1
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Sun X, Zhu Y, Zhang N, Yuan K, Ling J, Ye J. Prognostic value of serial coronary computed tomography angiography-derived perivascular fat-attenuation index and plaque volume in patients with suspected coronary artery disease. Clin Radiol 2024; 79:599-607. [PMID: 38755080 DOI: 10.1016/j.crad.2024.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 04/04/2024] [Accepted: 04/16/2024] [Indexed: 05/18/2024]
Abstract
AIMS To investigate the prognostic value of serial coronary computed tomography angiography (CCTA) derived plaque information, fractional flow reserve (CT-FFR), and perivascular fat-attenuation index (FAI) on major adverse cardiac events (MACE) in patients with suspected coronary artery disease. MATERIALS AND METHODS A total of 252 patients who underwent serial CCTA between January 2018 and December 2021 and were followed until June 2022. MACE were recorded. The analysis indexes included percent diameter stenosis (%DS), lesion length, plaque volume, CT-FFR, and FAI, with an emphasis on their changes between the baseline and follow-up CCTAs. Multivariate regression analysis were employed to identify independent risk factors for MACE. RESULTS After a median follow-up of 48-month, MACE occurred in 32 patients (12.7%). Patients with MACE displayed more severe stenosis, longer lesions, and larger plaque volumes in both baseline and follow-up CCTAs compared with no-MACE patients (all P<0.05). Patients with MACE displayed more severe stenosis, longer lesion, and larger plaque volume in both baseline and follow-up CCTAs compared with no-MACE patients. In addition, MACE patients also showed lower CT-FFR and higher △CT-FFR. Although FAI was significantly higher in MACE patients at baseline CCTA, FAI was notably increased in MACE patients, and decreased in the no-MACE patients (all P<0.05). Logistic regression analysis showed that ΔFAI, %DS, and plaque volume were independent predictors of MACE, with ΔFAI being the most significant (OR: 16.725, P<0.000). A multivariable model showed a significantly improved C-index of 0.903 (95% confidence interval: 0.836-0.970) for MACE prediction, when compared with single index alone. CONCLUSIONS Serial CCTA-derived ΔFAI, %DS, and plaque volume are crucial independent predictors of MACE in patients with suspected coronary artery disease, highlighting the importance of CCTA in patient risk stratification and prognostic assessment.
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Affiliation(s)
- X Sun
- Department of Radiology, Northern Jiangsu People's Hospital, Clinical Medical School of Yangzhou University, Yangzhou, PR China
| | - Y Zhu
- Department of Radiology, Northern Jiangsu People's Hospital, Clinical Medical School of Yangzhou University, Yangzhou, PR China
| | - N Zhang
- Department of Radiology, Northern Jiangsu People's Hospital, Clinical Medical School of Yangzhou University, Yangzhou, PR China
| | - K Yuan
- Department of Cadiology, Northern Jiangsu People's Hospital, Clinical Medical School of Yangzhou University, Yangzhou, PR China
| | - J Ling
- Department of Radiology, Northern Jiangsu People's Hospital, Clinical Medical School of Yangzhou University, Yangzhou, PR China.
| | - J Ye
- Department of Radiology, Northern Jiangsu People's Hospital, Clinical Medical School of Yangzhou University, Yangzhou, PR China.
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Lee SN, Lin A, Dey D, Berman DS, Han D. Application of Quantitative Assessment of Coronary Atherosclerosis by Coronary Computed Tomographic Angiography. Korean J Radiol 2024; 25:518-539. [PMID: 38807334 PMCID: PMC11136945 DOI: 10.3348/kjr.2023.1311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 02/29/2024] [Accepted: 03/23/2024] [Indexed: 05/30/2024] Open
Abstract
Coronary computed tomography angiography (CCTA) has emerged as a pivotal tool for diagnosing and risk-stratifying patients with suspected coronary artery disease (CAD). Recent advancements in image analysis and artificial intelligence (AI) techniques have enabled the comprehensive quantitative analysis of coronary atherosclerosis. Fully quantitative assessments of coronary stenosis and lumen attenuation have improved the accuracy of assessing stenosis severity and predicting hemodynamically significant lesions. In addition to stenosis evaluation, quantitative plaque analysis plays a crucial role in predicting and monitoring CAD progression. Studies have demonstrated that the quantitative assessment of plaque subtypes based on CT attenuation provides a nuanced understanding of plaque characteristics and their association with cardiovascular events. Quantitative analysis of serial CCTA scans offers a unique perspective on the impact of medical therapies on plaque modification. However, challenges such as time-intensive analyses and variability in software platforms still need to be addressed for broader clinical implementation. The paradigm of CCTA has shifted towards comprehensive quantitative plaque analysis facilitated by technological advancements. As these methods continue to evolve, their integration into routine clinical practice has the potential to enhance risk assessment and guide individualized patient management. This article reviews the evolving landscape of quantitative plaque analysis in CCTA and explores its applications and limitations.
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Affiliation(s)
- Su Nam Lee
- Department of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Division of Cardiology, Department of Internal Medicine, St. Vincent's Hospital, The Catholic University of Korea, Seoul, Republic of Korea
| | - Andrew Lin
- Monash Cardiovascular Research Centre, Victorian Heart Institute, Monash University and MonashHeart, Monash Health, Melbourne, Australia
| | - Damini Dey
- Department of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Daniel S Berman
- Department of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Donghee Han
- Department of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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3
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Chen W, Nie J, Zhang M, Zhu Z, Zhou Y, Wu Q, He X. The Plaque Analysis Classifies the Coronary Artery Disease-Reporting and Data System (CAD-RADS) Stenosis and Plaque Burden Categories: Association of the Plaque Features, Fat Attenuation Index, Coronary Computed Tomography Fractional Flow Reserve, and the Combination of Stenosis and Calcification. Clin Cardiol 2024; 47:e24305. [PMID: 38884449 PMCID: PMC11181293 DOI: 10.1002/clc.24305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 05/20/2024] [Accepted: 05/30/2024] [Indexed: 06/18/2024] Open
Abstract
BACKGROUND The coronary artery disease-reporting and data system (CAD-RADS) 2.0 is used to standardize the reporting of coronary computed tomography angiography (CCTA) results. Artificial intelligence software can quantify the plaque composition, fat attenuation index, and fractional flow reserve. OBJECTIVE To analyze plaque features of varying severity in patients with a combination of CAD-RADS stenosis and plaque burden categorization and establish a random forest classification model. METHODS The data of 100 patients treated between April 2021 and February 2022 were retrospectively collected. The most severe plaque observed in each patient was the target lesion. Patients were categorized into three groups according to CAD-RADS: CAD-RADS 1-2 + P0-2, CAD-RADS 3-4B + P0-2, and CAD-RADS 3-4B + P3-4. Differences and correlations between variables were assessed between groups. AUC, accuracy, precision, recall, and F1 score were used to evaluate the diagnostic performance. RESULTS A total of 100 patients and 178 arteries were included. The differences of computed tomography fractional flow reserve (CT-FFR) (H = 23.921, p < 0.001), the volume of lipid component (H = 12.996, p = 0.002), the volume of fibro-lipid component (H = 8.692, p = 0.013), the proportion of lipid component volume (H = 22.038, p < 0.001), the proportion of fibro-lipid component volume (H = 11.731, p = 0.003), the proportion of calcification component volume (H = 11.049, p = 0.004), and plaque type (χ2 = 18.110, p = 0.001) was statistically significant. CONCLUSION CT-FFR, volume and proportion of lipid and fibro-lipid components of plaques, the proportion of calcified components, and plaque type were valuable for CAD-RADS stenosis + plaque burden classification, especially CT-FFR, volume, and proportion of lipid and fibro-lipid components. The model built using the random forest was better than the clinical model (AUC: 0.874 vs. 0.647).
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Affiliation(s)
- Wenxi Chen
- Graduate SchoolGuangzhou University of Chinese MedicineGuangzhouChina
| | - Jiyan Nie
- Graduate SchoolGuangzhou University of Chinese MedicineGuangzhouChina
| | - Mingyu Zhang
- Graduate SchoolGuangzhou University of Chinese MedicineGuangzhouChina
| | - Zhi Zhu
- Graduate SchoolGuangzhou University of Chinese MedicineGuangzhouChina
- Department of RadiologyShunde Hospital of Guangzhou University of Chinese MedicineShundeChina
| | - Yuanyong Zhou
- Department of RadiologyShunde Hospital of Guangzhou University of Chinese MedicineShundeChina
| | - Qingde Wu
- Department of RadiologyShunde Hospital of Guangzhou University of Chinese MedicineShundeChina
| | - Xuxia He
- Department of RadiologyShunde Hospital of Guangzhou University of Chinese MedicineShundeChina
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4
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Omaygenc MO, Kadoya Y, Small GR, Chow BJW. Cardiac CT: Competition, complimentary or confounder. J Med Imaging Radiat Sci 2024; 55:S31-S38. [PMID: 38433089 DOI: 10.1016/j.jmir.2024.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 01/17/2024] [Accepted: 01/22/2024] [Indexed: 03/05/2024]
Abstract
Coronary CT angiography (CCTA) has been gradually adopted into clinical practice over the last two decades. CCTA has high diagnostic accuracy, prognostic value, and unique features such as assessment of plaque composition. CCTA-derived functional assessment techniques such as fractional flow reserve and CT perfusion are also available and can increase the diagnostic specificity of the modality. These properties propound CCTA as a competitor of functional testing in diagnosis of obstructive CAD, however, utilizing CCTA in a concomitant fashion to potentiate the performance of the latter can lead to better patient care and may provide more accurate prognostic information. Although multiple diagnostic challenges such as evaluation of calcified segments, stents, and small distal vessels still exist, the technologic developments in hardware as well as growing incorporation of artificial intelligence to daily practice are all set to augment the diagnostic and prognostic role of CCTA in cardiovascular disorders.
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Affiliation(s)
- Mehmet Onur Omaygenc
- Department of Medicine (Cardiology), University of Ottawa Heart Institute, 40 Ruskin Street, Ottawa, ON, K1Y 4W7, Canada.
| | - Yoshito Kadoya
- Department of Medicine (Cardiology), University of Ottawa Heart Institute, 40 Ruskin Street, Ottawa, ON, K1Y 4W7, Canada
| | - Gary Robert Small
- Department of Medicine (Cardiology), University of Ottawa Heart Institute, 40 Ruskin Street, Ottawa, ON, K1Y 4W7, Canada
| | - Benjamin Joe Wade Chow
- Department of Medicine (Cardiology), University of Ottawa Heart Institute, 40 Ruskin Street, Ottawa, ON, K1Y 4W7, Canada; Department of Radiology, University of Ottawa, Ottawa, Canada
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Buhl LF, Lehmann Christensen L, Diederichsen A, Lindholt JS, Kistorp CM, Glintborg D, Andersen M, Frystyk J. Impact of androgenic anabolic steroid use on cardiovascular and mental health in Danish recreational athletes: protocol for a nationwide cross-sectional cohort study as a part of the Fitness Doping in Denmark (FIDO-DK) study. BMJ Open 2024; 14:e078558. [PMID: 38719280 PMCID: PMC11086435 DOI: 10.1136/bmjopen-2023-078558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 04/26/2024] [Indexed: 05/12/2024] Open
Abstract
INTRODUCTION The use of androgenic anabolic steroids (AASs) among recreational athletes is steadily increasing. However, knowledge regarding the potentially harmful effects of AAS primarily originates from case reports and small observational studies. This large-scale study aims to investigate the impact of AAS use on vascular plaque formation, preclinical coronary disease, cardiac function, circulating cardiovascular risk markers, quality of life (QoL) and mental health in a broad population of illicit AAS users. METHODS AND ANALYSES A nationwide cross-sectional cohort study including a diverse population of men and women aged ≥18 years, with current or previous illicit AAS use for at least 3 months. Conducted at Odense University Hospital, Denmark, the study comprises two parts. In part A (the pilot study), 120 recreational athletes with an AAS history will be compared with a sex-matched and age-matched control population of 60 recreational athletes with no previous AAS use. Cardiovascular outcomes include examination of non-calcified coronary plaque volume and calcium score using coronary CT angiography, myocardial structure and function via echocardiography, and assessing carotid and femoral artery plaques using ultrasonography. Retinal microvascular status is evaluated through fundus photography. Cardiovascular risk markers are measured in blood. Mental health outcomes include health-related QoL, interpersonal difficulties, body image concerns, aggression dimensions, anxiety symptoms, depressive severity and cognitive function assessed through validated questionnaires. The findings of our comprehensive study will be used to compose a less intensive investigatory cohort study of cardiovascular and mental health (part B) involving a larger group of recreational athletes with a history of illicit AAS use. ETHICS AND DISSEMINATION The study received approval from the Regional Committee on Health Research Ethics for Southern Denmark (S-20210078) and the Danish Data Protection Agency (21/28259). All participants will provide signed informed consent. Research outcomes will be disseminated through peer-reviewed journals and scientific conferences. TRIAL REGISTRATION NUMBER NCT05178537.
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Affiliation(s)
- Laust Frisenberg Buhl
- Department of Endocrinology, University of Southern Denmark Faculty of Health Sciences, Odense, Denmark
| | | | - Axel Diederichsen
- Department of Cardiology, Odense University Hospital, Odense, Denmark
| | | | - Caroline Michaela Kistorp
- Department of Hormones and Metabolism, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Kobenhavn, Denmark
| | - Dorte Glintborg
- Department of Endocrinology, Faculty of Health Sciences University of Southern Denmark, Odense, Denmark
| | - Marianne Andersen
- Department of Endocrinology, Faculty of Health Sciences University of Southern Denmark, Odense, Denmark
| | - Jan Frystyk
- Department of Endocrinology, Faculty of Health Sciences University of Southern Denmark, Odense, Denmark
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6
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Zhang X, Zhang B, Zhang F. Stenosis Detection and Quantification of Coronary Artery Using Machine Learning and Deep Learning. Angiology 2024; 75:405-416. [PMID: 37399509 DOI: 10.1177/00033197231187063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/05/2023]
Abstract
The aim of this review is to introduce some applications of artificial intelligence (AI) algorithms for the detection and quantification of coronary stenosis using computed tomography angiography (CTA). The realization of automatic/semi-automatic stenosis detection and quantification includes the following steps: vessel central axis extraction, vessel segmentation, stenosis detection, and quantification. Many new AI techniques, such as machine learning and deep learning, have been widely used in medical image segmentation and stenosis detection. This review also summarizes the recent progress regarding coronary stenosis detection and quantification, and discusses the development trends in this field. Through evaluation and comparison, researchers can better understand the research frontier in related fields, compare the advantages and disadvantages of various methods, and better optimize the new technologies. Machine learning and deep learning will promote the process of automatic detection and quantification of coronary artery stenosis. However, the machine learning and the deep learning methods need a large amount of data, so they also face some challenges because of the lack of professional image annotations (manually add labels by experts).
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Affiliation(s)
- Xinhong Zhang
- School of Software, Henan University, Kaifeng, China
| | - Boyan Zhang
- School of Software, Henan University, Kaifeng, China
| | - Fan Zhang
- Huaihe Hospital, Henan University, Kaifeng, China
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7
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Lopez-Candales A, Sawalha K, Asif T. Nonobstructive epicardial coronary artery disease: an evolving concept in need of diagnostic and therapeutic guidance. Postgrad Med 2024; 136:366-376. [PMID: 38818874 DOI: 10.1080/00325481.2024.2360888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 05/23/2024] [Indexed: 06/01/2024]
Abstract
For decades, we have been treating patients presenting with angina and concerning electrocardiographic changes indicative of ischemia or injury, in whom no culprit epicardial coronary stenosis was found during diagnostic coronary angiography. Unfortunately, the clinical outcomes of these patients were not better than those with recognized obstructive coronary disease. Improvements in technology have allowed us to better characterize these patients. Consequently, an increasing number of patients with ischemia and no obstructive coronary artery disease (INOCA) or myocardial infarction in the absence of coronary artery disease (MINOCA) have now gained formal recognition and are more commonly encountered in clinical practice. Although both entities might share functional similarities at their core, they pose significant diagnostic and therapeutic challenges. Unless we become more proficient in identifying these patients, particularly those at higher risk, morbidity and mortality outcomes will not improve. Though this field remains in constant flux, data continue to become available. Therefore, we thought it would be useful to highlight important milestones that have been recognized so we can all learn about these clinical entities. Despite all the progress made regarding INOCA and MINOCA, many important knowledge gaps continue to exist. For the time being, prompt identification and early diagnosis remain crucial in managing these patients. Even though we are still not clear whether intensive medical therapy alters clinical outcomes, we remain vigilant and wait for more data.
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Affiliation(s)
- Angel Lopez-Candales
- Cardiovascular Medicine Division University Health Truman Medical Center, University of Missouri-Kansas City, Kansas City, MO, USA
| | - Khalid Sawalha
- Cardiometabolic Fellowship, University Health Truman Medical Center and the University of Missouri-Kansas City, Kansas City, USA
| | - Talal Asif
- Division of Cardiovascular Diseases, University Health Truman Medical Center and the University of Missouri-Kansas City Kansas City, Kansas City, MO, USA
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8
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Cohen O, Kundel V, Robson P, Al-Taie Z, Suárez-Fariñas M, Shah NA. Achieving Better Understanding of Obstructive Sleep Apnea Treatment Effects on Cardiovascular Disease Outcomes through Machine Learning Approaches: A Narrative Review. J Clin Med 2024; 13:1415. [PMID: 38592223 PMCID: PMC10932326 DOI: 10.3390/jcm13051415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 02/13/2024] [Accepted: 02/17/2024] [Indexed: 04/10/2024] Open
Abstract
Obstructive sleep apnea (OSA) affects almost a billion people worldwide and is associated with a myriad of adverse health outcomes. Among the most prevalent and morbid are cardiovascular diseases (CVDs). Nonetheless, randomized controlled trials (RCTs) of OSA treatment have failed to show improvements in CVD outcomes. A major limitation in our field is the lack of precision in defining OSA and specifically subgroups with the potential to benefit from therapy. Further, this has called into question the validity of using the time-honored apnea-hypopnea index as the ultimate defining criteria for OSA. Recent applications of advanced statistical methods and machine learning have brought to light a variety of OSA endotypes and phenotypes. These methods also provide an opportunity to understand the interaction between OSA and comorbid diseases for better CVD risk stratification. Lastly, machine learning and specifically heterogeneous treatment effects modeling can help uncover subgroups with differential outcomes after treatment initiation. In an era of data sharing and big data, these techniques will be at the forefront of OSA research. Advanced data science methods, such as machine-learning analyses and artificial intelligence, will improve our ability to determine the unique influence of OSA on CVD outcomes and ultimately allow us to better determine precision medicine approaches in OSA patients for CVD risk reduction. In this narrative review, we will highlight how team science via machine learning and artificial intelligence applied to existing clinical data, polysomnography, proteomics, and imaging can do just that.
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Affiliation(s)
- Oren Cohen
- Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (O.C.); (V.K.)
| | - Vaishnavi Kundel
- Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (O.C.); (V.K.)
| | - Philip Robson
- Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA;
| | - Zainab Al-Taie
- Center for Biostatistics, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (Z.A.-T.); (M.S.-F.)
| | - Mayte Suárez-Fariñas
- Center for Biostatistics, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (Z.A.-T.); (M.S.-F.)
| | - Neomi A. Shah
- Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (O.C.); (V.K.)
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Arefinia F, Aria M, Rabiei R, Hosseini A, Ghaemian A, Roshanpoor A. Non-invasive fractional flow reserve estimation using deep learning on intermediate left anterior descending coronary artery lesion angiography images. Sci Rep 2024; 14:1818. [PMID: 38245614 PMCID: PMC10799954 DOI: 10.1038/s41598-024-52360-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Accepted: 01/17/2024] [Indexed: 01/22/2024] Open
Abstract
This study aimed to design an end-to-end deep learning model for estimating the value of fractional flow reserve (FFR) using angiography images to classify left anterior descending (LAD) branch angiography images with average stenosis between 50 and 70% into two categories: FFR > 80 and FFR ≤ 80. In this study 3625 images were extracted from 41 patients' angiography films. Nine pre-trained convolutional neural networks (CNN), including DenseNet121, InceptionResNetV2, VGG16, VGG19, ResNet50V2, Xception, MobileNetV3Large, DenseNet201, and DenseNet169, were used to extract the features of images. DenseNet169 indicated higher performance compared to other networks. AUC, Accuracy, Sensitivity, Specificity, Precision, and F1-score of the proposed DenseNet169 network were 0.81, 0.81, 0.86, 0.75, 0.82, and 0.84, respectively. The deep learning-based method proposed in this study can non-invasively and consistently estimate FFR from angiographic images, offering significant clinical potential for diagnosing and treating coronary artery disease by combining anatomical and physiological parameters.
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Affiliation(s)
- Farhad Arefinia
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mehrad Aria
- Cancer Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Reza Rabiei
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Azamossadat Hosseini
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Ali Ghaemian
- Department of Cardiology, Faculty of Medicine, Cardiovascular Research Center, Mazandaran University of Medical Sciences, Sari, Iran
| | - Arash Roshanpoor
- Department of Computer, Yadegar-e-Imam Khomeini (RAH), Islamic Azad University, Janat-Abad Branch, Tehran, Iran
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Föllmer B, Williams MC, Dey D, Arbab-Zadeh A, Maurovich-Horvat P, Volleberg RHJA, Rueckert D, Schnabel JA, Newby DE, Dweck MR, Guagliumi G, Falk V, Vázquez Mézquita AJ, Biavati F, Išgum I, Dewey M. Roadmap on the use of artificial intelligence for imaging of vulnerable atherosclerotic plaque in coronary arteries. Nat Rev Cardiol 2024; 21:51-64. [PMID: 37464183 DOI: 10.1038/s41569-023-00900-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/07/2023] [Indexed: 07/20/2023]
Abstract
Artificial intelligence (AI) is likely to revolutionize the way medical images are analysed and has the potential to improve the identification and analysis of vulnerable or high-risk atherosclerotic plaques in coronary arteries, leading to advances in the treatment of coronary artery disease. However, coronary plaque analysis is challenging owing to cardiac and respiratory motion, as well as the small size of cardiovascular structures. Moreover, the analysis of coronary imaging data is time-consuming, can be performed only by clinicians with dedicated cardiovascular imaging training, and is subject to considerable interreader and intrareader variability. AI has the potential to improve the assessment of images of vulnerable plaque in coronary arteries, but requires robust development, testing and validation. Combining human expertise with AI might facilitate the reliable and valid interpretation of images obtained using CT, MRI, PET, intravascular ultrasonography and optical coherence tomography. In this Roadmap, we review existing evidence on the application of AI to the imaging of vulnerable plaque in coronary arteries and provide consensus recommendations developed by an interdisciplinary group of experts on AI and non-invasive and invasive coronary imaging. We also outline future requirements of AI technology to address bias, uncertainty, explainability and generalizability, which are all essential for the acceptance of AI and its clinical utility in handling the anticipated growing volume of coronary imaging procedures.
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Affiliation(s)
- Bernhard Föllmer
- Department of Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany.
| | | | - Damini Dey
- Biomedical Imaging Research Institute and Department of Imaging, Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Armin Arbab-Zadeh
- Division of Cardiology, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Pál Maurovich-Horvat
- Department of Radiology, Medical Imaging Center, Semmelweis University, Budapest, Hungary
| | - Rick H J A Volleberg
- Department of Cardiology, Radboud University Medical Center, Nijmegen, Netherlands
| | - Daniel Rueckert
- Artificial Intelligence in Medicine and Healthcare, Technical University of Munich, Munich, Germany
- Department of Computing, Imperial College London, London, UK
| | - Julia A Schnabel
- School of Biomedical Imaging and Imaging Sciences, King's College London, London, UK
- Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Neuherberg, Germany
- School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - David E Newby
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Marc R Dweck
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Giulio Guagliumi
- Division of Cardiology, IRCCS Galeazzi Sant'Ambrogio Hospital, Milan, Italy
| | - Volkmar Falk
- Department of Cardiothoracic and Vascular Surgery, Deutsches Herzzentrum der Charité, Charité Universitätsmedizin, Berlin, Germany
- Department of Health Science and Technology, ETH Zurich, Zurich, Switzerland
- Berlin Institute of Health at Charité and DZHK (German Centre for Cardiovascular Research), Partner Site, Berlin, Germany
| | | | - Federico Biavati
- Department of Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ivana Išgum
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
- Informatics Institute, Faculty of Science, University of Amsterdam, Amsterdam, Netherlands
| | - Marc Dewey
- Department of Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany.
- Berlin Institute of Health, Campus Charité Mitte, Berlin, Germany.
- DZHK (German Centre for Cardiovascular Research), Partner Site Berlin and Deutsches Herzzentrum der Charité (DHZC), Charité - Universitätsmedizin Berlin, Berlin, Germany.
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11
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Unlu O, Fahed AC. Machine Learning in Invasive and Noninvasive Coronary Angiography. Curr Atheroscler Rep 2023; 25:1025-1033. [PMID: 38095805 DOI: 10.1007/s11883-023-01178-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/21/2023] [Indexed: 01/06/2024]
Abstract
PURPOSE OF REVIEW The objective of this review is to shed light on the transformative potential of machine learning (ML) in coronary angiography. We aim to understand existing developments in using ML for coronary angiography and discuss broader implications for the future of coronary angiography and cardiovascular medicine. RECENT FINDINGS The developments in invasive and noninvasive imaging have revolutionized diagnosis and treatment of coronary artery disease (CAD). However, CAD remains underdiagnosed and undertreated. ML has emerged as a powerful tool to further improve image analysis, hemodynamic assessment, lesion detection, and predictive modeling. These advancements have enabled more accurate identification of CAD, streamlined workflows, reduced the need for invasive diagnostic procedures, and improved the diagnostic value of invasive procedures when they are needed. Further integration of ML with coronary angiography will advance the prevention, diagnosis, and treatment of CAD. The integration of ML with coronary angiography is ushering in a new era in cardiovascular medicine. We highlight five use cases to leverage ML in coronary angiography: (1) improvement of quality and efficacy, (2) characterization of plaque, (3) hemodynamic assessment, (4) prediction of future outcomes, and (5) diagnosis of non-atherosclerotic coronary disease.
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Affiliation(s)
- Ozan Unlu
- Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Clinical Informatics, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Cardiovascular Disease Initiative and ML for Health, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Akl C Fahed
- Cardiovascular Disease Initiative and ML for Health, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, 185 Cambridge Street CPZN 3.128, Boston, MA, 02114, USA.
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12
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Yu W, Yang L, Zhang F, Liu B, Shi Y, Wang J, Shao X, Chen Y, Yang X, Wang Y. Machine learning to predict hemodynamically significant CAD based on traditional risk factors, coronary artery calcium and epicardial fat volume. J Nucl Cardiol 2023; 30:2593-2606. [PMID: 37434084 DOI: 10.1007/s12350-023-03333-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 06/15/2023] [Indexed: 07/13/2023]
Abstract
We sought to establish an explainable machine learning (ML) model to screen for hemodynamically significant coronary artery disease (CAD) based on traditional risk factors, coronary artery calcium (CAC) and epicardial fat volume (EFV) measured from non-contrast CT scans. 184 symptomatic inpatients who underwent Single Photon Emission Computed Tomography/Myocardial Perfusion Imaging (SPECT/MPI) and Invasive Coronary Angiography (ICA) were enrolled. Clinical and imaging features (CAC and EFV) were collected. Hemodynamically significant CAD was defined when coronary stenosis severity ≥ 50% with a matched reversible perfusion defect in SPECT/MPI. Data was randomly split into a training cohort (70%) on which five-fold cross-validation was done and a test cohort (30%). The normalized training phase was preceded by the selection of features using recursive feature elimination (RFE). Three ML classifiers (LR, SVM, and XGBoost) were used to construct and choose the best predictive model for hemodynamically significant CAD. An explainable approach based on ML and the SHapley Additive exPlanations (SHAP) method was deployed to generate individual explanation of the model's decision. In the training cohort, hemodynamically significant CAD patients had significantly higher age, BMI and EFV, higher proportions of hypertension and CAC comparing with controls (P all < .05). In the test cohorts, hemodynamically significant CAD had significantly higher EFV and higher proportion of CAC. EFV, CAC, diabetes mellitus (DM), hypertension, and hyperlipidemia were the highest ranking features by RFE. XGBoost produced better performance (AUC of 0.88) compared with traditional LR model (AUC of 0.82) and SVM (AUC of 0.82) in the training cohort. Decision Curve Analysis (DCA) demonstrated that XGBoost model had the highest Net Benefit index. Validation of the model also yielded a favorable discriminatory ability with the AUC, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of 0.89, 68.0%, 96.8%, 94.4%, 79.0% and 83.9% in the XGBoost model. A XGBoost model based on EFV, CAC, hypertension, DM and hyperlipidemia to assess hemodynamically significant CAD was constructed and validated, which showed favorable predictive value. ML combined with SHAP can offer a transparent explanation of personalized risk prediction, enabling physicians to gain an intuitive understanding of the impact of key features in the model.
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Affiliation(s)
- Wenji Yu
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, No.185, Juqian Street, Changzhou, 213003, Jiangsu, China
| | - Le Yang
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, No.185, Juqian Street, Changzhou, 213003, Jiangsu, China
| | - Feifei Zhang
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, No.185, Juqian Street, Changzhou, 213003, Jiangsu, China
| | - Bao Liu
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, No.185, Juqian Street, Changzhou, 213003, Jiangsu, China
| | - Yunmei Shi
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, No.185, Juqian Street, Changzhou, 213003, Jiangsu, China
| | - Jianfeng Wang
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, No.185, Juqian Street, Changzhou, 213003, Jiangsu, China
| | - Xiaoliang Shao
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, No.185, Juqian Street, Changzhou, 213003, Jiangsu, China
| | - Yongjun Chen
- Department of Cardiology, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China
| | - Xiaoyu Yang
- Department of Cardiology, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China
| | - Yuetao Wang
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, No.185, Juqian Street, Changzhou, 213003, Jiangsu, China.
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13
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Jaltotage B, Sukudom S, Ihdayhid AR, Dwivedi G. Enhancing Risk Stratification on Coronary Computed Tomography Angiography: The Role of Artificial Intelligence. Clin Ther 2023; 45:1023-1028. [PMID: 37813776 DOI: 10.1016/j.clinthera.2023.09.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 09/15/2023] [Accepted: 09/26/2023] [Indexed: 10/11/2023]
Abstract
PURPOSE To describe and outline the role of artificial intelligence (AI) in assisting coronary computed tomography angiography (CCTA) in enhancing risk stratification. METHODS A comprehensive review of the literature was performed to identify published work investigating the utility of applying AI to CCTA. FINDINGS CCTA is an excellent diagnostic tool for the detection of atherosclerotic cardiovascular disease. The noninvasive nature and high diagnostic accuracy have made CCTA a viable alternative to invasive coronary angiography to detect luminal stenosis. However, it is now understood that stenosis is just one factor that predicts cardiac risk and other factors need to be considered. CCTA-derived plaque biomarkers have since emerged as established predictors of cardiac events to improve risk stratification. Despite awareness of these biomarkers, they are still yet to be incorporated into routine clinical practice. The major barriers to implementation include the specialized skills required for image evaluation and the time intensive nature of analysis. With the many recent advancements in the technology, AI presents itself as a promising solution. AI is attractive because it has the potential to rapidly automate technically challenging tasks with exceptional accuracy. IMPLICATIONS Developments in the field of AI are occurring at a rapid rate. There is already increasing evidence of the potential AI has to greatly improve the utility of CCTA by improving analysis time and extracting additional prognostic data from new plaque biomarkers. There are, however, technical and ethical challenges that need to be considered before implementing such technology into routine clinical practice.
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Affiliation(s)
| | - Sara Sukudom
- Department of Cardiology, Fiona Stanley Hospital, Perth, Australia; Harry Perkins Institute of Medical Research, School of Medicine, University of Western Australia, Perth, Australia
| | - Abdul Rahman Ihdayhid
- Department of Cardiology, Fiona Stanley Hospital, Perth, Australia; Harry Perkins Institute of Medical Research, School of Medicine, University of Western Australia, Perth, Australia; School of Medicine, Curtin University, Perth, Australia
| | - Girish Dwivedi
- Department of Cardiology, Fiona Stanley Hospital, Perth, Australia; Harry Perkins Institute of Medical Research, School of Medicine, University of Western Australia, Perth, Australia.
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14
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Farhad A, Reza R, Azamossadat H, Ali G, Arash R, Mehrad A, Zahra K. Artificial intelligence in estimating fractional flow reserve: a systematic literature review of techniques. BMC Cardiovasc Disord 2023; 23:407. [PMID: 37596521 PMCID: PMC10439535 DOI: 10.1186/s12872-023-03447-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Accepted: 08/12/2023] [Indexed: 08/20/2023] Open
Abstract
BACKGROUND Fractional Flow Reserve (FFR) is the gold standard for the functional evaluation of coronary arteries, which is effective in selecting patients for revascularization, avoiding unnecessary procedures, and reducing treatment costs. However, its use is limited due to invasiveness, high cost, and complexity. Therefore, the non-invasive estimation of FFR using artificial intelligence (AI) methods is crucial. OBJECTIVE This study aimed to identify the AI techniques used for FFR estimation and to explore the features of the studies that applied AI techniques in FFR estimation. METHODS The present systematic review was conducted by searching five databases, PubMed, Scopus, Web of Science, IEEE, and Science Direct, based on the search strategy of each database. RESULTS Five hundred seventy-three articles were extracted, and by applying the inclusion and exclusion criteria, twenty-five were finally selected for review. The findings revealed that AI methods, including Machine Learning (ML) and Deep Learning (DL), have been used to estimate the FFR. CONCLUSION This study shows that AI methods can be used non-invasively to estimate FFR, which can help physicians diagnose and treat coronary artery occlusion and provide significant clinical performance for patients.
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Affiliation(s)
- Arefinia Farhad
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Rabiei Reza
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Hosseini Azamossadat
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Ghaemian Ali
- Cardiovascular Research Center, Mazandaran University of Medical Sciences, Sari, Iran
| | - Roshanpoor Arash
- Department of Computer Science, Sama Technical and Vocational Training College, Tehran Branch (Tehran), Islamic Azad University (IAU), Tehran, Iran
| | - Aria Mehrad
- Department of Information Technology and Computer Engineering and Ophthalmic Epidemiology Research Center, Azarbaijan Shahid Madani University, Tabriz, Iran
| | - Khorrami Zahra
- Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, Tabriz, Iran
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15
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Adolf R, Nano N, Chami A, von Schacky CE, Will A, Hendrich E, Martinoff SA, Hadamitzky M. Convolutional neural networks on risk stratification of patients with suspected coronary artery disease undergoing coronary computed tomography angiography. THE INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING 2023; 39:1209-1216. [PMID: 37010650 DOI: 10.1007/s10554-023-02824-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 02/26/2023] [Indexed: 05/28/2023]
Abstract
To assess the prognostic value of convolutional neural networks (CNN) on coronary computed tomography angiography (CCTA) in comparison to conventional computed tomography (CT) reporting and clinical risk scores. 5468 patients who underwent CCTA with suspected coronary artery disease (CAD) were included. Primary endpoint was defined as a composite of all-cause death, myocardial infarction, unstable angina or late revascularization (> 90 days after CCTA). Early revascularization was additionally included as a training endpoint for the CNN algorithm. Cardiovascular risk stratification was based on Morise score and the extent of CAD (eoCAD) as assessed on CCTA. Semiautomatic post-processing was performed for vessel delineation and annotation of calcified and non-calcified plaque areas. Using a two-step training of a DenseNet-121 CNN the entire network was trained with the training endpoint, followed by training the feature layer with the primary endpoint. During a median follow-up of 7.2 years, the primary endpoint occurred in 334 patients. CNN showed an AUC of 0.631 ± 0.015 for prediction of the combined primary endpoint, while combining it with conventional CT and clinical risk scores showed an improvement of AUC from 0.646 ± 0.014 (based on eoCAD only) to 0.680 ± 0.015 (p < 0.0001) and from 0.619 ± 0.0149 (based on Morise Score only) to 0.6812 ± 0.0145 (p < 0.0001), respectively. In a stepwise model including all prediction methods, it was found an AUC of 0.680 ± 0.0148. CNN analysis showed to improve conventional CCTA-derived and clinical risk stratification when evaluating CCTA of patients with suspected CAD.
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Affiliation(s)
- Rafael Adolf
- Department of Radiology and Nuclear Medicine, German Heart Center Munich, Technical University of Munich, Lazarettstrasse 36, 80636, Munich, Germany
| | - Nejva Nano
- Department of Radiology and Nuclear Medicine, German Heart Center Munich, Technical University of Munich, Lazarettstrasse 36, 80636, Munich, Germany
| | - Alessa Chami
- Department of Diagnostic and Interventional Radiology, Klinikum München Neuperlach, Munich, Germany
| | - Claudio E von Schacky
- Department of Diagnostic and Interventional Radiology, Klinikum Rechts der Isar of Munich Technical University, Munich, Germany
| | - Albrecht Will
- Department of Radiology and Nuclear Medicine, German Heart Center Munich, Technical University of Munich, Lazarettstrasse 36, 80636, Munich, Germany
| | - Eva Hendrich
- Department of Radiology and Nuclear Medicine, German Heart Center Munich, Technical University of Munich, Lazarettstrasse 36, 80636, Munich, Germany
| | - Stefan A Martinoff
- Department of Radiology and Nuclear Medicine, German Heart Center Munich, Technical University of Munich, Lazarettstrasse 36, 80636, Munich, Germany
| | - Martin Hadamitzky
- Department of Radiology and Nuclear Medicine, German Heart Center Munich, Technical University of Munich, Lazarettstrasse 36, 80636, Munich, Germany.
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Lin A, Pieszko K, Park C, Ignor K, Williams MC, Slomka P, Dey D. Artificial intelligence in cardiovascular imaging: enhancing image analysis and risk stratification. BJR Open 2023; 5:20220021. [PMID: 37396483 PMCID: PMC10311632 DOI: 10.1259/bjro.20220021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 03/14/2023] [Accepted: 04/03/2023] [Indexed: 07/04/2023] Open
Abstract
In this review, we summarize state-of-the-art artificial intelligence applications for non-invasive cardiovascular imaging modalities including CT, MRI, echocardiography, and nuclear myocardial perfusion imaging.
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Affiliation(s)
| | | | - Caroline Park
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Katarzyna Ignor
- Department of Interventional Cardiology, Collegium Medicum, University of Zielona Góra, Zielona Góra, Poland
| | - Michelle C Williams
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
| | - Piotr Slomka
- Division of Artificial Intelligence, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Damini Dey
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
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17
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Danilov A, Aronow WS. Artificial Intelligence in Cardiology: Applications and Obstacles. Curr Probl Cardiol 2023; 48:101750. [PMID: 37088174 DOI: 10.1016/j.cpcardiol.2023.101750] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 04/17/2023] [Indexed: 04/25/2023]
Abstract
Artificial intelligence (AI) technology is poised to alter the flow of daily life, and in particular, medicine, where it may eventually complement the physician's work in diagnosing and treating disease. Despite the recent frenzy and uptick in AI research over the past decade, the integration of AI into medical practice is in its early stages. Cardiology stands to benefit due to its many diagnostic modalities and diverse treatments. AI methods have been applied to various domains within cardiology: imaging, electrocardiography, wearable devices, risk prediction, and disease classification. While many AI-based approaches have been developed that perform equal to or better than the state-of-the-art, few prospective randomized studies have evaluated their use. Furthermore, obstacles at the intersection of medicine and AI remain unsolved, including model understanding, bias, model evaluation, relevance and reproducibility, and legal and ethical dilemmas. We summarize recent and current applications of AI in cardiology, followed by a discussion of the aforementioned complications.
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Affiliation(s)
| | - Wilbert S Aronow
- New York Medical College, School of Medicine, Valhalla, New York; Department of Cardiology, Westchester Medical Center, Valhalla, NY
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18
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Lorenzatti D, Piña P, Csecs I, Schenone AL, Gongora CA, Garcia MJ, Blaha MJ, Budoff MJ, Williams MC, Dey D, Berman DS, Virani SS, Slipczuk L. Does Coronary Plaque Morphology Matter Beyond Plaque Burden? Curr Atheroscler Rep 2023; 25:167-180. [PMID: 36808390 DOI: 10.1007/s11883-023-01088-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/04/2023] [Indexed: 02/23/2023]
Abstract
PURPOSE OF REVIEW Imaging of adverse coronary plaque features by coronary computed tomography angiography (CCTA) has advanced greatly and at a fast pace. We aim to describe the evolution, present and future in plaque analysis, and its value in comparison to plaque burden. RECENT FINDINGS Recently, it has been demonstrated that in addition to plaque burden, quantitative and qualitative assessment of coronary plaque by CCTA can improve the prediction of future major adverse cardiovascular events in diverse coronary artery disease scenarios. The detection of high-risk non-obstructive coronary plaque can lead to higher use of preventive medical therapies such as statins and aspirin, help identify culprit plaque, and differentiate between myocardial infarction types. Even more, over traditional plaque burden, plaque analysis including pericoronary inflammation can potentially be useful tools for tracking disease progression and response to medical therapy. The identification of the higher risk phenotypes with plaque burden, plaque characteristics, or ideally both can allow the allocation of targeted therapies and potentially monitor response. Further observational data are now required to investigate these key issues in diverse populations, followed by rigorous randomized controlled trials.
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Affiliation(s)
- Daniel Lorenzatti
- Cardiology Division, Montefiore Healthcare Network/Albert Einstein College of Medicine, Bronx, NY, USA
| | - Pamela Piña
- Cardiology Division, Montefiore Healthcare Network/Albert Einstein College of Medicine, Bronx, NY, USA
- Cardiology Division, CEDIMAT Cardiovascular Center, Santo Domingo, Dominican Republic
| | - Ibolya Csecs
- Cardiology Division, Montefiore Healthcare Network/Albert Einstein College of Medicine, Bronx, NY, USA
| | - Aldo L Schenone
- Cardiology Division, Montefiore Healthcare Network/Albert Einstein College of Medicine, Bronx, NY, USA
| | - Carlos A Gongora
- Cardiology Division, Montefiore Healthcare Network/Albert Einstein College of Medicine, Bronx, NY, USA
| | - Mario J Garcia
- Cardiology Division, Montefiore Healthcare Network/Albert Einstein College of Medicine, Bronx, NY, USA
| | - Michael J Blaha
- Johns Hopkins Ciccarone Center for the Prevention of Heart Disease, Baltimore, MD, USA
| | - Matthew J Budoff
- Department of Medicine, Lundquist Institute at Harbor UCLA Medical Center, Torrance, CA, USA
| | - Michelle C Williams
- BHF Centre of Cardiovascular Science, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, Queen's Medical Research Institute University of Edinburgh, Edinburgh, UK
| | - Damini Dey
- Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Daniel S Berman
- Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Salim S Virani
- Section of Cardiology, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
- Section of Cardiology, Department of Medicine, The Aga Khan University, Karachi, Pakistan
| | - Leandro Slipczuk
- Cardiology Division, Montefiore Healthcare Network/Albert Einstein College of Medicine, Bronx, NY, USA.
- Clinical Cardiology, Advanced Cardiac Imaging, CV Atherosclerosis and Lipid Disorder Center, Montefiore Health System, NewYork, USA.
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Bauer MJ, Nano N, Adolf R, Will A, Hendrich E, Martinoff SA, Hadamitzky M. Prognostic Value of Machine Learning-based Time-to-Event Analysis Using Coronary CT Angiography in Patients with Suspected Coronary Artery Disease. Radiol Cardiothorac Imaging 2023; 5:e220107. [PMID: 37124636 PMCID: PMC10141344 DOI: 10.1148/ryct.220107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 02/09/2023] [Accepted: 02/22/2023] [Indexed: 05/02/2023]
Abstract
Purpose To assess the long-term prognostic value of a machine learning (ML) approach in time-to-event analyses incorporating coronary CT angiography (CCTA)-derived and clinical parameters in patients with suspected coronary artery disease. Materials and Methods The retrospective analysis included patients with suspected coronary artery disease who underwent CCTA between October 2004 and December 2017. Major adverse cardiovascular events were defined as the composite of all-cause death, myocardial infarction, unstable angina, or late revascularization (>90 days after index scan). Clinical and CCTA-derived parameters were assessed as predictors of major adverse cardiovascular events and incorporated into two models: a Cox proportional hazards model with recursive feature elimination and an ML model based on random survival forests. Both models were trained and validated by employing repeated nested cross-validation. Harrell concordance index (C-index) was used to assess the predictive power. Results A total of 5457 patients (mean age, 61 years ± 11 [SD]; 3648 male patients) were evaluated. The predictive power of the ML model (C-index, 0.74; 95% CI: 0.71, 0.76) was significantly higher than the Cox model (C-index, 0.71; 95% CI: 0.68, 0.74; P = .02). The ML model also outperformed the segment stenosis score (C-index, 0.69; 95% CI: 0.66, 0.72; P < .001), which was the best performing CCTA-derived parameter, and patient age (C-index, 0.66; 95% CI: 0.63, 0.69; P < .001), the best performing clinical parameter. Conclusion An ML model for time-to-event analysis based on random survival forests had higher performance in predicting major adverse cardiovascular events compared with established clinical or CCTA-derived metrics and a conventional Cox model.Keywords: Machine Learning, CT Angiography, Cardiac, Arteries, Heart, Arteriosclerosis, Coronary Artery DiseaseSupplemental material is available for this article.© RSNA, 2023.
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20
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Han D, van Diemen P, Kuronuma K, Lin A, Motwani M, McElhinney P, Tomasino GF, Park C, Kwan A, Tzolos E, Klein E, Grodecki K, Shou B, Tamarappoo B, Cadet S, Danad I, Driessen RS, Berman DS, Slomka PJ, Dey D, Knaapen P. Sex differences in computed tomography angiography-derived coronary plaque burden in relation to invasive fractional flow reserve. J Cardiovasc Comput Tomogr 2023; 17:112-119. [PMID: 36670043 PMCID: PMC10148895 DOI: 10.1016/j.jcct.2022.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 12/12/2022] [Accepted: 12/13/2022] [Indexed: 01/20/2023]
Abstract
BACKGROUND Distinct sex-related differences exist in coronary artery plaque burden and distribution. We aimed to explore sex differences in quantitative plaque burden by coronary CT angiography (CCTA) in relation to ischemia by invasive fractional flow reserve (FFR). METHODS This post-hoc analysis of the PACIFIC trial included 581 vessels in 203 patients (mean age 58.1 ± 8.7 years, 63.5% male) who underwent CCTA and per-vessel invasive FFR. Quantitative assessment of total, calcified, non-calcified, and low-density non-calcified plaque burden were performed using semiautomated software. Significant ischemia was defined as invasive FFR ≤0.8. RESULTS The per-vessel frequency of ischemia was higher in men than women (33.5% vs. 7.5%, p < 0.001). Women had a smaller burden of all plaque subtypes (all p < 0.01). There was no sex difference on total, calcified, or non-calcified plaque burdens in vessels with ischemia; only low-density non-calcified plaque burden was significantly lower in women (beta: -0.183, p = 0.035). The burdens of all plaque subtypes were independently associated with ischemia in both men and women (For total plaque burden (5% increase): Men, OR: 1.15, 95%CI: 1.06-1.24, p = 0.001; Women, OR: 1.96, 95%CI: 1.11-3.46, p = 0.02). No significant interaction existed between sex and total plaque burden for predicting ischemia (interaction p = 0.108). The addition of quantitative plaque burdens to stenosis severity and adverse plaque characteristics improved the discrimination of ischemia in both men and women. CONCLUSIONS In symptomatic patients with suspected CAD, women have a lower CCTA-derived burden of all plaque subtypes compared to men. Quantitative plaque burden provides independent and incremental predictive value for ischemia, irrespective of sex.
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Affiliation(s)
- Donghee Han
- Department of Imaging and Medicine and the Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
| | - Pepijn van Diemen
- Department of Cardiology, VU University Medical Center, Amsterdam, the Netherlands
| | - Keiichiro Kuronuma
- Department of Imaging and Medicine and the Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Andrew Lin
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Manish Motwani
- Manchester Heart Centre, Manchester University NHS Foundation Trust, Manchester, United Kingdom
| | - Priscilla McElhinney
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | | | - Caroline Park
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Alan Kwan
- Department of Imaging and Medicine and the Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Evangelos Tzolos
- Department of Imaging and Medicine and the Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, United Kingdom
| | - Eyal Klein
- Department of Imaging and Medicine and the Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Kajetan Grodecki
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Benjamin Shou
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Balaji Tamarappoo
- Department of Imaging and Medicine and the Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Cardiovascular Institute, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Sebastien Cadet
- Department of Imaging and Medicine and the Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ibrahim Danad
- Department of Cardiology, VU University Medical Center, Amsterdam, the Netherlands
| | - Roel S Driessen
- Department of Cardiology, VU University Medical Center, Amsterdam, the Netherlands
| | - Daniel S Berman
- Department of Imaging and Medicine and the Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Piotr J Slomka
- Artificial Interlligence in Medicine Program, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Damini Dey
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Paul Knaapen
- Department of Cardiology, VU University Medical Center, Amsterdam, the Netherlands
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Hochhegger B, Pasini R, Roncally Carvalho A, Rodrigues R, Altmayer S, Kayat Bittencourt L, Marchiori E, Forghani R. Artificial Intelligence for Cardiothoracic Imaging: Overview of Current and Emerging Applications. Semin Roentgenol 2023; 58:184-195. [PMID: 37087139 DOI: 10.1053/j.ro.2023.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 02/02/2023] [Indexed: 03/07/2023]
Abstract
Artificial intelligence algorithms can learn by assimilating information from large datasets in order to decipher complex associations, identify previously undiscovered pathophysiological states, and construct prediction models. There has been tremendous interest and increased incorporation of artificial intelligence into various industries, including healthcare. As a result, there has been an exponential rise in the number of research articles and industry participants producing models intended for a variety of applications in medical imaging, which can be challenging to navigate for radiologists. In thoracic imaging, multiple applications are being evaluated for chest radiography and computed tomography and include applications for lung nodule evaluation and cancer imaging, quantifying diffuse lung disorders, and cardiac imaging, to name a few. This review aims to provide an overview of current clinical AI models, focusing on the most common clinical applications of AI in cardiothoracic imaging.
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Joshi M, Melo DP, Ouyang D, Slomka PJ, Williams MC, Dey D. Current and Future Applications of Artificial Intelligence in Cardiac CT. Curr Cardiol Rep 2023; 25:109-117. [PMID: 36708505 DOI: 10.1007/s11886-022-01837-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/10/2022] [Indexed: 01/29/2023]
Abstract
PURPOSE OF REVIEW In this review, we aim to summarize state-of-the-art artificial intelligence (AI) approaches applied to cardiovascular CT and their future implications. RECENT FINDINGS Recent studies have shown that deep learning networks can be applied for rapid automated segmentation of coronary plaque from coronary CT angiography, with AI-enabled measurement of total plaque volume predicting future heart attack. AI has also been applied to automate assessment of coronary artery calcium on cardiac and ungated chest CT and to automate the measurement of epicardial fat. Additionally, AI-based prediction models integrating clinical and imaging parameters have been shown to improve prediction of cardiac events compared to traditional risk scores. Artificial intelligence applications have been applied in all aspects of cardiovascular CT - in image acquisition, reconstruction and denoising, segmentation and quantitative analysis, diagnosis and decision assistance and to integrate prognostic risk from clinical data and images. Further incorporation of artificial intelligence in cardiovascular imaging holds important promise to enhance cardiovascular CT as a precision medicine tool.
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Affiliation(s)
- Mugdha Joshi
- Department of Medicine, Stanford Healthcare, Palo Alto, CA, USA
| | - Diana Patricia Melo
- Division of Cardiovascular Medicine, Stanford Healthcare, Palo Alto, CA, USA
| | - David Ouyang
- Cedars-Sinai Medical Center, Smidt Heart Institute, Los Angeles, CA, USA
| | - Piotr J Slomka
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Michelle C Williams
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Damini Dey
- Cedars-Sinai Medical Center, Biomedical Imaging Research Institute, 116 N Robertson Boulevard, Los Angeles, CA, 90048, USA.
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23
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Artificial Intelligence as a Diagnostic Tool in Non-Invasive Imaging in the Assessment of Coronary Artery Disease. Med Sci (Basel) 2023; 11:medsci11010020. [PMID: 36976528 PMCID: PMC10053913 DOI: 10.3390/medsci11010020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 02/20/2023] [Accepted: 02/22/2023] [Indexed: 03/02/2023] Open
Abstract
Coronary artery disease (CAD) remains a leading cause of mortality and morbidity worldwide, and it is associated with considerable economic burden. In an ageing, multimorbid population, it has become increasingly important to develop reliable, consistent, low-risk, non-invasive means of diagnosing CAD. The evolution of multiple cardiac modalities in this field has addressed this dilemma to a large extent, not only in providing information regarding anatomical disease, as is the case with coronary computed tomography angiography (CCTA), but also in contributing critical details about functional assessment, for instance, using stress cardiac magnetic resonance (S-CMR). The field of artificial intelligence (AI) is developing at an astounding pace, especially in healthcare. In healthcare, key milestones have been achieved using AI and machine learning (ML) in various clinical settings, from smartwatches detecting arrhythmias to retinal image analysis and skin cancer prediction. In recent times, we have seen an emerging interest in developing AI-based technology in the field of cardiovascular imaging, as it is felt that ML methods have potential to overcome some limitations of current risk models by applying computer algorithms to large databases with multidimensional variables, thus enabling the inclusion of complex relationships to predict outcomes. In this paper, we review the current literature on the various applications of AI in the assessment of CAD, with a focus on multimodality imaging, followed by a discussion on future perspectives and critical challenges that this field is likely to encounter as it continues to evolve in cardiology.
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Incremental diagnostic value of radiomics signature of pericoronary adipose tissue for detecting functional myocardial ischemia: a multicenter study. Eur Radiol 2023; 33:3007-3019. [PMID: 36729175 DOI: 10.1007/s00330-022-09377-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 12/07/2022] [Accepted: 12/12/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVES To determine the incremental diagnostic value of radiomics signature of pericoronary adipose tissue (PCAT) in addition to the coronary artery stenosis and plaque characters for detecting hemodynamic significant coronary artery disease (CAD) based on coronary computed tomography angiography (CCTA). METHODS In a multicenter trial of 262 patients, CCTA and invasive coronary angiography were performed, with fractional flow reserve (FFR) in 306 vessels. A total of 13 conventional quantitative characteristics including plaque characteristics (N = 10) and epicardial adipose tissue characteristics (N = 3) were obtained. A total of 106 radiomics features depicting the phenotype of the PCAT surrounding the lesion were calculated. All data were randomly split into a training dataset (75%) and a testing dataset (25%). Then three models (including the conventional model, the PCAT radiomics model, and the combined model) were established in the training dataset using multivariate logistic regression algorithm based on the conventional quantitative features and the PCAT radiomics features after dimension reduction. RESULTS A total of 124/306 vessels showed functional ischemia (FFR ≤ 0.80). The radiomics model performed better in discriminating ischemia from non-ischemia than the conventional model in both training (area under the receiver operating characteristic (ROC) curve (AUC): 0.770 vs 0.732, p < 0.05) and testing datasets (AUC: 0.740 vs 0.696, p < 0.05). The combined model showed significantly better discrimination than the conventional model in both training (AUC: 0.810 vs 0.732, p < 0.05) and testing datasets (AUC: 0.809 vs 0.696, p < 0.05). CONCLUSIONS The PCAT radiomics model showed good performance in predicting myocardial ischemia. Addition of PCAT radiomics to lesion quantitative characteristics improves the predictive power of functionally relevant CAD. KEY POINTS • Based on the plaque characteristics and EAT characteristics, the conventional model showed poor performance in predicting myocardial ischemia. • The PCAT radiomics model showed good prospect in predicting myocardial ischemia. • When combining the radiomics signature with the conventional quantitative features (including plaque features and EAT features), it showed significantly better performance in predicting myocardial ischemia.
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25
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Motwani M, Williams MC, Nieman K, Choi AD. Great debates in cardiac computed tomography: OPINION: "Artificial intelligence is key to the future of CCTA - The great hope". J Cardiovasc Comput Tomogr 2023; 17:18-21. [PMID: 35945132 DOI: 10.1016/j.jcct.2022.07.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 07/05/2022] [Accepted: 07/16/2022] [Indexed: 10/17/2022]
Affiliation(s)
- Manish Motwani
- Manchester Heart Institute, Manchester University NHS Foundation Trust, UK; Institute of Cardiovascular Science, University of Manchester, UK
| | - Michelle C Williams
- Center for Cardiovascular Sciences, The University of Edinburgh, Edinburgh, UK
| | - Koen Nieman
- Departments of Cardiovascular Medicine and Radiology, Stanford University, Stanford, CA, USA
| | - Andrew D Choi
- Division of Cardiology and Department of Radiology, The George Washington University School of Medicine, Washington, DC, USA.
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26
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Weberling LD, Lossnitzer D, Frey N, André F. Coronary Computed Tomography vs. Cardiac Magnetic Resonance Imaging in the Evaluation of Coronary Artery Disease. Diagnostics (Basel) 2022; 13:diagnostics13010125. [PMID: 36611417 PMCID: PMC9818886 DOI: 10.3390/diagnostics13010125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 12/23/2022] [Accepted: 12/28/2022] [Indexed: 01/04/2023] Open
Abstract
Coronary artery disease (CAD) represents a widespread burden to both individual and public health, steadily rising across the globe. The current guidelines recommend non-invasive anatomical or functional testing prior to invasive procedures. Both coronary computed tomography angiography (cCTA) and stress cardiac magnetic resonance imaging (CMR) are appropriate imaging modalities, which are increasingly used in these patients. Both exhibit excellent safety profiles and high diagnostic accuracy. In the last decade, cCTA image quality has improved, radiation exposure has decreased and functional information such as CT-derived fractional flow reserve or perfusion can complement anatomic evaluation. CMR has become more robust and faster, and advances have been made in functional assessment and tissue characterization allowing for earlier and better risk stratification. This review compares both imaging modalities regarding their strengths and weaknesses in the assessment of CAD and aims to give physicians rationales to select the most appropriate modality for individual patients.
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Affiliation(s)
- Lukas D. Weberling
- Department of Cardiology, Angiology and Pneumology, University Hospital Heidelberg, Im Neuenheimer Feld 410, 69120 Heidelberg, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Heidelberg/Mannheim, 69120 Heidelberg, Germany
- Correspondence: ; Tel.: +49-6221-8676
| | - Dirk Lossnitzer
- Department of Cardiology, Angiology and Pneumology, University Hospital Heidelberg, Im Neuenheimer Feld 410, 69120 Heidelberg, Germany
| | - Norbert Frey
- Department of Cardiology, Angiology and Pneumology, University Hospital Heidelberg, Im Neuenheimer Feld 410, 69120 Heidelberg, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Heidelberg/Mannheim, 69120 Heidelberg, Germany
| | - Florian André
- Department of Cardiology, Angiology and Pneumology, University Hospital Heidelberg, Im Neuenheimer Feld 410, 69120 Heidelberg, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Heidelberg/Mannheim, 69120 Heidelberg, Germany
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27
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Fahmy D, Alksas A, Elnakib A, Mahmoud A, Kandil H, Khalil A, Ghazal M, van Bogaert E, Contractor S, El-Baz A. The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma. Cancers (Basel) 2022; 14:cancers14246123. [PMID: 36551606 PMCID: PMC9777232 DOI: 10.3390/cancers14246123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/08/2022] [Accepted: 12/09/2022] [Indexed: 12/15/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common primary hepatic neoplasm. Thanks to recent advances in computed tomography (CT) and magnetic resonance imaging (MRI), there is potential to improve detection, segmentation, discrimination from HCC mimics, and monitoring of therapeutic response. Radiomics, artificial intelligence (AI), and derived tools have already been applied in other areas of diagnostic imaging with promising results. In this review, we briefly discuss the current clinical applications of radiomics and AI in the detection, segmentation, and management of HCC. Moreover, we investigate their potential to reach a more accurate diagnosis of HCC and to guide proper treatment planning.
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Affiliation(s)
- Dalia Fahmy
- Diagnostic Radiology Department, Mansoura University Hospital, Mansoura 35516, Egypt
| | - Ahmed Alksas
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Ahmed Elnakib
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Ali Mahmoud
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Heba Kandil
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
- Faculty of Computer Sciences and Information, Mansoura University, Mansoura 35516, Egypt
| | - Ashraf Khalil
- College of Technological Innovation, Zayed University, Abu Dhabi 4783, United Arab Emirates
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates
| | - Eric van Bogaert
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Sohail Contractor
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Ayman El-Baz
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
- Correspondence:
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28
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Alskaf E, Dutta U, Scannell CM, Chiribiri A. Deep learning applications in coronary anatomy imaging: a systematic review and meta-analysis. JOURNAL OF MEDICAL ARTIFICIAL INTELLIGENCE 2022; 5:11. [PMID: 36861064 PMCID: PMC7614252 DOI: 10.21037/jmai-22-36] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Background The application of deep learning on medical imaging is growing in prevalence in the recent literature. One of the most studied areas is coronary artery disease (CAD). Imaging of coronary artery anatomy is fundamental, which has led to a high number of publications describing a variety of techniques. The aim of this systematic review is to review the evidence behind the accuracy of deep learning applications in coronary anatomy imaging. Methods The search for the relevant studies, which applied deep learning on coronary anatomy imaging, was performed in a systematic approach on MEDLINE and EMBASE databases, followed by reviewing of abstracts and full texts. The data from the final studies was retrieved using data extraction forms. A meta-analysis was performed on a subgroup of studies, which looked at fractional flow reserve (FFR) prediction. Heterogeneity was tested using tau2, I2 and Q tests. Finally, a risk of bias was performed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS) approach. Results A total of 81 studies met the inclusion criteria. The most common imaging modality was coronary computed tomography angiography (CCTA) (58%) and the most common deep learning method was convolutional neural network (CNN) (52%). The majority of studies demonstrated good performance metrics. The most common outputs were focused on coronary artery segmentation, clinical outcome prediction, coronary calcium quantification and FFR prediction, and most studies reported area under the curve (AUC) of ≥80%. The pooled diagnostic odds ratio (DOR) derived from 8 studies looking at FFR prediction using CCTA was 12.5 using the Mantel-Haenszel (MH) method. There was no significant heterogeneity amongst studies according to Q test (P=0.2496). Conclusions Deep learning has been used in many applications on coronary anatomy imaging, most of which are yet to be externally validated and prepared for clinical use. The performance of deep learning, especially CNN models, proved to be powerful and some applications have already translated into medical practice, such as computed tomography (CT)-FFR. These applications have the potential to translate technology into better care of CAD patients.
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Affiliation(s)
- Ebraham Alskaf
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Utkarsh Dutta
- GKT School of Medical Education, King’s College London, London, UK
| | - Cian M. Scannell
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK,Medical Image Analysis Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Amedeo Chiribiri
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
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29
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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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30
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Karatzia L, Aung N, Aksentijevic D. Artificial intelligence in cardiology: Hope for the future and power for the present. Front Cardiovasc Med 2022; 9:945726. [PMID: 36312266 PMCID: PMC9608631 DOI: 10.3389/fcvm.2022.945726] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 09/09/2022] [Indexed: 11/17/2022] Open
Abstract
Cardiovascular disease (CVD) is the principal cause of mortality and morbidity globally. With the pressures for improved care and translation of the latest medical advances and knowledge to an actionable plan, clinical decision-making for cardiologists is challenging. Artificial Intelligence (AI) is a field in computer science that studies the design of intelligent agents which take the best feasible action in a situation. It incorporates the use of computational algorithms which simulate and perform tasks that traditionally require human intelligence such as problem solving and learning. Whilst medicine is arguably the last to apply AI in its everyday routine, cardiology is at the forefront of AI revolution in the medical field. The development of AI methods for accurate prediction of CVD outcomes, non-invasive diagnosis of coronary artery disease (CAD), detection of malignant arrythmias through wearables, and diagnosis, treatment strategies and prediction of outcomes for heart failure (HF) patients, demonstrates the potential of AI in future cardiology. With the advancements of AI, Internet of Things (IoT) and the promotion of precision medicine, the future of cardiology will be heavily based on these innovative digital technologies. Despite this, ethical dilemmas regarding the implementation of AI technologies in real-world are still unaddressed.
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Affiliation(s)
- Loucia Karatzia
- Centre for Biochemical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Nay Aung
- Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom,National Institute for Health and Care Research (NIHR) Barts Biomedical Research Centre, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Dunja Aksentijevic
- Centre for Biochemical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom,*Correspondence: Dunja Aksentijevic,
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31
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Muscogiuri G, Volpato V, Cau R, Chiesa M, Saba L, Guglielmo M, Senatieri A, Chierchia G, Pontone G, Dell’Aversana S, Schoepf UJ, Andrews MG, Basile P, Guaricci AI, Marra P, Muraru D, Badano LP, Sironi S. Application of AI in cardiovascular multimodality imaging. Heliyon 2022; 8:e10872. [PMID: 36267381 PMCID: PMC9576885 DOI: 10.1016/j.heliyon.2022.e10872] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 08/23/2022] [Accepted: 09/27/2022] [Indexed: 12/16/2022] Open
Abstract
Technical advances in artificial intelligence (AI) in cardiac imaging are rapidly improving the reproducibility of this approach and the possibility to reduce time necessary to generate a report. In cardiac computed tomography angiography (CCTA) the main application of AI in clinical practice is focused on detection of stenosis, characterization of coronary plaques, and detection of myocardial ischemia. In cardiac magnetic resonance (CMR) the application of AI is focused on post-processing and particularly on the segmentation of cardiac chambers during late gadolinium enhancement. In echocardiography, the application of AI is focused on segmentation of cardiac chambers and is helpful for valvular function and wall motion abnormalities. The common thread represented by all of these techniques aims to shorten the time of interpretation without loss of information compared to the standard approach. In this review we provide an overview of AI applications in multimodality cardiac imaging.
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Affiliation(s)
- Giuseppe Muscogiuri
- Department of Radiology, Istituto Auxologico Italiano IRCCS, San Luca Hospital, Italy,School of Medicine, University of Milano-Bicocca, Milan, Italy,Corresponding author.
| | - Valentina Volpato
- Department of Cardiac, Neurological and Metabolic Sciences, San Luca Hospital, Istituto Auxologico Italiano IRCCS, Milan, Italy,IRCCS Ospedale Galeazzi - Sant'Ambrogio, University Cardiology Department, Milan, Italy
| | - Riccardo Cau
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari, Polo di Monserrato, Cagliari, Italy
| | | | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari, Polo di Monserrato, Cagliari, Italy
| | - Marco Guglielmo
- Department of Cardiology, Division of Heart and Lungs, Utrecht University, Utrecht University Medical Center, Utrecht, the Netherlands
| | | | | | | | - Serena Dell’Aversana
- Department of Radiology, Ospedale S. Maria Delle Grazie - ASL Napoli 2 Nord, Pozzuoli, Italy
| | - U. Joseph Schoepf
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Dr., Charleston, SC, USA
| | - Mason G. Andrews
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Dr., Charleston, SC, USA
| | - Paolo Basile
- University Cardiology Unit, Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy
| | - Andrea Igoren Guaricci
- University Cardiology Unit, Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy
| | - Paolo Marra
- Department of Radiology, ASST Papa Giovanni XXIII, 24127 Bergamo, Italy
| | - Denisa Muraru
- School of Medicine, University of Milano-Bicocca, Milan, Italy,Department of Cardiac, Neurological and Metabolic Sciences, San Luca Hospital, Istituto Auxologico Italiano IRCCS, Milan, Italy
| | - Luigi P. Badano
- School of Medicine, University of Milano-Bicocca, Milan, Italy,Department of Cardiac, Neurological and Metabolic Sciences, San Luca Hospital, Istituto Auxologico Italiano IRCCS, Milan, Italy
| | - Sandro Sironi
- School of Medicine, University of Milano-Bicocca, Milan, Italy,Department of Radiology, ASST Papa Giovanni XXIII, 24127 Bergamo, Italy
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32
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Lin A, van Diemen PA, Motwani M, McElhinney P, Otaki Y, Han D, Kwan A, Tzolos E, Klein E, Kuronuma K, Grodecki K, Shou B, Rios R, Manral N, Cadet S, Danad I, Driessen RS, Berman DS, Nørgaard BL, Slomka PJ, Knaapen P, Dey D. Machine Learning From Quantitative Coronary Computed Tomography Angiography Predicts Fractional Flow Reserve-Defined Ischemia and Impaired Myocardial Blood Flow. Circ Cardiovasc Imaging 2022; 15:e014369. [PMID: 36252116 PMCID: PMC10085569 DOI: 10.1161/circimaging.122.014369] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 09/13/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND A pathophysiological interplay exists between plaque morphology and coronary physiology. Machine learning (ML) is increasingly being applied to coronary computed tomography angiography (CCTA) for cardiovascular risk stratification. We sought to assess the performance of a ML score integrating CCTA-based quantitative plaque features for predicting vessel-specific ischemia by invasive fractional flow reserve (FFR) and impaired myocardial blood flow (MBF) by positron emission tomography (PET). METHODS This post-hoc analysis of the PACIFIC trial (Prospective Comparison of Cardiac Positron Emission Tomography/Computed Tomography [CT]' Single Photon Emission Computed Tomography/CT Perfusion Imaging and CT Coronary Angiography with Invasive Coronary Angiography) included 208 patients with suspected coronary artery disease who prospectively underwent CCTA' [15O]H2O PET, and invasive FFR. Plaque quantification from CCTA was performed using semiautomated software. An ML algorithm trained on the prospective NXT trial (484 vessels) was used to develop a ML score for the prediction of ischemia (FFR≤0.80), which was then evaluated in 581 vessels from the PACIFIC trial. Thereafter, the ML score was applied for predicting impaired hyperemic MBF (≤2.30 mL/min per g) from corresponding PET scans. The performance of the ML score was compared with CCTA reads and noninvasive FFR derived from CCTA (FFRCT). RESULTS One hundred thirty-nine (23.9%) vessels had FFR-defined ischemia, and 195 (33.6%) vessels had impaired hyperemic MBF. For the prediction of FFR-defined ischemia, the ML score yielded an area under the receiver-operating characteristic curve of 0.92, which was significantly higher than that of visual stenosis grade (0.84; P<0.001) and comparable with that of FFRCT (0.93; P=0.34). Quantitative percent diameter stenosis and low-density noncalcified plaque volume had the greatest ML feature importance for predicting FFR-defined ischemia. When applied for impaired MBF prediction, the ML score exhibited an area under the receiver-operating characteristic curve of 0.80; significantly higher than visual stenosis grade (area under the receiver-operating characteristic curve 0.74; P=0.02) and comparable with FFRCT (area under the receiver-operating characteristic curve 0.77; P=0.16). CONCLUSIONS An externally validated ML score integrating CCTA-based quantitative plaque features accurately predicts FFR-defined ischemia and impaired MBF by PET, performing superiorly to standard CCTA stenosis evaluation and comparably to FFRCT.
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Affiliation(s)
- Andrew Lin
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Pepijn A. van Diemen
- Department of Cardiology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Manish Motwani
- Manchester Heart Centre, Manchester University NHS Foundation Trust, Manchester, United Kingdom
| | - Priscilla McElhinney
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Yuka Otaki
- Department of Imaging and Medicine and the Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Donghee Han
- Department of Imaging and Medicine and the Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Alan Kwan
- Department of Imaging and Medicine and the Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Evangelos Tzolos
- Department of Imaging and Medicine and the Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, United Kingdom
| | - Eyal Klein
- Department of Imaging and Medicine and the Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Keiichiro Kuronuma
- Department of Imaging and Medicine and the Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Kajetan Grodecki
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Benjamin Shou
- Department of Imaging and Medicine and the Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Richard Rios
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Nipun Manral
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Sebastien Cadet
- Department of Imaging and Medicine and the Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ibrahim Danad
- Department of Cardiology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Roel S. Driessen
- Department of Cardiology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Daniel S. Berman
- Department of Imaging and Medicine and the Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Bjarne L. Nørgaard
- Department of Cardiology, Aarhus University Hospital Skejby, Aarhus, Denmark
| | - Piotr J. Slomka
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Paul Knaapen
- Department of Cardiology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Damini Dey
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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Chen X, Lei Y, Su J, Yang H, Ni W, Yu J, Gu Y, Mao Y. A Review of Artificial Intelligence in Cerebrovascular Disease Imaging: Applications and Challenges. Curr Neuropharmacol 2022; 20:1359-1382. [PMID: 34749621 PMCID: PMC9881077 DOI: 10.2174/1570159x19666211108141446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 09/07/2021] [Accepted: 10/10/2021] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND A variety of emerging medical imaging technologies based on artificial intelligence have been widely applied in many diseases, but they are still limitedly used in the cerebrovascular field even though the diseases can lead to catastrophic consequences. OBJECTIVE This work aims to discuss the current challenges and future directions of artificial intelligence technology in cerebrovascular diseases through reviewing the existing literature related to applications in terms of computer-aided detection, prediction and treatment of cerebrovascular diseases. METHODS Based on artificial intelligence applications in four representative cerebrovascular diseases including intracranial aneurysm, arteriovenous malformation, arteriosclerosis and moyamoya disease, this paper systematically reviews studies published between 2006 and 2021 in five databases: National Center for Biotechnology Information, Elsevier Science Direct, IEEE Xplore Digital Library, Web of Science and Springer Link. And three refinement steps were further conducted after identifying relevant literature from these databases. RESULTS For the popular research topic, most of the included publications involved computer-aided detection and prediction of aneurysms, while studies about arteriovenous malformation, arteriosclerosis and moyamoya disease showed an upward trend in recent years. Both conventional machine learning and deep learning algorithms were utilized in these publications, but machine learning techniques accounted for a larger proportion. CONCLUSION Algorithms related to artificial intelligence, especially deep learning, are promising tools for medical imaging analysis and will enhance the performance of computer-aided detection, prediction and treatment of cerebrovascular diseases.
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Affiliation(s)
- Xi Chen
- School of Information Science and Technology, Fudan University, Shanghai, China; ,These authors contributed equally to this work
| | - Yu Lei
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China,These authors contributed equally to this work
| | - Jiabin Su
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Heng Yang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Wei Ni
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Jinhua Yu
- School of Information Science and Technology, Fudan University, Shanghai, China; ,Address correspondence to these authors at the School of Information Science and Technology, Fudan University, Shanghai 200433, China; Tel: +86 021 65643202; Fax: +86 021 65643202; E-mail: Department of Neurosurgery, Huashan Hospital of Fudan University, Shanghai 200040, China; Tel: +86 021 52889999; Fax: +86 021 62489191; E-mail:
| | - Yuxiang Gu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China,Address correspondence to these authors at the School of Information Science and Technology, Fudan University, Shanghai 200433, China; Tel: +86 021 65643202; Fax: +86 021 65643202; E-mail: Department of Neurosurgery, Huashan Hospital of Fudan University, Shanghai 200040, China; Tel: +86 021 52889999; Fax: +86 021 62489191; E-mail:
| | - Ying Mao
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
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Koulaouzidis G, Jadczyk T, Iakovidis DK, Koulaouzidis A, Bisnaire M, Charisopoulou D. Artificial Intelligence in Cardiology-A Narrative Review of Current Status. J Clin Med 2022; 11:jcm11133910. [PMID: 35807195 PMCID: PMC9267740 DOI: 10.3390/jcm11133910] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 06/17/2022] [Accepted: 06/20/2022] [Indexed: 02/01/2023] Open
Abstract
Artificial intelligence (AI) is an integral part of clinical decision support systems (CDSS), offering methods to approximate human reasoning and computationally infer decisions. Such methods are generally based on medical knowledge, either directly encoded with rules or automatically extracted from medical data using machine learning (ML). ML techniques, such as Artificial Neural Networks (ANNs) and support vector machines (SVMs), are based on mathematical models with parameters that can be optimally tuned using appropriate algorithms. The ever-increasing computational capacity of today’s computer systems enables more complex ML systems with millions of parameters, bringing AI closer to human intelligence. With this objective, the term deep learning (DL) has been introduced to characterize ML based on deep ANN (DNN) architectures with multiple layers of artificial neurons. Despite all of these promises, the impact of AI in current clinical practice is still limited. However, this could change shortly, as the significantly increased papers in AI, machine learning and deep learning in cardiology show. We highlight the significant achievements of recent years in nearly all areas of cardiology and underscore the mounting evidence suggesting how AI will take a central stage in the field.
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Affiliation(s)
- George Koulaouzidis
- Department of Biochemical Sciences, Pomeranian Medical University (PMU), 70-204 Szczecin, Poland;
| | - Tomasz Jadczyk
- Division of Cardiology and Structural Heart Diseases, Medical University of Silesia, 40-551 Katowice, Poland;
- International Clinical Research Center, St. Anne’s University Hospital Brno, 656 91 Brno, Czech Republic
| | - Dimitris K. Iakovidis
- Department of Computer Science and Biomedical Informatics, University of Thessaly, 40500 Lamia, Greece;
| | - Anastasios Koulaouzidis
- Department of Social Medicine & Public Health, Pomeranian Medical University (PMU), 70-204 Szczecin, Poland
- Department of Medicine, OUH Svendborg Sygehus, 5700 Svendborg, Denmark
- Surgical Research Unit, Odense University Hospital, 5000 Odense, Denmark
- Department of Clinical Research, University of Southern Denmark (SDU), 5000 Odense, Denmark
- Correspondence:
| | - Marc Bisnaire
- Cardiology Research and Scientific Advancements, UVA Research, Toronto, ON L3R 3Z3, Canada;
| | - Dafni Charisopoulou
- Academic Centre for Congenital Heart Disease, 6500 HB Nijmegen, The Netherlands;
- Amalia Children’s Hospital, Radboud University Medical Centre, 6525 GA Nijmegen, The Netherlands
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35
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Dou G, Shan D, Wang K, Wang X, Liu Z, Zhang W, Li D, He B, Jing J, Wang S, Chen Y, Yang J. Integrating Coronary Plaque Information from CCTA by ML Predicts MACE in Patients with Suspected CAD. J Pers Med 2022; 12:jpm12040596. [PMID: 35455712 PMCID: PMC9025955 DOI: 10.3390/jpm12040596] [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: 03/03/2022] [Revised: 03/25/2022] [Accepted: 03/31/2022] [Indexed: 11/29/2022] Open
Abstract
Conventional prognostic risk analysis in patients undergoing noninvasive imaging is based upon a limited selection of clinical and imaging findings, whereas machine learning (ML) algorithms include a greater number and complexity of variables. Therefore, this paper aimed to explore the predictive value of integrating coronary plaque information from coronary computed tomographic angiography (CCTA) with ML to predict major adverse cardiovascular events (MACEs) in patients with suspected coronary artery disease (CAD). Patients who underwent CCTA due to suspected coronary artery disease with a 30-month follow-up for MACEs were included. We collected demographic characteristics, cardiovascular risk factors, and information on coronary plaques by analyzing CCTA information (plaque length, plaque composition and coronary artery stenosis of 18 coronary artery segments, coronary dominance, myocardial bridge (MB), and patients with vulnerable plaque) and follow-up information (cardiac death, nonfatal myocardial infarction and unstable angina requiring hospitalization). An ML algorithm was used for survival analysis (CoxBoost). This analysis showed that chest symptoms, the stenosis severity of the proximal anterior descending branch, and the stenosis severity of the middle right coronary artery were among the top three variables in the ML model. After the 22nd month of follow-up, in the testing dataset, ML showed the largest C-index and AUC compared with Cox regression, SIS, SIS score + clinical factors, and clinical factors. The DCA of all the models showed that the net benefit of the ML model was the highest when the treatment threshold probability was between 1% and 9%. Integrating coronary plaque information from CCTA based on ML technology provides a feasible and superior method to assess prognosis in patients with suspected coronary artery disease over an approximately three-year period.
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Affiliation(s)
- Guanhua Dou
- Department of Cardiology, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing 100853, China;
| | - Dongkai Shan
- Department of Cardiology, Sixth Medical Center, Chinese PLA General Hospital, Beijing 100048, China; (D.S.); (D.L.); (Y.C.)
| | - Kai Wang
- Department of Cardiology, Yongchuan Hospital of Chongqing Medical University, Chongqing 402160, China;
| | - Xi Wang
- Department of Cardiology, First Medical Center, Chinese PLA General Hospital, Beijing 100853, China; (X.W.); (Z.L.); (W.Z.); (B.H.); (J.J.)
| | - Zinuan Liu
- Department of Cardiology, First Medical Center, Chinese PLA General Hospital, Beijing 100853, China; (X.W.); (Z.L.); (W.Z.); (B.H.); (J.J.)
| | - Wei Zhang
- Department of Cardiology, First Medical Center, Chinese PLA General Hospital, Beijing 100853, China; (X.W.); (Z.L.); (W.Z.); (B.H.); (J.J.)
| | - Dandan Li
- Department of Cardiology, Sixth Medical Center, Chinese PLA General Hospital, Beijing 100048, China; (D.S.); (D.L.); (Y.C.)
| | - Bai He
- Department of Cardiology, First Medical Center, Chinese PLA General Hospital, Beijing 100853, China; (X.W.); (Z.L.); (W.Z.); (B.H.); (J.J.)
| | - Jing Jing
- Department of Cardiology, First Medical Center, Chinese PLA General Hospital, Beijing 100853, China; (X.W.); (Z.L.); (W.Z.); (B.H.); (J.J.)
| | - Sicong Wang
- General Electric Healthcare China, Beijing 100176, China;
| | - Yundai Chen
- Department of Cardiology, Sixth Medical Center, Chinese PLA General Hospital, Beijing 100048, China; (D.S.); (D.L.); (Y.C.)
| | - Junjie Yang
- Department of Cardiology, Sixth Medical Center, Chinese PLA General Hospital, Beijing 100048, China; (D.S.); (D.L.); (Y.C.)
- Correspondence:
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Slomka P. Future of nuclear cardiology is bright: Promise of cardiac PET/CT and artificial intelligence. J Nucl Cardiol 2022; 29:389-391. [PMID: 35244874 DOI: 10.1007/s12350-022-02942-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 02/17/2022] [Indexed: 11/26/2022]
Affiliation(s)
- Piotr Slomka
- Division of Artificial Intelligence, Department of Medicine, Cedars-Sinai, Los Angeles, USA.
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37
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Bray JJH, Hanif MA, Alradhawi M, Ibbetson J, Dosanjh SS, Smith SL, Ahmad M, Pimenta D. Machine learning applications in cardiac computed tomography: a composite systematic review. EUROPEAN HEART JOURNAL OPEN 2022; 2:oeac018. [PMID: 35919128 PMCID: PMC9242067 DOI: 10.1093/ehjopen/oeac018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 03/10/2022] [Indexed: 12/02/2022]
Abstract
Artificial intelligence and machine learning (ML) models are rapidly being applied to the analysis of cardiac computed tomography (CT). We sought to provide an overview of the contemporary advances brought about by the combination of ML and cardiac CT. Six searches were performed in Medline, Embase, and the Cochrane Library up to November 2021 for (i) CT-fractional flow reserve (CT-FFR), (ii) atrial fibrillation (AF), (iii) aortic stenosis, (iv) plaque characterization, (v) fat quantification, and (vi) coronary artery calcium score. We included 57 studies pertaining to the aforementioned topics. Non-invasive CT-FFR can accurately be estimated using ML algorithms and has the potential to reduce the requirement for invasive angiography. Coronary artery calcification and non-calcified coronary lesions can now be automatically and accurately calculated. Epicardial adipose tissue can also be automatically, accurately, and rapidly quantified. Effective ML algorithms have been developed to streamline and optimize the safety of aortic annular measurements to facilitate pre-transcatheter aortic valve replacement valve selection. Within electrophysiology, the left atrium (LA) can be segmented and resultant LA volumes have contributed to accurate predictions of post-ablation recurrence of AF. In this review, we discuss the latest studies and evolving techniques of ML and cardiac CT.
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Affiliation(s)
- Jonathan James Hyett Bray
- Institute of Life Sciences 2, Swansea University Medical, School , Swansea, UK
- Cardiology Department, Royal Free Hospital, Royal Free London NHS Foundation Trust , London, UK
| | - Moghees Ahmad Hanif
- Cardiology Department, Royal Free Hospital, Royal Free London NHS Foundation Trust , London, UK
| | | | - Jacob Ibbetson
- Cardiology Department, Royal Free Hospital, Royal Free London NHS Foundation Trust , London, UK
| | | | - Sabrina Lucy Smith
- Barts and the London School of Medicine and Dentistry , London E1 2AD, UK
| | - Mahmood Ahmad
- Cardiology Department, Royal Free Hospital, Royal Free London NHS Foundation Trust , London, UK
- University College London Medical School , London WC1E 6DE, UK
| | - Dominic Pimenta
- Richmond Research Institute, St George’s Hospital, University of London , Cranmer Terrace, Tooting, London SW17 0RE, UK
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38
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Zhang J, Han R, Shao G, Lv B, Sun K. Artificial Intelligence in Cardiovascular Atherosclerosis Imaging. J Pers Med 2022; 12:jpm12030420. [PMID: 35330420 PMCID: PMC8952318 DOI: 10.3390/jpm12030420] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 02/15/2022] [Accepted: 03/04/2022] [Indexed: 12/22/2022] Open
Abstract
At present, artificial intelligence (AI) has already been applied in cardiovascular imaging (e.g., image segmentation, automated measurements, and eventually, automated diagnosis) and it has been propelled to the forefront of cardiovascular medical imaging research. In this review, we presented the current status of artificial intelligence applied to image analysis of coronary atherosclerotic plaques, covering multiple areas from plaque component analysis (e.g., identification of plaque properties, identification of vulnerable plaque, detection of myocardial function, and risk prediction) to risk prediction. Additionally, we discuss the current evidence, strengths, limitations, and future directions for AI in cardiac imaging of atherosclerotic plaques, as well as lessons that can be learned from other areas. The continuous development of computer science and technology may further promote the development of this field.
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Affiliation(s)
- Jia Zhang
- Hohhot Health Committee, Hohhot 010000, China;
| | - Ruijuan Han
- The People’s Hospital of Longgang District, Shenzhen 518172, China;
| | - Guo Shao
- The Third People’s Hospital of Longgang District, Shenzhen 518100, China;
| | - Bin Lv
- Fuwai Hospital, National Center for Cardiovascular Diseases, Beijing 100037, China;
| | - Kai Sun
- The Third People’s Hospital of Longgang District, Shenzhen 518100, China;
- Correspondence:
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DelSole EM, Keck WL, Patel AA. The State of Machine Learning in Spine Surgery: A Systematic Review. Clin Spine Surg 2022; 35:80-89. [PMID: 34121074 DOI: 10.1097/bsd.0000000000001208] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 04/14/2021] [Indexed: 11/27/2022]
Abstract
STUDY DESIGN This was a systematic review of existing literature. OBJECTIVE The objective of this study was to evaluate the current state-of-the-art trends and utilization of machine learning in the field of spine surgery. SUMMARY OF BACKGROUND DATA The past decade has seen a rise in the clinical use of machine learning in many fields including diagnostic radiology and oncology. While studies have been performed that specifically pertain to spinal surgery, there have been relatively few aggregate reviews of the existing scientific literature as applied to clinical spine surgery. METHODS This study utilized Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to review the scientific literature from 2009 to 2019 with syntax specific for machine learning and spine surgery applications. Specific data was extracted from the available literature including algorithm application, algorithms tested, database type and size, algorithm training method, and outcome of interest. RESULTS A total of 44 studies met inclusion criteria, of which the majority were level III evidence. Studies were grouped into 4 general types: diagnostic tools, clinical outcome prediction, surgical assessment tools, and decision support tools. Across studies, a wide swath of algorithms were used, which were trained across multiple disparate databases. There were no studies identified that assessed the ethical implementation or patient perceptions of machine learning in clinical care. CONCLUSIONS The results reveal the broad range of clinical applications and methods used to create machine learning algorithms for use in the field of spine surgery. Notable disparities exist in algorithm choice, database characteristics, and training methods. Ongoing research is needed to make machine learning operational on a large scale.
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Affiliation(s)
- Edward M DelSole
- Department of Orthopaedic Surgery, Division of Spine Surgery, Geisinger Musculoskeletal Institute
| | - Wyatt L Keck
- Geisinger Commonwealth School of Medicine, Scranton
| | - Aalpen A Patel
- Department of Radiology (Geisinger), Steele Institute for Health Innovation and Geisinger, Danville, PA
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40
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Madan N, Lucas J, Akhter N, Collier P, Cheng F, Guha A, Zhang L, Sharma A, Hamid A, Ndiokho I, Wen E, Garster NC, Scherrer-Crosbie M, Brown SA. Artificial intelligence and imaging: Opportunities in cardio-oncology. AMERICAN HEART JOURNAL PLUS : CARDIOLOGY RESEARCH AND PRACTICE 2022; 15:100126. [PMID: 35693323 PMCID: PMC9187287 DOI: 10.1016/j.ahjo.2022.100126] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 03/20/2022] [Accepted: 03/21/2022] [Indexed: 12/29/2022]
Abstract
Cardiovascular disease is a leading cause of death in cancer survivors. It is critical to apply new predictive and early diagnostic methods in this population, as this can potentially inform cardiovascular treatment and surveillance decision-making. We discuss the application of artificial intelligence (AI) technologies to cardiovascular imaging in cardio-oncology, with a particular emphasis on prevention and targeted treatment of a variety of cardiovascular conditions in cancer patients. Recently, the use of AI-augmented cardiac imaging in cardio-oncology is gaining traction. A large proportion of cardio-oncology patients are screened and followed using left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS), currently obtained using echocardiography. This use will continue to increase with new cardiotoxic cancer treatments. AI is being tested to increase precision, throughput, and accuracy of LVEF and GLS, guide point-of-care image acquisition, and integrate imaging and clinical data to optimize the prediction and detection of cardiac dysfunction. The application of AI to cardiovascular magnetic resonance imaging (CMR), computed tomography (CT; especially coronary artery calcium or CAC scans), single proton emission computed tomography (SPECT) and positron emission tomography (PET) imaging acquisition is also in early stages of analysis for prediction and assessment of cardiac tumors and cardiovascular adverse events in patients treated for childhood or adult cancer. The opportunities for application of AI in cardio-oncology imaging are promising, and if availed, will improve clinical practice and benefit patient care.
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Affiliation(s)
- Nidhi Madan
- Division of Cardiology, Rush University Medical Center, Chicago, IL, USA
| | | | - Nausheen Akhter
- Division of Cardiology, Northwestern University, Chicago, IL, USA
| | - Patrick Collier
- Robert and Suzanne Tomsich Department of Cardiovascular Medicine, Sydell and Arnold Miller Family Heart and Vascular Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Avirup Guha
- Harrington Heart and Vascular Institute, Cleveland, OH, USA
| | - Lili Zhang
- Cardio-Oncology Program, Division of Cardiology, Department of Medicine, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Abhinav Sharma
- Division of Cardiovascular Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
| | | | - Imeh Ndiokho
- Medical College of Wisconsin, Milwaukee, WI, USA
| | - Ethan Wen
- Medical College of Wisconsin, Milwaukee, WI, USA
| | - Noelle C. Garster
- Division of Cardiovascular Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
| | | | - Sherry-Ann Brown
- Cardio-Oncology Program, Division of Cardiovascular Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
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Pontone G, Rossi A, Guglielmo M, Dweck MR, Gaemperli O, Nieman K, Pugliese F, Maurovich-Horvat P, Gimelli A, Cosyns B, Achenbach S. Clinical applications of cardiac computed tomography: a consensus paper of the European Association of Cardiovascular Imaging-part II. Eur Heart J Cardiovasc Imaging 2022; 23:e136-e161. [PMID: 35175348 PMCID: PMC8944330 DOI: 10.1093/ehjci/jeab292] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 12/28/2021] [Indexed: 11/12/2022] Open
Abstract
Cardiac computed tomography (CT) was initially developed as a non-invasive diagnostic tool to detect and quantify coronary stenosis. Thanks to the rapid technological development, cardiac CT has become a comprehensive imaging modality which offers anatomical and functional information to guide patient management. This is the second of two complementary documents endorsed by the European Association of Cardiovascular Imaging aiming to give updated indications on the appropriate use of cardiac CT in different clinical scenarios. In this article, emerging CT technologies and biomarkers, such as CT-derived fractional flow reserve, perfusion imaging, and pericoronary adipose tissue attenuation, are described. In addition, the role of cardiac CT in the evaluation of atherosclerotic plaque, cardiomyopathies, structural heart disease, and congenital heart disease is revised.
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Affiliation(s)
- Gianluca Pontone
- Corresponding author. Tel: +39 02 58002574; Fax: +39 02 58002231. E-mail:
| | | | - Marco Guglielmo
- Centro Cardiologico Monzino IRCCS, Via C. Parea 4, 20138 Milan, Italy
| | - Marc R Dweck
- Centre for Cardiovascular Sciences, University of Edinburgh, Edinburgh, UK
| | | | - Koen Nieman
- Department of Radiology and Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Francesca Pugliese
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK,Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Pal Maurovich-Horvat
- MTA-SE Cardiovascular Imaging Research Group, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Alessia Gimelli
- Fondazione CNR/Regione Toscana “Gabriele Monasterio”, Pisa, Italy
| | - Bernard Cosyns
- Department of Cardiology, CHVZ (Centrum voor Hart en Vaatziekten), ICMI (In Vivo Cellular and Molecular Imaging) Laboratory, Universitair ziekenhuis Brussel, Brussel, Belgium
| | - Stephan Achenbach
- Department of Cardiology, Friedrich-Alexander-University of Erlangen, Erlangen, Germany
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Rampidis GP, Kampaktsis PΝ, Kouskouras K, Samaras A, Benetos G, Giannopoulos AΑ, Karamitsos T, Kallifatidis A, Samaras A, Vogiatzis I, Hadjimiltiades S, Ziakas A, Buechel RR, Gebhard C, Smilowitz NR, Toutouzas K, Tsioufis K, Prassopoulos P, Karvounis H, Reynolds H, Giannakoulas G. Role of cardiac CT in the diagnostic evaluation and risk stratification of patients with myocardial infarction and non-obstructive coronary arteries (MINOCA): rationale and design of the MINOCA-GR study. BMJ Open 2022; 12:e054698. [PMID: 35110321 PMCID: PMC8811605 DOI: 10.1136/bmjopen-2021-054698] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
INTRODUCTION Myocardial infarction with non-obstructive coronary arteries (MINOCA) occurs in 5%-15% of all patients with acute myocardial infarction. Cardiac MR (CMR) and optical coherence tomography have been used to identify the underlying pathophysiological mechanism in MINOCA. The role of cardiac CT angiography (CCTA) in patients with MINOCA, however, has not been well studied so far. CCTA can be used to assess atherosclerotic plaque volume, vulnerable plaque characteristics as well as pericoronary fat tissue attenuation, which has not been yet studied in MINOCA. METHODS AND ANALYSIS MINOCA-GR is a prospective, multicentre, observational cohort study based on a national registry that will use CCTA in combination with CMR and invasive coronary angiography (ICA) to evaluate the extent and characteristics of coronary atherosclerosis and its correlation with pericoronary fat attenuation in patients with MINOCA. A total of 60 consecutive adult patients across 4 participating study sites are expected to be enrolled. Following ICA and CMR, patients will undergo CCTA during index hospitalisation. The primary endpoints are quantification of extent and severity of coronary atherosclerosis, description of high-risk plaque features and attenuation profiling of pericoronary fat tissue around all three major epicardial coronary arteries in relation to CMR. Follow-up CCTA for the evaluation of changes in pericoronary fat attenuation will also be performed. MINOCA-GR aims to be the first study to explore the role of CCTA in combination with CMR and ICA in the underlying pathophysiological mechanisms and assisting in diagnostic evaluation and prognosis of patients with MINOCA. ETHICS AND DISSEMINATION The study protocol has been approved by the institutional review board/independent ethics committee at each site prior to study commencement. All patients will provide written informed consent. Results will be disseminated at national meetings and published in peer-reviewed journals. TRIAL REGISTRATION NUMBER NCT4186676.
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Affiliation(s)
- Georgios P Rampidis
- First Department of Cardiology, University General Hospital of Thessaloniki AHEPA, Thessaloniki, Greece
- Cardiac Imaging Unit, Diagnostic Center "PANAGIA", Veroia, Greece
| | | | - Konstantinos Kouskouras
- Department of Radiology, University General Hospital of Thessaloniki AHEPA, Thessaloniki, Greece
| | - Athanasios Samaras
- First Department of Cardiology, University General Hospital of Thessaloniki AHEPA, Thessaloniki, Greece
| | - Georgios Benetos
- First Department of Cardiology, Hippokration Hospital, Athens, Greece
| | - Andreas Α Giannopoulos
- Department of Nuclear Medicine - Cardiac Imaging Unit, University Hospital Zurich, Zurich, Switzerland
| | - Theodoros Karamitsos
- First Department of Cardiology, University General Hospital of Thessaloniki AHEPA, Thessaloniki, Greece
| | | | - Antonios Samaras
- Department of Cardiology, General Hospital of Veroia, Veroia, Greece
| | - Ioannis Vogiatzis
- Department of Cardiology, General Hospital of Veroia, Veroia, Greece
| | - Stavros Hadjimiltiades
- First Department of Cardiology, University General Hospital of Thessaloniki AHEPA, Thessaloniki, Greece
| | - Antonios Ziakas
- First Department of Cardiology, University General Hospital of Thessaloniki AHEPA, Thessaloniki, Greece
| | - Ronny R Buechel
- Department of Nuclear Medicine - Cardiac Imaging Unit, University Hospital Zurich, Zurich, Switzerland
| | - Catherine Gebhard
- Department of Nuclear Medicine - Cardiac Imaging Unit, University Hospital Zurich, Zurich, Switzerland
| | | | | | | | - Panagiotis Prassopoulos
- Department of Radiology, University General Hospital of Thessaloniki AHEPA, Thessaloniki, Greece
| | - Haralambos Karvounis
- First Department of Cardiology, University General Hospital of Thessaloniki AHEPA, Thessaloniki, Greece
| | - Harmony Reynolds
- Sarah Ross Soter Center for Women's Cardiovascular Research, Leon H. Charney Division of Cardiology, Department of Medicine, NYU Grossman School of Medicine, New York, New York, USA
| | - George Giannakoulas
- First Department of Cardiology, University General Hospital of Thessaloniki AHEPA, Thessaloniki, Greece
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Shu ZY, Cui SJ, Zhang YQ, Xu YY, Hung SC, Fu LP, Pang PP, Gong XY, Jin QY. Predicting Chronic Myocardial Ischemia Using CCTA-Based Radiomics Machine Learning Nomogram. J Nucl Cardiol 2022; 29:262-274. [PMID: 32557238 DOI: 10.1007/s12350-020-02204-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 05/05/2020] [Indexed: 12/13/2022]
Abstract
BACKGROUND Coronary computed tomography angiography (CCTA) is a well-established non-invasive diagnostic test for the assessment of coronary artery diseases (CAD). CCTA not only provides information on luminal stenosis but also permits non-invasive assessment and quantitative measurement of stenosis based on radiomics. PURPOSE This study is aimed to develop and validate a CT-based radiomics machine learning for predicting chronic myocardial ischemia (MIS). METHODS CCTA and SPECT-myocardial perfusion imaging (MPI) of 154 patients with CAD were retrospectively analyzed and 94 patients were diagnosed with MIS. The patients were randomly divided into two sets: training (n = 107) and test (n = 47). Features were extracted for each CCTA cross-sectional image to identify myocardial segments. Multivariate logistic regression was used to establish a radiomics signature after feature dimension reduction. Finally, the radiomics nomogram was built based on a predictive model of MIS which in turn was constructed by machine learning combined with the clinically related factors. We then validated the model using data from 49 CAD patients and included 18 MIS patients from another medical center. The receiver operating characteristic curve evaluated the diagnostic accuracy of the nomogram based on the training set and was validated by the test and validation set. Decision curve analysis (DCA) was used to validate the clinical practicability of the nomogram. RESULTS The accuracy of the nomogram for the prediction of MIS in the training, test and validation sets was 0.839, 0.832, and 0.816, respectively. The diagnosis accuracy of the nomogram, signature, and vascular stenosis were 0.824, 0.736 and 0.708, respectively. A significant difference in the number of patients with MIS between the high and low-risk groups was identified based on the nomogram (P < .05). The DCA curve demonstrated that the nomogram was clinically feasible. CONCLUSION The radiomics nomogram constructed based on the image of CCTA act as a non-invasive tool for predicting MIS that helps to identify high-risk patients with coronary artery disease.
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Affiliation(s)
- Zhen-Yu Shu
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, Zhejiang, China
| | - Si-Jia Cui
- Second Clinical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yue-Qiao Zhang
- Department of Radiology, Shao-Yifu Hospital Affiliated to Zhejiang University, Hangzhou, China
| | - Yu-Yun Xu
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, Zhejiang, China
| | - Shng-Che Hung
- Division of Neuroradiology, Department of Radiology, University of North Carolina School of Medicine, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Li-Ping Fu
- Department of Nuclear Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, China
| | | | - Xiang-Yang Gong
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, Zhejiang, China.
- Institute of Artificial Intelligence and Remote Imaging, Hangzhou Medical College, Hangzhou, China.
| | - Qin-Yang Jin
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, Zhejiang, China.
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Sanchez-Martinez S, Camara O, Piella G, Cikes M, González-Ballester MÁ, Miron M, Vellido A, Gómez E, Fraser AG, Bijnens B. Machine Learning for Clinical Decision-Making: Challenges and Opportunities in Cardiovascular Imaging. Front Cardiovasc Med 2022; 8:765693. [PMID: 35059445 PMCID: PMC8764455 DOI: 10.3389/fcvm.2021.765693] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 12/07/2021] [Indexed: 11/30/2022] Open
Abstract
The use of machine learning (ML) approaches to target clinical problems is called to revolutionize clinical decision-making in cardiology. The success of these tools is dependent on the understanding of the intrinsic processes being used during the conventional pathway by which clinicians make decisions. In a parallelism with this pathway, ML can have an impact at four levels: for data acquisition, predominantly by extracting standardized, high-quality information with the smallest possible learning curve; for feature extraction, by discharging healthcare practitioners from performing tedious measurements on raw data; for interpretation, by digesting complex, heterogeneous data in order to augment the understanding of the patient status; and for decision support, by leveraging the previous steps to predict clinical outcomes, response to treatment or to recommend a specific intervention. This paper discusses the state-of-the-art, as well as the current clinical status and challenges associated with the two later tasks of interpretation and decision support, together with the challenges related to the learning process, the auditability/traceability, the system infrastructure and the integration within clinical processes in cardiovascular imaging.
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Affiliation(s)
| | - Oscar Camara
- Department of Information and Communication Technologies, University Pompeu Fabra, Barcelona, Spain
| | - Gemma Piella
- Department of Information and Communication Technologies, University Pompeu Fabra, Barcelona, Spain
| | - Maja Cikes
- Department of Cardiovascular Diseases, University of Zagreb School of Medicine, University Hospital Centre Zagreb, Zagreb, Croatia
| | | | - Marius Miron
- Joint Research Centre, European Commission, Seville, Spain
| | - Alfredo Vellido
- Computer Science Department, Intelligent Data Science and Artificial Intelligence (IDEAI-UPC) Research Center, Universitat Politècnica de Catalunya, Barcelona, Spain
| | - Emilia Gómez
- Department of Information and Communication Technologies, University Pompeu Fabra, Barcelona, Spain
- Joint Research Centre, European Commission, Seville, Spain
| | - Alan G. Fraser
- School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Bart Bijnens
- August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain
- ICREA, Barcelona, Spain
- Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
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45
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Kulkarni P, Mahadevappa M, Chilakamarri S. The Emergence of Artificial Intelligence in Cardiology: Current and Future Applications. Curr Cardiol Rev 2022; 18:e191121198124. [PMID: 34802407 PMCID: PMC9615212 DOI: 10.2174/1573403x17666211119102220] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 10/18/2021] [Accepted: 10/25/2021] [Indexed: 11/22/2022] Open
Abstract
Artificial intelligence technology is emerging as a promising entity in cardiovascular medicine, potentially improving diagnosis and patient care. In this article, we review the literature on artificial intelligence and its utility in cardiology. We provide a detailed description of concepts of artificial intelligence tools like machine learning, deep learning, and cognitive computing. This review discusses the current evidence, application, prospects, and limitations of artificial intelligence in cardiology.
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Affiliation(s)
- Prashanth Kulkarni
- Department of Cardiology, Kindle Clinics, Gachibowli, Hyderabad, 500032 India
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46
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Artificial Intelligence: A Shifting Paradigm in Cardio-Cerebrovascular Medicine. J Clin Med 2021; 10:jcm10235710. [PMID: 34884412 PMCID: PMC8658222 DOI: 10.3390/jcm10235710] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 12/02/2021] [Indexed: 12/21/2022] Open
Abstract
The future of healthcare is an organic blend of technology, innovation, and human connection. As artificial intelligence (AI) is gradually becoming a go-to technology in healthcare to improve efficiency and outcomes, we must understand our limitations. We should realize that our goal is not only to provide faster and more efficient care, but also to deliver an integrated solution to ensure that the care is fair and not biased to a group of sub-population. In this context, the field of cardio-cerebrovascular diseases, which encompasses a wide range of conditions-from heart failure to stroke-has made some advances to provide assistive tools to care providers. This article aimed to provide an overall thematic review of recent development focusing on various AI applications in cardio-cerebrovascular diseases to identify gaps and potential areas of improvement. If well designed, technological engines have the potential to improve healthcare access and equitability while reducing overall costs, diagnostic errors, and disparity in a system that affects patients and providers and strives for efficiency.
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47
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Freitas SA, Nienow D, da Costa CA, Ramos GDO. Functional Coronary Artery Assessment: a Systematic Literature Review. Wien Klin Wochenschr 2021; 134:302-318. [PMID: 34870740 DOI: 10.1007/s00508-021-01970-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 10/11/2021] [Indexed: 11/28/2022]
Abstract
Cardiovascular diseases represent the number one cause of death in the world, including the most common disorders in the heart's health, namely coronary artery disease (CAD). CAD is mainly caused by fat accumulated in the arteries' internal walls, creating an atherosclerotic plaque that impacts the blood flow functional behavior. Anatomical plaque characteristics are essential but not sufficient for a complete functional assessment of CAD. In fact, plaque analysis and visual inspection alone have proven insufficient to determine the lesion severity and hemodynamic repercussion. Furthermore, the fractional flow reserve (FFR) exam, which is considered the gold standard for stenosis functional impair determination, is invasive and contains several limitations. Such a panorama evidences the need for new techniques applied to image exams to improve CAD functional assessment. In this article, we perform a systematic literature review on emerging methods determining CAD significance, thus delivering a unique base for comparing these methods, qualitatively and quantitatively. Our goal is to guide further studies with evidence from the most promising methods, highlighting the benefits from both areas. We summarize benchmarks, metrics for evaluation, and challenges already faced, thus shedding light on the requirements for a valid, meaningful, and accepted technique for functional assessment evaluation. We create a base of comparison based on quantitative and qualitative indicators and highlight the most relevant geometrical metrics that correlate with lesion significance. Finally, we point out future benchmarks based on recent literature.
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Affiliation(s)
- Samuel A Freitas
- Software Innovation Laboratory, Graduate Program in Applied Computing, Universidade do Vale do Rio dos Sinos, São Leopoldo, Brazil
| | - Débora Nienow
- Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - Cristiano A da Costa
- Software Innovation Laboratory, Graduate Program in Applied Computing, Universidade do Vale do Rio dos Sinos, São Leopoldo, Brazil
| | - Gabriel de O Ramos
- Software Innovation Laboratory, Graduate Program in Applied Computing, Universidade do Vale do Rio dos Sinos, São Leopoldo, Brazil.
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48
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Lin A, Kolossváry M, Motwani M, Išgum I, Maurovich-Horvat P, Slomka PJ, Dey D. Artificial intelligence in cardiovascular CT: Current status and future implications. J Cardiovasc Comput Tomogr 2021; 15:462-469. [PMID: 33812855 PMCID: PMC8455701 DOI: 10.1016/j.jcct.2021.03.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 01/29/2021] [Accepted: 03/15/2021] [Indexed: 12/23/2022]
Abstract
Artificial intelligence (AI) refers to the use of computational techniques to mimic human thought processes and learning capacity. The past decade has seen a rapid proliferation of AI developments for cardiovascular computed tomography (CT). These algorithms aim to increase efficiency, objectivity, and performance in clinical tasks such as image quality improvement, structure segmentation, quantitative measurements, and outcome prediction. By doing so, AI has the potential to streamline clinical workflow, increase interpretative speed and accuracy, and inform subsequent clinical pathways. This review covers state-of-the-art AI techniques in cardiovascular CT and the future role of AI as a clinical support tool.
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Affiliation(s)
- Andrew Lin
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Márton Kolossváry
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Manish Motwani
- Manchester Heart Centre, Manchester University Hospitals NHS Foundation Trust, Manchester, United Kingdom
| | - Ivana Išgum
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Centers, Location AMC, University of Amsterdam, Amsterdam, Netherlands; Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Location AMC, University of Amsterdam, Amsterdam, Netherlands; Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | | | - Piotr J Slomka
- Artificial Intelligence in Medicine Program, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Damini Dey
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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Seetharam K, Bhat P, Orris M, Prabhu H, Shah J, Asti D, Chawla P, Mir T. Artificial intelligence and machine learning in cardiovascular computed tomography. World J Cardiol 2021; 13:546-555. [PMID: 34754399 PMCID: PMC8554359 DOI: 10.4330/wjc.v13.i10.546] [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: 05/08/2021] [Revised: 07/10/2021] [Accepted: 08/13/2021] [Indexed: 02/06/2023] Open
Abstract
Computed tomography (CT) is emerging as a prominent diagnostic modality in the field of cardiovascular imaging. Artificial intelligence (AI) is making significant strides in the field of information technology, the commercial industry, and health care. Machine learning (ML), a branch of AI, can optimize the performance of CT and augment the assessment of coronary artery disease. These ML platforms can automate multiple tasks, perform calculations, and integrate information from a variety of data sources. In this review article, we explore the ML in CT imaging.
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Affiliation(s)
- Karthik Seetharam
- Department of Cardiology, West Virgina University, Morgan Town, NY 26501, United States
| | - Premila Bhat
- Department of Medicine, Wyckoff Heights Medical Center, Brooklyn, NY 11237, United States
| | - Maxine Orris
- Department of Medicine, Wyckoff Heights Medical Center, Brooklyn, NY 11237, United States
| | - Hejmadi Prabhu
- Department of Cardiology, Wyckoff Heights Medical Center, Brooklyn, NY 11237, United States
| | - Jilan Shah
- Department of Medicine, Wyckoff Heights Medical Center, Brooklyn, NY 11237, United States
| | - Deepak Asti
- Department of Cardiology, Wyckoff Heights Medical Center, Brooklyn, NY 11237, United States
| | - Preety Chawla
- Department of Cardiology, Wyckoff Heights Medical Center, Brooklyn, NY 11237, United States
| | - Tanveer Mir
- Department of Internal Medicine, Wyckoff Heights Medical Center, Brooklyn, NY 11237, United States
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50
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Lauzier PT, Avram R, Dey D, Slomka P, Afilalo J, Chow BJ. The evolving role of artificial intelligence in cardiac image analysis. Can J Cardiol 2021; 38:214-224. [PMID: 34619340 DOI: 10.1016/j.cjca.2021.09.030] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 09/28/2021] [Accepted: 09/28/2021] [Indexed: 12/13/2022] Open
Abstract
Research in artificial intelligence (AI) have progressed over the last decade. The field of cardiac imaging has seen significant developments using newly developed deep learning methods for automated image analysis and AI tools for disease detection and prognostication. This review article is aimed at those without special background in AI. We review AI concepts and we survey the growing contemporary applications of AI for image analysis in echocardiography, nuclear cardiology, cardiac computed tomography, cardiac magnetic resonance, and invasive angiography.
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Affiliation(s)
| | - Robert Avram
- University of Ottawa Heart Institute, Ottawa, ON, Canada; Montreal Heart Institute, Montreal, QC, Canada
| | - Damini Dey
- Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Piotr Slomka
- Cedars-Sinai Medical Center, Los Angeles, CA, USA
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