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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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2
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Xu Y, Li W, Yang Y, Dong S, Meng F, Zhang K, Wang Y, Ruan L, Zhang L. Deep learning-based prediction of coronary artery calcium scoring in hemodialysis patients using radial artery calcification. Semin Dial 2024; 37:234-241. [PMID: 38178376 DOI: 10.1111/sdi.13191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 10/30/2023] [Accepted: 11/28/2023] [Indexed: 01/06/2024]
Abstract
OBJECTIVE This study used random forest model to explore the feasibility of radial artery calcification in prediction of coronary artery calcification in hemodialysis patients. MATERIAL AND METHODS We enrolled hemodialysis patients and performed ultrasound examinations on their radial arteries to evaluate the calcification status using a calcification index. All involved patients received coronary artery computed tomography scans to generate coronary artery calcification scores (CACS). Clinical variables were collected from all patients. We constructed both a random forest model and a logistic regression model to predict CACS. Logistic regression model was used to identify the risk factors of radial artery calcification. RESULTS One hundred eighteen patients were included in our analysis. In random forest model, the radial artery calcification index, age, serum C-reactive protein, body mass index (BMI), diabetes, and hypertension history were related to CACS based on the average decrease of the Gini coefficient. The random forest model achieved a sensitivity of 76.9%, specificity of 75.0%, and area under receiver operating characteristic of 0.869, while the logistic regression model achieved a sensitivity of 75.2%, specificity of 68.7%, and area under receiver operating characteristic of 0.742 in prediction of CACS. Sex, BMI index, smoking history, hypertension history, diabetes history, and serum total calcium were all the risk factors related to radial artery calcification. CONCLUSIONS A random forest model based on radial artery calcification could be used to predict CACS in hemodialysis patients, providing a potential method for rapid screening and prediction of coronary artery calcification.
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Affiliation(s)
- Yuankai Xu
- Department of Nephrology, Zhejiang Hospital, Hangzhou City, China
| | - Wen Li
- Department of Nephrology, The First Hospital of Hebei Medical University, Shijiazhuang City, China
| | - Yanli Yang
- Department of Nephrology, The First Hospital of Hebei Medical University, Shijiazhuang City, China
| | - Shiyi Dong
- Department of Nephrology, The First Hospital of Hebei Medical University, Shijiazhuang City, China
| | - Fulei Meng
- Department of Nephrology, The First Hospital of Hebei Medical University, Shijiazhuang City, China
| | - Kaidi Zhang
- Department of Nephrology, The First Hospital of Hebei Medical University, Shijiazhuang City, China
| | - Yuhuan Wang
- Department of Nephrology, The First Hospital of Shijiazhuang City, Shijiazhuang, China
| | - Lin Ruan
- Department of Nephrology, The First Hospital of Hebei Medical University, Shijiazhuang City, China
| | - Lihong Zhang
- Department of Nephrology, The First Hospital of Hebei Medical University, Shijiazhuang City, China
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Cundari G, Marchitelli L, Pambianchi G, Catapano F, Conia L, Stancanelli G, Catalano C, Galea N. Imaging biomarkers in cardiac CT: moving beyond simple coronary anatomical assessment. LA RADIOLOGIA MEDICA 2024; 129:380-400. [PMID: 38319493 PMCID: PMC10942914 DOI: 10.1007/s11547-024-01771-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 01/03/2024] [Indexed: 02/07/2024]
Abstract
Cardiac computed tomography angiography (CCTA) is considered the standard non-invasive tool to rule-out obstructive coronary artery disease (CAD). Moreover, several imaging biomarkers have been developed on cardiac-CT imaging to assess global CAD severity and atherosclerotic burden, including coronary calcium scoring, the segment involvement score, segment stenosis score and the Leaman-score. Myocardial perfusion imaging enables the diagnosis of myocardial ischemia and microvascular damage, and the CT-based fractional flow reserve quantification allows to evaluate non-invasively hemodynamic impact of the coronary stenosis. The texture and density of the epicardial and perivascular adipose tissue, the hypodense plaque burden, the radiomic phenotyping of coronary plaques or the fat radiomic profile are novel CT imaging features emerging as biomarkers of inflammation and plaque instability, which may implement the risk stratification strategies. The ability to perform myocardial tissue characterization by extracellular volume fraction and radiomic features appears promising in predicting arrhythmogenic risk and cardiovascular events. New imaging biomarkers are expanding the potential of cardiac CT for phenotyping the individual profile of CAD involvement and opening new frontiers for the practice of more personalized medicine.
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Affiliation(s)
- Giulia Cundari
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy
| | - Livia Marchitelli
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy
| | - Giacomo Pambianchi
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy
| | - Federica Catapano
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini, 4, Pieve Emanuele, 20090, Milano, Italy
- Humanitas Research Hospital IRCCS, Via Alessandro Manzoni, 56, Rozzano, 20089, Milano, Italy
| | - Luca Conia
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy
| | - Giuseppe Stancanelli
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy
| | - Carlo Catalano
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy
| | - Nicola Galea
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy.
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Alahdab F, El Shawi R, Ahmed AI, Han Y, Al-Mallah M. Patient-level explainable machine learning to predict major adverse cardiovascular events from SPECT MPI and CCTA imaging. PLoS One 2023; 18:e0291451. [PMID: 37967112 PMCID: PMC10651041 DOI: 10.1371/journal.pone.0291451] [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: 04/13/2023] [Accepted: 08/30/2023] [Indexed: 11/17/2023] Open
Abstract
BACKGROUND Machine learning (ML) has shown promise in improving the risk prediction in non-invasive cardiovascular imaging, including SPECT MPI and coronary CT angiography. However, most algorithms used remain black boxes to clinicians in how they compute their predictions. Furthermore, objective consideration of the multitude of available clinical data, along with the visual and quantitative assessments from CCTA and SPECT, are critical for optimal patient risk stratification. We aim to provide an explainable ML approach to predict MACE using clinical, CCTA, and SPECT data. METHODS Consecutive patients who underwent clinically indicated CCTA and SPECT myocardial imaging for suspected CAD were included and followed up for MACEs. A MACE was defined as a composite outcome that included all-cause mortality, myocardial infarction, or late revascularization. We employed an Automated Machine Learning (AutoML) approach to predict MACE using clinical, CCTA, and SPECT data. Various mainstream models with different sets of hyperparameters have been explored, and critical predictors of risk are obtained using explainable techniques on the global and patient levels. Ten-fold cross-validation was used in training and evaluating the AutoML model. RESULTS A total of 956 patients were included (mean age 61.1 ±14.2 years, 54% men, 89% hypertension, 81% diabetes, 84% dyslipidemia). Obstructive CAD on CCTA and ischemia on SPECT were observed in 14% of patients, and 11% experienced MACE. ML prediction's sensitivity, specificity, and accuracy in predicting a MACE were 69.61%, 99.77%, and 96.54%, respectively. The top 10 global predictive features included 8 CCTA attributes (segment involvement score, number of vessels with severe plaque ≥70, ≥50% stenosis in the left marginal coronary artery, calcified plaque, ≥50% stenosis in the left circumflex coronary artery, plaque type in the left marginal coronary artery, stenosis degree in the second obtuse marginal of the left circumflex artery, and stenosis category in the marginals of the left circumflex artery) and 2 clinical features (past medical history of MI or left bundle branch block, being an ever smoker). CONCLUSION ML can accurately predict risk of developing a MACE in patients suspected of CAD undergoing SPECT MPI and CCTA. ML feature-ranking can also show, at a sample- as well as at a patient-level, which features are key in making such a prediction.
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Affiliation(s)
- Fares Alahdab
- Houston Methodist DeBakey Heart & Vascular Center, Houston, TX, United States of America
| | - Radwa El Shawi
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Ahmed Ibrahim Ahmed
- Houston Methodist DeBakey Heart & Vascular Center, Houston, TX, United States of America
| | - Yushui Han
- Houston Methodist DeBakey Heart & Vascular Center, Houston, TX, United States of America
| | - Mouaz Al-Mallah
- Houston Methodist DeBakey Heart & Vascular Center, Houston, TX, United States of America
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Bienstock S, Lin F, Blankstein R, Leipsic J, Cardoso R, Ahmadi A, Gelijns A, Patel K, Baldassarre LA, Hadley M, LaRocca G, Sanz J, Narula J, Chandrashekhar YS, Shaw LJ, Fuster V. Advances in Coronary Computed Tomographic Angiographic Imaging of Atherosclerosis for Risk Stratification and Preventive Care. JACC Cardiovasc Imaging 2023; 16:1099-1115. [PMID: 37178070 DOI: 10.1016/j.jcmg.2023.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 01/04/2023] [Accepted: 02/01/2023] [Indexed: 05/15/2023]
Abstract
The diagnostic evaluation of coronary artery disease is undergoing a dramatic transformation with a new focus on atherosclerotic plaque. This review details the evidence needed for effective risk stratification and targeted preventive care based on recent advances in automated measurement of atherosclerosis from coronary computed tomography angiography (CTA). To date, research findings support that automated stenosis measurement is reasonably accurate, but evidence on variability by location, artery size, or image quality is unknown. The evidence for quantification of atherosclerotic plaque is unfolding, with strong concordance reported between coronary CTA and intravascular ultrasound measurement of total plaque volume (r >0.90). Statistical variance is higher for smaller plaque volumes. Limited data are available on how technical or patient-specific factors result in measurement variability by compositional subgroups. Coronary artery dimensions vary by age, sex, heart size, coronary dominance, and race and ethnicity. Accordingly, quantification programs excluding smaller arteries affect accuracy for women, patients with diabetes, and other patient subsets. Evidence is unfolding that quantification of atherosclerotic plaque is useful to enhance risk prediction, yet more evidence is required to define high-risk patients across varied populations and to determine whether such information is incremental to risk factors or currently used coronary computed tomography techniques (eg, coronary artery calcium scoring or visual assessment of plaque burden or stenosis). In summary, there is promise for the utility of coronary CTA quantification of atherosclerosis, especially if it can lead to targeted and more intensive cardiovascular prevention, notably for those patients with nonobstructive coronary artery disease and high-risk plaque features. The new quantification techniques available to imagers must not only provide sufficient added value to improve patient care, but also add minimal and reasonable cost to alleviate the financial burden on our patients and the health care system.
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Affiliation(s)
- Solomon Bienstock
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Fay Lin
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ron Blankstein
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Jonathon Leipsic
- University of British Columbia, Vancouver, British Columbia, Canada
| | - Rhanderson Cardoso
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Amir Ahmadi
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Annetine Gelijns
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Krishna Patel
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Lauren A Baldassarre
- Department of Cardiovascular Medicine and Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Michael Hadley
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Gina LaRocca
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Javier Sanz
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jagat Narula
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | - Leslee J Shaw
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
| | - Valentin Fuster
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Dikici E, Nguyen XV, Takacs N, Prevedello LM. Prediction of model generalizability for unseen data: Methodology and case study in brain metastases detection in T1-Weighted contrast-enhanced 3D MRI. Comput Biol Med 2023; 159:106901. [PMID: 37068317 DOI: 10.1016/j.compbiomed.2023.106901] [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: 12/22/2022] [Revised: 03/08/2023] [Accepted: 04/09/2023] [Indexed: 04/19/2023]
Abstract
BACKGROUND AND PURPOSE A medical AI system's generalizability describes the continuity of its performance acquired from varying geographic, historical, and methodologic settings. Previous literature on this topic has mostly focused on "how" to achieve high generalizability (e.g., via larger datasets, transfer learning, data augmentation, model regularization schemes), with limited success. Instead, we aim to understand "when" the generalizability is achieved: Our study presents a medical AI system that could estimate its generalizability status for unseen data on-the-fly. MATERIALS AND METHODS We introduce a latent space mapping (LSM) approach utilizing Fréchet distance loss to force the underlying training data distribution into a multivariate normal distribution. During the deployment, a given test data's LSM distribution is processed to detect its deviation from the forced distribution; hence, the AI system could predict its generalizability status for any previously unseen data set. If low model generalizability is detected, then the user is informed by a warning message integrated into a sample deployment workflow. While the approach is applicable for most classification deep neural networks (DNNs), we demonstrate its application to a brain metastases (BM) detector for T1-weighted contrast-enhanced (T1c) 3D MRI. The BM detection model was trained using 175 T1c studies acquired internally (from the authors' institution) and tested using (1) 42 internally acquired exams and (2) 72 externally acquired exams from the publicly distributed Brain Mets dataset provided by the Stanford University School of Medicine. Generalizability scores, false positive (FP) rates, and sensitivities of the BM detector were computed for the test datasets. RESULTS AND CONCLUSION The model predicted its generalizability to be low for 31% of the testing data (i.e., two of the internally and 33 of the externally acquired exams), where it produced (1) ∼13.5 false positives (FPs) at 76.1% BM detection sensitivity for the low and (2) ∼10.5 FPs at 89.2% BM detection sensitivity for the high generalizability groups respectively. These results suggest that the proposed formulation enables a model to predict its generalizability for unseen data.
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Affiliation(s)
- Engin Dikici
- The Ohio State University, College of Medicine, Department of Radiology, Columbus, OH, 43210, USA.
| | - Xuan V Nguyen
- The Ohio State University, College of Medicine, Department of Radiology, Columbus, OH, 43210, USA
| | - Noah Takacs
- The Ohio State University, College of Medicine, Department of Radiology, Columbus, OH, 43210, USA
| | - Luciano M Prevedello
- The Ohio State University, College of Medicine, Department of Radiology, Columbus, OH, 43210, USA
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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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Artificial Intelligence in Cardiovascular CT and MR Imaging. Life (Basel) 2023; 13:life13020507. [PMID: 36836864 PMCID: PMC9968221 DOI: 10.3390/life13020507] [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: 01/22/2023] [Revised: 02/06/2023] [Accepted: 02/09/2023] [Indexed: 02/15/2023] Open
Abstract
The technological development of Artificial Intelligence (AI) has grown rapidly in recent years. The applications of AI to cardiovascular imaging are various and could improve the radiologists' workflow, speeding up acquisition and post-processing time, increasing image quality and diagnostic accuracy. Several studies have already proved AI applications in Coronary Computed Tomography Angiography and Cardiac Magnetic Resonance, including automatic evaluation of calcium score, quantification of coronary stenosis and plaque analysis, or the automatic quantification of heart volumes and myocardial tissue characterization. The aim of this review is to summarize the latest advances in the field of AI applied to cardiovascular CT and MR imaging.
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Kampaktsis PN, Emfietzoglou M, Al Shehhi A, Fasoula NA, Bakogiannis C, Mouselimis D, Tsarouchas A, Vassilikos VP, Kallmayer M, Eckstein HH, Hadjileontiadis L, Karlas A. Artificial intelligence in atherosclerotic disease: Applications and trends. Front Cardiovasc Med 2023; 9:949454. [PMID: 36741834 PMCID: PMC9896100 DOI: 10.3389/fcvm.2022.949454] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 12/28/2022] [Indexed: 01/21/2023] Open
Abstract
Atherosclerotic cardiovascular disease (ASCVD) is the most common cause of death globally. Increasing amounts of highly diverse ASCVD data are becoming available and artificial intelligence (AI) techniques now bear the promise of utilizing them to improve diagnosis, advance understanding of disease pathogenesis, enable outcome prediction, assist with clinical decision making and promote precision medicine approaches. Machine learning (ML) algorithms in particular, are already employed in cardiovascular imaging applications to facilitate automated disease detection and experts believe that ML will transform the field in the coming years. Current review first describes the key concepts of AI applications from a clinical standpoint. We then provide a focused overview of current AI applications in four main ASCVD domains: coronary artery disease (CAD), peripheral arterial disease (PAD), abdominal aortic aneurysm (AAA), and carotid artery disease. For each domain, applications are presented with refer to the primary imaging modality used [e.g., computed tomography (CT) or invasive angiography] and the key aim of the applied AI approaches, which include disease detection, phenotyping, outcome prediction, and assistance with clinical decision making. We conclude with the strengths and limitations of AI applications and provide future perspectives.
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Affiliation(s)
- Polydoros N. Kampaktsis
- Division of Cardiology, Columbia University Irving Medical Center, New York, NY, United States,*Correspondence: Polydoros N. Kampaktsis,
| | - Maria Emfietzoglou
- Heart Centre, John Radcliffe Hospital, Oxford University Hospitals, NHS Foundation Trust, Oxford, United Kingdom
| | - Aamna Al Shehhi
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Nikolina-Alexia Fasoula
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany,School of Medicine, Chair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), Technical University of Munich, Munich, Germany
| | - Constantinos Bakogiannis
- Third Department of Cardiology, Hippokration University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Dimitrios Mouselimis
- Third Department of Cardiology, Hippokration University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Anastasios Tsarouchas
- Third Department of Cardiology, Hippokration University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vassilios P. Vassilikos
- Third Department of Cardiology, Hippokration University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Michael Kallmayer
- Department for Vascular and Endovascular Surgery, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Hans-Henning Eckstein
- Department for Vascular and Endovascular Surgery, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany,DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
| | - Leontios Hadjileontiadis
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates,Healthcare Innovation Center, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates,Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Angelos Karlas
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany,School of Medicine, Chair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), Technical University of Munich, Munich, Germany,Department for Vascular and Endovascular Surgery, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany,DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
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Burch RA, Siddiqui TA, Tou LC, Turner KB, Umair M. The Cost Effectiveness of Coronary CT Angiography and the Effective Utilization of CT-Fractional Flow Reserve in the Diagnosis of Coronary Artery Disease. J Cardiovasc Dev Dis 2023; 10:jcdd10010025. [PMID: 36661920 PMCID: PMC9863924 DOI: 10.3390/jcdd10010025] [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: 09/19/2022] [Revised: 12/10/2022] [Accepted: 12/24/2022] [Indexed: 01/11/2023] Open
Abstract
Given the high global disease burden of coronary artery disease (CAD), a major problem facing healthcare economic policy is identifying the most cost-effective diagnostic strategy for patients with suspected CAD. The aim of this review is to assess the long-term cost-effectiveness of coronary computed tomography angiography (CCTA) when compared with other diagnostic modalities and to define the cost and effective diagnostic utilization of computed tomography-fractional flow reserve (CT-FFR). A search was conducted through the MEDLINE database using PubMed with 16 of 119 manuscripts fitting the inclusion and exclusion criteria for review. An analysis of the data included in this review suggests that CCTA is a cost-effective strategy for both low risk acute chest pain patients presenting to the emergency department (ED) and low-to-intermediate risk stable chest pain outpatients. For patients with intermediate-to-high risk, CT-FFR is superior to CCTA in identifying clinically significant stenosis. In low-to-intermediate risk patients, CCTA provides a cost-effective diagnostic strategy with the potential to reduce economic burden and improve long-term health outcomes. CT-FFR should be utilized in intermediate-to-high risk patients with stenosis of uncertain clinical significance. Long-term analysis of cost-effectiveness and diagnostic utility is needed to determine the optimal balance between the cost-effectiveness and diagnostic utility of CT-FFR.
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Affiliation(s)
- Rex A. Burch
- Philadelphia College of Osteopathic Medicine, 625 Old Peachtree Rd NW, Suwanee, GA 30024, USA
- Correspondence:
| | - Taha A. Siddiqui
- Philadelphia College of Osteopathic Medicine, 625 Old Peachtree Rd NW, Suwanee, GA 30024, USA
| | - Leila C. Tou
- Charles E. Schmidt College of Medicine, Florida Atlantic University, 777 Glades Road BC-71, Boca Raton, FL 33431, USA
| | - Kiera B. Turner
- Charles E. Schmidt College of Medicine, Florida Atlantic University, 777 Glades Road BC-71, Boca Raton, FL 33431, USA
| | - Muhammad Umair
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins Hospital, 601 N Caroline St, Baltimore, MD 21205, USA
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11
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Huang Y, Ren Y, Yang H, Ding Y, Liu Y, Yang Y, Mao A, Yang T, Wang Y, Xiao F, He Q, Zhang Y. Using a machine learning-based risk prediction model to analyze the coronary artery calcification score and predict coronary heart disease and risk assessment. Comput Biol Med 2022; 151:106297. [PMID: 36435054 DOI: 10.1016/j.compbiomed.2022.106297] [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: 08/07/2022] [Revised: 10/12/2022] [Accepted: 11/06/2022] [Indexed: 11/16/2022]
Abstract
OBJECTIVES To calculate the coronary artery calcification score (CACS) obtained from coronary artery computed tomography angiography (CCTA) examination and combine it with the influencing factors of coronary artery calcification (CAC), which is then analyzed by machine learning (ML) to predict the probability of coronary heart disease(CHD). METHODS All patients who were admitted to the Affiliated Hospital of Traditional Chinese Medicine of Southwest Medical University from January 2019 to March 2022, suspected of CHD, and underwent CCTA inspection were retrospectively selected. The degree of CAC was quantified based on the Agatston score. To compare the correlation between the CACS and clinical-related factors, we collected 31 variables, including hypertension, diabetes, smoking, hyperlipidemia, among others. ML models containing the random forest (RF), radial basis function neural network (RBFNN),support vector machine (SVM),K-Nearest Neighbor algorithm (KNN) and kernel ridge regression (KRR) were used to assess the risk of CHD based on CACS and clinical-related factors. RESULTS Among the five ML models, RF achieves the best performance about accuracy (ACC) (78.96%), sensitivity (SN) (93.86%), specificity(Spe) (51.13%), and Matthew's correlation coefficient (MCC) (0.5192).It also has the best area under the receiver operator characteristic curve (ROC) (0.8375), which is far superior to the other four ML models. CONCLUSION Computer ML model analysis confirmed the importance of CACS in predicting the occurrence of CHD, especially the outstanding RF model, making it another advancement of the ML model in the field of medical analysis.
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Affiliation(s)
- Yue Huang
- Department of Anesthesiology, Hospital (T.C.M) Affiliated to Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - YingBo Ren
- Department of Anesthesiology, Hospital (T.C.M) Affiliated to Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Hai Yang
- Department of Anesthesiology, Hospital (T.C.M) Affiliated to Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - YiJie Ding
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, 324000, Quzhou, Zhejiang, China
| | - Yan Liu
- Department of Anesthesiology, Hospital (T.C.M) Affiliated to Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - YunChun Yang
- Department of Anesthesiology, Hospital (T.C.M) Affiliated to Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - AnQiong Mao
- Department of Anesthesiology, Hospital (T.C.M) Affiliated to Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Tan Yang
- Department of Cardiac and Vascular Surgery, Hospital (T.C.M) Affiliated to Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - YingZi Wang
- Southwest Medical University, Luzhou, 646099, Sichuan, China
| | - Feng Xiao
- Southwest Medical University, Luzhou, 646099, Sichuan, China
| | - QiZhou He
- Department of Radiology,Hospital (T.C.M) Affiliated to Southwest Medical University, Luzhou, 646000, Sichuan, China.
| | - Ying Zhang
- Department of Anesthesiology, Hospital (T.C.M) Affiliated to Southwest Medical University, Luzhou, 646000, Sichuan, China.
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12
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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: 8] [Impact Index Per Article: 4.0] [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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13
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Muscogiuri G, Guaricci AI, Soldato N, Cau R, Saba L, Siena P, Tarsitano MG, Giannetta E, Sala D, Sganzerla P, Gatti M, Faletti R, Senatieri A, Chierchia G, Pontone G, Marra P, Rabbat MG, Sironi S. Multimodality Imaging of Sudden Cardiac Death and Acute Complications in Acute Coronary Syndrome. J Clin Med 2022; 11:jcm11195663. [PMID: 36233531 PMCID: PMC9573273 DOI: 10.3390/jcm11195663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 09/07/2022] [Accepted: 09/22/2022] [Indexed: 11/23/2022] Open
Abstract
Sudden cardiac death (SCD) is a potentially fatal event usually caused by a cardiac arrhythmia, which is often the result of coronary artery disease (CAD). Up to 80% of patients suffering from SCD have concomitant CAD. Arrhythmic complications may occur in patients with acute coronary syndrome (ACS) before admission, during revascularization procedures, and in hospital intensive care monitoring. In addition, about 20% of patients who survive cardiac arrest develop a transmural myocardial infarction (MI). Prevention of ACS can be evaluated in selected patients using cardiac computed tomography angiography (CCTA), while diagnosis can be depicted using electrocardiography (ECG), and complications can be evaluated with cardiac magnetic resonance (CMR) and echocardiography. CCTA can evaluate plaque, burden of disease, stenosis, and adverse plaque characteristics, in patients with chest pain. ECG and echocardiography are the first-line tests for ACS and are affordable and useful for diagnosis. CMR can evaluate function and the presence of complications after ACS, such as development of ventricular thrombus and presence of myocardial tissue characterization abnormalities that can be the substrate of ventricular arrhythmias.
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Affiliation(s)
- Giuseppe Muscogiuri
- Department of Radiology, Istituto Auxologico Italiano IRCCS, San Luca Hospital, Piazzale Brescia 20, 20149 Milan, Italy
- School of Medicine, University of Milano-Bicocca, 20126 Milan, Italy
- Correspondence:
| | - Andrea Igoren Guaricci
- University Cardiology Unit, Department of Interdisciplinary Medicine, University of Bari, 70121 Bari, Italy
| | - Nicola Soldato
- University Cardiology Unit, Department of Interdisciplinary Medicine, University of Bari, 70121 Bari, Italy
| | - Riccardo Cau
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato, 09124 Cagliari, Italy
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato, 09124 Cagliari, Italy
| | - Paola Siena
- University Cardiology Unit, Department of Interdisciplinary Medicine, University of Bari, 70121 Bari, Italy
| | - Maria Grazia Tarsitano
- Department of Medical and Surgical Science, University Magna Grecia, 88100 Catanzaro, Italy
| | - Elisa Giannetta
- Department of Experimental Medicine, Sapienza University of Rome, Viale Regina Elena, 324, 00161 Rome, Italy
| | - Davide Sala
- Department of Cardiac, Neurological and Metabolic Sciences, San Luca Hospital, Istituto Auxologico Italiano IRCCS, 20149 Milan, Italy
| | - Paolo Sganzerla
- Department of Cardiac, Neurological and Metabolic Sciences, San Luca Hospital, Istituto Auxologico Italiano IRCCS, 20149 Milan, Italy
| | - Marco Gatti
- Radiology Unit, Department of Surgical Sciences, University of Turin, 10124 Turin, Italy
| | - Riccardo Faletti
- Radiology Unit, Department of Surgical Sciences, University of Turin, 10124 Turin, Italy
| | - Alberto Senatieri
- School of Medicine, University of Milano-Bicocca, 20126 Milan, Italy
| | | | | | - Paolo Marra
- School of Medicine, University of Milano-Bicocca, 20126 Milan, Italy
- Department of Radiology, ASST Papa Giovanni XXIII, 24127 Bergamo, Italy
| | - Mark G. Rabbat
- Division of Cardiology, Loyola University of Chicago, Chicago, IL 60611, USA
- Edward Hines Jr. VA Hospital, Hines, IL 60141, USA
| | - Sandro Sironi
- School of Medicine, University of Milano-Bicocca, 20126 Milan, Italy
- Department of Radiology, ASST Papa Giovanni XXIII, 24127 Bergamo, Italy
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14
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Lyu PF, Wang Y, Meng QX, Fan PM, Ma K, Xiao S, Cao XC, Lin GX, Dong SY. Mapping intellectual structures and research hotspots in the application of artificial intelligence in cancer: A bibliometric analysis. Front Oncol 2022; 12:955668. [PMID: 36212413 PMCID: PMC9535738 DOI: 10.3389/fonc.2022.955668] [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: 05/29/2022] [Accepted: 08/25/2022] [Indexed: 11/23/2022] Open
Abstract
Background Artificial intelligence (AI) is more and more widely used in cancer, which is of great help to doctors in diagnosis and treatment. This study aims to summarize the current research hotspots in the Application of Artificial Intelligence in Cancer (AAIC) and to assess the research trends in AAIC. Methods Scientific publications for AAIC-related research from 1 January 1998 to 1 July 2022 were obtained from the Web of Science database. The metrics analyses using bibliometrics software included publication, keyword, author, journal, institution, and country. In addition, the blustering analysis on the binary matrix was performed on hot keywords. Results The total number of papers in this study is 1592. The last decade of AAIC research has been divided into a slow development phase (2013-2018) and a rapid development phase (2019-2022). An international collaboration centered in the USA is dedicated to the development and application of AAIC. Li J is the most prolific writer in AAIC. Through clustering analysis and high-frequency keyword research, it has been shown that AI plays a significantly important role in the prediction, diagnosis, treatment and prognosis of cancer. Classification, diagnosis, carcinogenesis, risk, and validation are developing topics. Eight hotspot fields of AAIC were also identified. Conclusion AAIC can benefit cancer patients in diagnosing cancer, assessing the effectiveness of treatment, making a decision, predicting prognosis and saving costs. Future AAIC research may be dedicated to optimizing AI calculation tools, improving accuracy, and promoting AI.
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Affiliation(s)
- Peng-fei Lyu
- Department of Breast Surgery, The First Affiliated Hospital of Hainan Medical University, Haikou, China
- The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Yu Wang
- Department of Breast Surgery, The First Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Qing-Xiang Meng
- The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Ping-ming Fan
- Department of Breast Surgery, The First Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Ke Ma
- The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Sha Xiao
- International School of Public Health and One Health, Heinz Mehlhorn Academician Workstation, Hainan Medical University, Haikou, China
| | - Xun-chen Cao
- The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Guang-Xun Lin
- Department of Orthopedics, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- *Correspondence: Guang-Xun Lin, ; Si-yuan Dong,
| | - Si-yuan Dong
- Thoracic Department, The First Hospital of China Medical University, Shenyang, China
- *Correspondence: Guang-Xun Lin, ; Si-yuan Dong,
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15
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Muscogiuri G, Chiesa M, Baggiano A, Spadafora P, De Santis R, Guglielmo M, Scafuri S, Fusini L, Mushtaq S, Conte E, Annoni A, Formenti A, Mancini ME, Ricci F, Ariano FP, Spiritigliozzi L, Babbaro M, Mollace R, Maragna R, Giacari CM, Andreini D, Guaricci AI, Colombo GI, Rabbat MG, Pepi M, Sardanelli F, Pontone G. Diagnostic performance of deep learning algorithm for analysis of computed tomography myocardial perfusion. Eur J Nucl Med Mol Imaging 2022; 49:3119-3128. [PMID: 35194673 DOI: 10.1007/s00259-022-05732-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 02/12/2022] [Indexed: 12/30/2022]
Abstract
PURPOSE To evaluate the diagnostic accuracy of a deep learning (DL) algorithm predicting hemodynamically significant coronary artery disease (CAD) by using a rest dataset of myocardial computed tomography perfusion (CTP) as compared to invasive evaluation. METHODS One hundred and twelve consecutive symptomatic patients scheduled for clinically indicated invasive coronary angiography (ICA) underwent CCTA plus static stress CTP and ICA with invasive fractional flow reserve (FFR) for stenoses ranging between 30 and 80%. Subsequently, a DL algorithm for the prediction of significant CAD by using the rest dataset (CTP-DLrest) and stress dataset (CTP-DLstress) was developed. The diagnostic accuracy for identification of significant CAD using CCTA, CCTA + CTP stress, CCTA + CTP-DLrest, and CCTA + CTP-DLstress was measured and compared. The time of analysis for CTP stress, CTP-DLrest, and CTP-DLStress was recorded. RESULTS Patient-specific sensitivity, specificity, NPV, PPV, accuracy, and area under the curve (AUC) of CCTA alone and CCTA + CTPStress were 100%, 33%, 100%, 54%, 63%, 67% and 86%, 89%, 89%, 86%, 88%, 87%, respectively. Patient-specific sensitivity, specificity, NPV, PPV, accuracy, and AUC of CCTA + DLrest and CCTA + DLstress were 100%, 72%, 100%, 74%, 84%, 96% and 93%, 83%, 94%, 81%, 88%, 98%, respectively. All CCTA + CTP stress, CCTA + CTP-DLRest, and CCTA + CTP-DLStress significantly improved detection of hemodynamically significant CAD compared to CCTA alone (p < 0.01). Time of CTP-DL was significantly lower as compared to human analysis (39.2 ± 3.2 vs. 379.6 ± 68.0 s, p < 0.001). CONCLUSION Evaluation of myocardial ischemia using a DL approach on rest CTP datasets is feasible and accurate. This approach may be a useful gatekeeper prior to CTP stress..
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Affiliation(s)
| | - Mattia Chiesa
- Centro Cardiologico Monzino, IRCCS, Milan, Italy.,Department of Electronics, Information and Biomedical Engineering, Politecnico di Milano, 20133, Milan, Italy
| | | | - Pierino Spadafora
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
| | - Rossella De Santis
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
| | | | | | - Laura Fusini
- Centro Cardiologico Monzino, IRCCS, Milan, Italy
| | | | | | | | | | | | | | | | | | | | | | | | | | - Daniele Andreini
- Centro Cardiologico Monzino, IRCCS, Milan, Italy.,Department of Clinical Sciences and Community Health, Cardiovascular Section, University of Milan, Milan, Italy
| | - Andrea Igoren Guaricci
- Department of Emergency and Organ Transplantation, Institute of Cardiovascular Disease, University Hospital "Policlinico Consorziale" of Bari, Bari, Italy
| | | | - Mark G Rabbat
- Loyola University of Chicago, Chicago, IL, USA.,Edward Hines Jr. VA Hospital, Hines, IL, USA
| | - Mauro Pepi
- Centro Cardiologico Monzino, IRCCS, Milan, Italy
| | - Francesco Sardanelli
- Department of Electronics, Information and Biomedical Engineering, Politecnico di Milano, 20133, Milan, Italy.,Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Milan, Italy
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16
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D’Ascenzi F, Baggiano A, Cavigli L, Mandoli GE, Andreini D, Marallo C, Valente S, Focardi M, Cameli M, Pontone G. The role of cardiac computed tomography in sports cardiology: back to the future! Eur Heart J Cardiovasc Imaging 2022; 23:e481-e493. [DOI: 10.1093/ehjci/jeac069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 02/15/2022] [Accepted: 03/21/2022] [Indexed: 11/14/2022] Open
Abstract
Abstract
In recent years, the role of pre-participation evaluation (PPE) in the prevention of sudden cardiac death in competitive athletes has become evident. Most physicians routinely supplement assessment by resting electrocardiogram with imaging techniques, such as echocardiography. The primary goal of imaging in the clinical assessment of competitive athletes is to exclude cardiovascular conditions associated with adverse outcomes. Cardiac computed tomography is emerging as an important technique for stratifying cardiovascular risk and assessing coronary artery disease (CAD), particularly in master athletes. Conversely, in young athletes, this technique has the best non-invasive coronary artery resolution and provides valuable details on coronary artery anatomy. Recent technical developments have brought about a dramatic reduction in radiation exposure, a major drawback of this diagnostic method; nowadays cardiac computed tomography may be performed at a dose of barely one millisievert. The present review provides a practical guide for the use of cardiac computed tomography in the PPE of competitive athletes, with a specific focus on its value for detecting congenital coronary anomalies and CAD in young and master athletes, respectively.
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Affiliation(s)
- Flavio D’Ascenzi
- Department of Medical Biotechnologies, Division of Cardiology, University of Siena, V.le M. Bracci 16, 53100 Siena, Italy
| | - Andrea Baggiano
- Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, Milan, Italy
- Department of Clinical Sciences and Community Health, Cardiovascular Section, University of Milan, Milan, Italy
| | - Luna Cavigli
- Department of Medical Biotechnologies, Division of Cardiology, University of Siena, V.le M. Bracci 16, 53100 Siena, Italy
| | - Giulia Elena Mandoli
- Department of Medical Biotechnologies, Division of Cardiology, University of Siena, V.le M. Bracci 16, 53100 Siena, Italy
| | - Daniele Andreini
- Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, Milan, Italy
- Department of Clinical Sciences and Community Health, Cardiovascular Section, University of Milan, Milan, Italy
| | - Carmine Marallo
- Department of Medical Biotechnologies, Division of Cardiology, University of Siena, V.le M. Bracci 16, 53100 Siena, Italy
| | - Serafina Valente
- Department of Medical Biotechnologies, Division of Cardiology, University of Siena, V.le M. Bracci 16, 53100 Siena, Italy
| | - Marta Focardi
- Department of Medical Biotechnologies, Division of Cardiology, University of Siena, V.le M. Bracci 16, 53100 Siena, Italy
| | - Matteo Cameli
- Department of Medical Biotechnologies, Division of Cardiology, University of Siena, V.le M. Bracci 16, 53100 Siena, Italy
| | - Gianluca Pontone
- Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, Milan, Italy
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17
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Normalizing Flows for Out-of-Distribution Detection: Application to Coronary Artery Segmentation. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12083839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Coronary computed tomography angiography (CCTA) is an effective imaging modality, increasingly accepted as a first-line test to diagnose coronary artery disease (CAD). The accurate segmentation of the coronary artery lumen on CCTA is important for the anatomical, morphological, and non-invasive functional assessment of stenoses. Hence, semi-automated approaches are currently still being employed. The processing time for a semi-automated lumen segmentation can be reduced by pre-selecting vessel locations likely to require manual inspection and by submitting only those for review to the radiologist. Detection of faulty lumen segmentation masks can be formulated as an Out-of-Distribution (OoD) detection problem. Two Normalizing Flows architectures are investigated and benchmarked herein: a Glow-like baseline, and a proposed one employing a novel coupling layer. Synthetic mask perturbations are used for evaluating and fine-tuning the learnt probability densities. Expert annotations on a separate test-set are employed to measure detection performance relative to inter-user variability. Regular coupling-layers tend to focus more on local pixel correlations and to disregard semantic content. Experiments and analyses show that, in contrast, the proposed architecture is capable of capturing semantic content and is therefore better suited for OoD detection of faulty lumen segmentations. When compared against expert consensus, the proposed model achieves an accuracy of 78.6% and a sensitivity of 76%, close to the inter-user mean of 80.9% and 79%, respectively, while the baseline model achieves an accuracy of 64.3% and a sensitivity of 48%.
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18
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Muscogiuri G, Guglielmo M, Serra A, Gatti M, Volpato V, Schoepf UJ, Saba L, Cau R, Faletti R, McGill LJ, De Cecco CN, Pontone G, Dell’Aversana S, Sironi S. Multimodality Imaging in Ischemic Chronic Cardiomyopathy. J Imaging 2022; 8:jimaging8020035. [PMID: 35200737 PMCID: PMC8877428 DOI: 10.3390/jimaging8020035] [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: 11/28/2021] [Revised: 01/23/2022] [Accepted: 01/27/2022] [Indexed: 02/01/2023] Open
Abstract
Ischemic chronic cardiomyopathy (ICC) is still one of the most common cardiac diseases leading to the development of myocardial ischemia, infarction, or heart failure. The application of several imaging modalities can provide information regarding coronary anatomy, coronary artery disease, myocardial ischemia and tissue characterization. In particular, coronary computed tomography angiography (CCTA) can provide information regarding coronary plaque stenosis, its composition, and the possible evaluation of myocardial ischemia using fractional flow reserve CT or CT perfusion. Cardiac magnetic resonance (CMR) can be used to evaluate cardiac function as well as the presence of ischemia. In addition, CMR can be used to characterize the myocardial tissue of hibernated or infarcted myocardium. Echocardiography is the most widely used technique to achieve information regarding function and myocardial wall motion abnormalities during myocardial ischemia. Nuclear medicine can be used to evaluate perfusion in both qualitative and quantitative assessment. In this review we aim to provide an overview regarding the different noninvasive imaging techniques for the evaluation of ICC, providing information ranging from the anatomical assessment of coronary artery arteries to the assessment of ischemic myocardium and myocardial infarction. In particular this review is going to show the different noninvasive approaches based on the specific clinical history of patients with ICC.
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Affiliation(s)
- Giuseppe Muscogiuri
- Department of Radiology, Istituto Auxologico Italiano IRCCS, San Luca Hospital, University Milano Bicocca, 20149 Milan, Italy
- Correspondence: ; Tel.: +39-329-404-9840
| | - Marco Guglielmo
- Department of Cardiology, Division of Heart and Lungs, Utrecht University, Utrecht University Medical Center, 3584 Utrecht, The Netherlands;
| | - Alessandra Serra
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato, 09042 Cagliari, Italy; (A.S.); (L.S.); (R.C.)
| | - Marco Gatti
- Radiology Unit, Department of Surgical Sciences, University of Turin, 10124 Turin, Italy; (M.G.); (R.F.)
| | - Valentina Volpato
- Department of Cardiac, Neurological and Metabolic Sciences, Istituto Auxologico Italiano IRCCS, San Luca Hospital, University Milano Bicocca, 20149 Milan, Italy;
| | - Uwe Joseph Schoepf
- Department of Radiology and Radiological Science, MUSC Ashley River Tower, Medical University of South Carolina, 25 Courtenay Dr, Charleston, SC 29425, USA; (U.J.S.); (L.J.M.)
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato, 09042 Cagliari, Italy; (A.S.); (L.S.); (R.C.)
| | - Riccardo Cau
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato, 09042 Cagliari, Italy; (A.S.); (L.S.); (R.C.)
| | - Riccardo Faletti
- Radiology Unit, Department of Surgical Sciences, University of Turin, 10124 Turin, Italy; (M.G.); (R.F.)
| | - Liam J. McGill
- Department of Radiology and Radiological Science, MUSC Ashley River Tower, Medical University of South Carolina, 25 Courtenay Dr, Charleston, SC 29425, USA; (U.J.S.); (L.J.M.)
| | - Carlo Nicola De Cecco
- Division of Cardiothoracic Imaging, Nuclear Medicine and Molecular Imaging, Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA 30322, USA;
| | | | - Serena Dell’Aversana
- Department of Radiology, Ospedale S. Maria Delle Grazie—ASL Napoli 2 Nord, 80078 Pozzuoli, Italy;
| | - Sandro Sironi
- School of Medicine and Post Graduate School of Diagnostic Radiology, University of Milano-Bicocca, 20126 Milan, Italy;
- Department of Radiology, ASST Papa Giovanni XXIII, 24127 Bergamo, Italy
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19
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Artificial Intelligence Advances in the World of Cardiovascular Imaging. Healthcare (Basel) 2022; 10:healthcare10010154. [PMID: 35052317 PMCID: PMC8776229 DOI: 10.3390/healthcare10010154] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 01/11/2022] [Accepted: 01/11/2022] [Indexed: 02/04/2023] Open
Abstract
The tremendous advances in digital information and communication technology have entered everything from our daily lives to the most intricate aspects of medical and surgical care. These advances are seen in electronic and mobile health and allow many new applications to further improve and make the diagnoses of patient diseases and conditions more precise. In the area of digital radiology with respect to diagnostics, the use of advanced imaging tools and techniques is now at the center of evaluation and treatment. Digital acquisition and analysis are central to diagnostic capabilities, especially in the field of cardiovascular imaging. Furthermore, the introduction of artificial intelligence (AI) into the world of digital cardiovascular imaging greatly broadens the capabilities of the field both with respect to advancement as well as with respect to complete and accurate diagnosis of cardiovascular conditions. The application of AI in recognition, diagnostics, protocol automation, and quality control for the analysis of cardiovascular imaging modalities such as echocardiography, nuclear cardiac imaging, cardiovascular computed tomography, cardiovascular magnetic resonance imaging, and other imaging, is a major advance that is improving rapidly and continuously. We document the innovations in the field of cardiovascular imaging that have been brought about by the acceptance and implementation of AI in relation to healthcare professionals and patients in the cardiovascular field.
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20
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Boerhout CKM, Feenstra RGT, Somsen GA, Appelman Y, Ong P, Beijk MAM, Hofstra L, van de Hoef TP, Piek JJ. Coronary computed tomographic angiography as gatekeeper for new-onset stable angina. Neth Heart J 2021; 29:551-556. [PMID: 34676522 PMCID: PMC8556428 DOI: 10.1007/s12471-021-01639-7] [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] [Accepted: 09/01/2021] [Indexed: 01/16/2023] Open
Abstract
Patients with new-onset stable angina constitute a substantial part of the population seen by cardiologists. Currently, the diagnostic workup of these patients depends on the pre-test probability of having obstructive coronary artery disease. It consists of either functional testing for myocardial ischaemia or anatomical testing by using coronary computed tomographic angiography (CCTA) or invasive coronary angiography. In case the pre-test probability is > 5%, the current guidelines for the management of chronic coronary syndromes do not state a clear preference for one of the noninvasive techniques. However, based on the recently published cost-effectiveness analysis of the PROMISE trial and considering the diagnostic yield in patients with angina and nonobstructive coronary artery disease, we argue a more prominent role for CCTA as a gatekeeper for patients with new-onset stable angina.
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Affiliation(s)
- C K M Boerhout
- Department of Cardiology, Heart Centre, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - R G T Feenstra
- Department of Cardiology, Heart Centre, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - G A Somsen
- Cardiology Centres of the Netherlands, Amsterdam, The Netherlands
| | - Y Appelman
- Department of Cardiology, Heart Centre, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - P Ong
- Department of Cardiology, Robert-Bosch-Krankenhaus, Stuttgart, Germany
| | - M A M Beijk
- Department of Cardiology, Heart Centre, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - L Hofstra
- Department of Cardiology, Heart Centre, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - T P van de Hoef
- Department of Cardiology, Heart Centre, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - J J Piek
- Department of Cardiology, Heart Centre, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, Amsterdam, The Netherlands.
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21
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Maragna R, Giacari CM, Guglielmo M, Baggiano A, Fusini L, Guaricci AI, Rossi A, Rabbat M, Pontone G. Artificial Intelligence Based Multimodality Imaging: A New Frontier in Coronary Artery Disease Management. Front Cardiovasc Med 2021; 8:736223. [PMID: 34631834 PMCID: PMC8493089 DOI: 10.3389/fcvm.2021.736223] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 08/25/2021] [Indexed: 12/14/2022] Open
Abstract
Coronary artery disease (CAD) represents one of the most important causes of death around the world. Multimodality imaging plays a fundamental role in both diagnosis and risk stratification of acute and chronic CAD. For example, the role of Coronary Computed Tomography Angiography (CCTA) has become increasingly important to rule out CAD according to the latest guidelines. These changes and others will likely increase the request for appropriate imaging tests in the future. In this setting, artificial intelligence (AI) will play a pivotal role in echocardiography, CCTA, cardiac magnetic resonance and nuclear imaging, making multimodality imaging more efficient and reliable for clinicians, as well as more sustainable for healthcare systems. Furthermore, AI can assist clinicians in identifying early predictors of adverse outcome that human eyes cannot see in the fog of “big data.” AI algorithms applied to multimodality imaging will play a fundamental role in the management of patients with suspected or established CAD. This study aims to provide a comprehensive overview of current and future AI applications to the field of multimodality imaging of ischemic heart disease.
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Affiliation(s)
- Riccardo Maragna
- Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Carlo Maria Giacari
- Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Marco Guglielmo
- Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Andrea Baggiano
- Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy.,Department of Clinical Sciences and Community Health, Cardiovascular Section, University of Milan, Milan, Italy
| | - Laura Fusini
- Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Andrea Igoren Guaricci
- Department of Emergency and Organ Transplantation, Institute of Cardiovascular Disease, University Hospital Policlinico of Bari, Bari, Italy
| | - Alexia Rossi
- Department of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland.,Center for Molecular Cardiology, University Hospital Zurich, Zurich, Switzerland
| | - Mark Rabbat
- Department of Medicine and Radiology, Division of Cardiology, Loyola University of Chicago, Chicago, IL, United States.,Department of Medicine, Division of Cardiology, Edward Hines Jr. VA Hospital, Hines, IL, United States
| | - Gianluca Pontone
- Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
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22
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Benjamin MM, Rabbat MG. Machine learning-based advances in coronary computed tomography angiography. Quant Imaging Med Surg 2021; 11:2208-2213. [PMID: 34079695 DOI: 10.21037/qims-21-99] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Mina M Benjamin
- Department of Cardiology, Loyola University Medical Center, Maywood, Illinois, USA
| | - Mark G Rabbat
- Department of Cardiology, Loyola University Medical Center, Maywood, Illinois, USA
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23
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Machine Learning Quantitation of Cardiovascular and Cerebrovascular Disease: A Systematic Review of Clinical Applications. Diagnostics (Basel) 2021; 11:diagnostics11030551. [PMID: 33808677 PMCID: PMC8003459 DOI: 10.3390/diagnostics11030551] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 03/12/2021] [Accepted: 03/16/2021] [Indexed: 01/10/2023] Open
Abstract
Research into machine learning (ML) for clinical vascular analysis, such as those useful for stroke and coronary artery disease, varies greatly between imaging modalities and vascular regions. Limited accessibility to large diverse patient imaging datasets, as well as a lack of transparency in specific methods, are obstacles to further development. This paper reviews the current status of quantitative vascular ML, identifying advantages and disadvantages common to all imaging modalities. Literature from the past 8 years was systematically collected from MEDLINE® and Scopus database searches in January 2021. Papers satisfying all search criteria, including a minimum of 50 patients, were further analysed and extracted of relevant data, for a total of 47 publications. Current ML image segmentation, disease risk prediction, and pathology quantitation methods have shown sensitivities and specificities over 70%, compared to expert manual analysis or invasive quantitation. Despite this, inconsistencies in methodology and the reporting of results have prevented inter-model comparison, impeding the identification of approaches with the greatest potential. The clinical potential of this technology has been well demonstrated in Computed Tomography of coronary artery disease, but remains practically limited in other modalities and body regions, particularly due to a lack of routine invasive reference measurements and patient datasets.
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