvm.2019.00172] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 11/12/2019] [Indexed: 01/10/2023] Open
Abstract
Cardiac computed tomography (CT) allows rapid visualization of the heart and coronary arteries with high spatial resolution. However, analysis of cardiac CT scans for manifestation of coronary artery disease is time-consuming and challenging. Machine learning (ML) approaches have the potential to address these challenges with high accuracy and consistent performance. In this mini review, we present a survey of the literature on ML-based analysis of coronary artery disease in cardiac CT. We summarize ML methods for detection and characterization of atherosclerotic plaque as well as anatomically and functionally significant coronary artery stenosis.
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Affiliation(s)
- Nils Hampe
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.,Amsterdam Cardiovascular Sciences, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.,Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - Jelmer M Wolterink
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.,Amsterdam Cardiovascular Sciences, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.,Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - Sanne G M van Velzen
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - Tim Leiner
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Ivana Išgum
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.,Amsterdam Cardiovascular Sciences, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.,Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands.,Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
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