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Bors S, Abler D, Dietz M, Andrearczyk V, Fageot J, Nicod-Lalonde M, Schaefer N, DeKemp R, Kamani CH, Prior JO, Depeursinge A. Comparing various AI approaches to traditional quantitative assessment of the myocardial perfusion in [ 82Rb] PET for MACE prediction. Sci Rep 2024; 14:9644. [PMID: 38671059 PMCID: PMC11053111 DOI: 10.1038/s41598-024-60095-6] [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: 08/30/2023] [Accepted: 04/18/2024] [Indexed: 04/28/2024] Open
Abstract
Assessing the individual risk of Major Adverse Cardiac Events (MACE) is of major importance as cardiovascular diseases remain the leading cause of death worldwide. Quantitative Myocardial Perfusion Imaging (MPI) parameters such as stress Myocardial Blood Flow (sMBF) or Myocardial Flow Reserve (MFR) constitutes the gold standard for prognosis assessment. We propose a systematic investigation of the value of Artificial Intelligence (AI) to leverage [82 Rb] Silicon PhotoMultiplier (SiPM) PET MPI for MACE prediction. We establish a general pipeline for AI model validation to assess and compare the performance of global (i.e. average of the entire MPI signal), regional (17 segments), radiomics and Convolutional Neural Network (CNN) models leveraging various MPI signals on a dataset of 234 patients. Results showed that all regional AI models significantly outperformed the global model ( p < 0.001 ), where the best AUC of 73.9% (CI 72.5-75.3) was obtained with a CNN model. A regional AI model based on MBF averages from 17 segments fed to a Logistic Regression (LR) constituted an excellent trade-off between model simplicity and performance, achieving an AUC of 73.4% (CI 72.3-74.7). A radiomics model based on intensity features revealed that the global average was the least important feature when compared to other aggregations of the MPI signal over the myocardium. We conclude that AI models can allow better personalized prognosis assessment for MACE.
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Affiliation(s)
- Sacha Bors
- Nuclear Medicine and Molecular Imaging Department, Lausanne University Hospital, Lausanne, Switzerland
- Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland
| | - Daniel Abler
- Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
- Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland
| | - Matthieu Dietz
- INSERM U1060, CarMeN laboratory, University of Lyon, Lyon, France
| | - Vincent Andrearczyk
- Nuclear Medicine and Molecular Imaging Department, Lausanne University Hospital, Lausanne, Switzerland
- Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland
| | - Julien Fageot
- AudioVisual Communications Laboratory (LCAV), EPFL, Lausanne, Switzerland
| | - Marie Nicod-Lalonde
- Nuclear Medicine and Molecular Imaging Department, Lausanne University Hospital, Lausanne, Switzerland
- University of Lausanne, Lausanne, Switzerland
| | - Niklaus Schaefer
- Nuclear Medicine and Molecular Imaging Department, Lausanne University Hospital, Lausanne, Switzerland
- University of Lausanne, Lausanne, Switzerland
| | - Robert DeKemp
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, ON, Canada
| | - Christel H Kamani
- Department of Cardiology, Lausanne University Hospital, Lausanne, Switzerland
| | - John O Prior
- Nuclear Medicine and Molecular Imaging Department, Lausanne University Hospital, Lausanne, Switzerland.
- University of Lausanne, Lausanne, Switzerland.
| | - Adrien Depeursinge
- Nuclear Medicine and Molecular Imaging Department, Lausanne University Hospital, Lausanne, Switzerland
- Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland
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Juarez-Orozco LE, Niemi M, Yeung MW, Benjamins JW, Maaniitty T, Teuho J, Saraste A, Knuuti J, van der Harst P, Klén R. Hybridizing machine learning in survival analysis of cardiac PET/CT imaging. J Nucl Cardiol 2023; 30:2750-2759. [PMID: 37656345 PMCID: PMC10682215 DOI: 10.1007/s12350-023-03359-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 07/07/2023] [Indexed: 09/02/2023]
Abstract
BACKGROUND Machine Learning (ML) allows integration of the numerous variables delivered by cardiac PET/CT, while traditional survival analysis can provide explainable prognostic estimates from a restricted number of input variables. We implemented a hybrid ML-and-survival analysis of multimodal PET/CT data to identify patients who developed myocardial infarction (MI) or death in long-term follow up. METHODS Data from 739 intermediate risk patients who underwent coronary CT and selectively stress 15O-water-PET perfusion were analyzed for the occurrence of MI and all-cause mortality. Images were evaluated segmentally for atherosclerosis and absolute myocardial perfusion through 75 variables that were integrated through ML into an ML-CCTA and an ML-PET score. These scores were then modeled along with clinical variables through Cox regression. This hybridized model was compared against an expert interpretation-based and a calcium score-based model. RESULTS Compared with expert- and calcium score-based models, the hybridized ML-survival model showed the highest performance (CI .81 vs .71 and .64). The strongest predictor for outcomes was the ML-CCTA score. CONCLUSION Prognostic modeling of PET/CT data for the long-term occurrence of adverse events may be improved through ML imaging score integration and subsequent traditional survival analysis with clinical variables. This hybridization of methods offers an alternative to traditional survival modeling of conventional expert image scoring and interpretation.
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Affiliation(s)
- Luis Eduardo Juarez-Orozco
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
- Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20520, Turku, Finland.
| | - Mikael Niemi
- Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20520, Turku, Finland
| | - Ming Wai Yeung
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Jan Walter Benjamins
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Teemu Maaniitty
- Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20520, Turku, Finland
| | - Jarmo Teuho
- Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20520, Turku, Finland
| | - Antti Saraste
- Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20520, Turku, Finland
- Heart Center, Turku University Hospital, Turku, Finland
| | - Juhani Knuuti
- Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20520, Turku, Finland
| | - Pim van der Harst
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Riku Klén
- Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20520, Turku, Finland
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de A Fernandes F, Larsen K, He Z, Nascimento E, Peix A, Sha Q, Paez D, Garcia EV, Zhou W, Mesquita CT. A machine learning method integrating ECG and gated SPECT for cardiac resynchronization therapy decision support. Eur J Nucl Med Mol Imaging 2023; 50:3022-3033. [PMID: 37195444 PMCID: PMC10959568 DOI: 10.1007/s00259-023-06259-4] [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: 01/08/2023] [Accepted: 05/01/2023] [Indexed: 05/18/2023]
Abstract
PURPOSE Cardiac resynchronization therapy (CRT) has been established as an important therapy for heart failure. Mechanical dyssynchrony has the potential to predict responders to CRT. The aim of this study was to report the development and the validation of machine learning models which integrate ECG, gated SPECT MPI (GMPS), and clinical variables to predict patients' response to CRT. METHODS This analysis included 153 patients who met criteria for CRT from a prospective cohort study. The variables were used to model predictive methods for CRT. Patients were classified as "responders" for an increase of LVEF ≥ 5% at follow-up. In a second analysis, patients were classified as "super-responders" for an increase of LVEF ≥ 15%. For ML, variable selection was applied, and Prediction Analysis of Microarrays (PAM) approach was used to model response while Naïve Bayes (NB) was used to model super-response. These ML models were compared to models obtained with guideline variables. RESULTS PAM had AUC of 0.80 against 0.72 of partial least squares-discriminant analysis with guideline variables (p = 0.52). The sensitivity (0.86) and specificity (0.75) were better than for guideline alone, sensitivity (0.75) and specificity (0.24). Neural network with guideline variables was better than NB (AUC = 0.93 vs. 0.87) however without statistical significance (p = 0.48). Its sensitivity and specificity (1.0 and 0.75, respectively) were better than guideline alone (0.78 and 0.25, respectively). CONCLUSIONS Compared to guideline criteria, ML methods trended toward improved CRT response and super-response prediction. GMPS was central in the acquisition of most parameters. Further studies are needed to validate the models.
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Affiliation(s)
- Fernando de A Fernandes
- Nuclear Medicine Department, Hospital Universitario Antonio Pedro-EBSERH-UFF, 303 Marquês de Parana Street, Niteroi, Rio de Janeiro, 24033-900, Brazil.
| | - Kristoffer Larsen
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA
| | - Zhuo He
- Department of Applied Computing, Michigan Technological University, 1400 Townsend Dr, Houghton, MI, 49931, USA
| | - Erivelton Nascimento
- Cardiology Department, Hospital Universitario Antonio Pedro-EBSERH-UFF, Niteroi, Brazil
| | - Amalia Peix
- Nuclear Medicine Department, Institute of Cardiology, La Habana, Cuba
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA
| | - Diana Paez
- Nuclear Medicine and Diagnostic Imaging Section, Division of Human Health, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna, Austria
| | - Ernest V Garcia
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA
| | - Weihua Zhou
- Department of Applied Computing, Michigan Technological University, 1400 Townsend Dr, Houghton, MI, 49931, USA.
- Center for Biocomputing and Digital Health, Institute of Computing and Cybersystems, and Health Research Institute, Michigan Technological University, Houghton, MI, USA.
| | - Claudio T Mesquita
- Nuclear Medicine Department, Hospital Universitario Antonio Pedro-EBSERH-UFF, 303 Marquês de Parana Street, Niteroi, Rio de Janeiro, 24033-900, Brazil
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Garcia EV, Piccinelli M. Preparing for the Artificial Intelligence Revolution in Nuclear Cardiology. Nucl Med Mol Imaging 2023; 57:51-60. [PMID: 36998588 PMCID: PMC10043081 DOI: 10.1007/s13139-021-00733-3] [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: 12/07/2021] [Revised: 12/18/2021] [Accepted: 12/19/2021] [Indexed: 10/19/2022] Open
Abstract
A major opportunity in nuclear cardiology is the many significant artificial intelligence (AI) applications that have recently been reported. These developments include using deep learning (DL) for reducing the needed injected dose and acquisition time in perfusion acquisitions also due to DL improvements in image reconstruction and filtering, SPECT attenuation correction using DL without need for transmission images, DL and machine learning (ML) use for feature extraction to define myocardial left ventricular (LV) borders for functional measurements and improved detection of the LV valve plane and AI, ML, and DL implementations for MPI diagnosis, prognosis, and structured reporting. Although some have, most of these applications have yet to make it to widespread commercial distribution due to the recency of their developments, most reported in 2020. We must be prepared both technically and socio-economically to fully benefit from these and a tsunami of other AI applications that are coming.
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Affiliation(s)
- Ernest V. Garcia
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 101 Woodruff Circle, Room 1203, GA 30322 Atlanta, USA
| | - Marina Piccinelli
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 101 Woodruff Circle, Room 1203, GA 30322 Atlanta, USA
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5
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Miller RJH, Rozanski A, Slomka PJ, Han D, Gransar H, Hayes SW, Friedman JD, Thomson LEJ, Berman DS. Development and validation of ischemia risk scores. J Nucl Cardiol 2023; 30:324-334. [PMID: 35484468 DOI: 10.1007/s12350-022-02976-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 03/27/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND The likelihood of ischemia on myocardial perfusion imaging is central to physician decisions regarding test selection, but dedicated risk scores are lacking. We derived and validated two novel ischemia risk scores to support physician decision making. METHODS Risk scores were derived using 15,186 patients and validated with 2,995 patients from a different center. Logistic regression was used to assess associations with ischemia to derive point-based and calculated ischemia scores. Predictive performance for ischemia was assessed using area under the receiver operating characteristic curve (AUC) and compared with the CAD consortium basic and clinical models. RESULTS During derivation, the calculated ischemia risk score (0.801) had higher AUC compared to the point-based score (0.786, p < 0.001). During validation, the calculated ischemia score (0.716, 95% CI 0.684- 0.748) had higher AUC compared to the point-based ischemia score (0.699, 95% CI 0.666- 0.732, p = 0.016) and the clinical CAD model (AUC 0.667, 95% CI 0.633- 0.701, p = 0.002). Calibration for both ischemia scores was good in both populations (Brier score < 0.100). CONCLUSIONS We developed two novel risk scores for predicting probability of ischemia on MPI which demonstrated high accuracy during model derivation and in external testing. These scores could support physician decisions regarding diagnostic testing strategies.
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Affiliation(s)
- Robert J H Miller
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Libin Cardiovascular Institute of Alberta, University of Calgary, Calgary, AB, Canada
| | - Alan Rozanski
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Division of Cardiology and Department of Medicine, Mount Sinai Morningside Hospital, Mount Sinai Heart and the Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Division of Cardiac Sciences, Mount Sinai Morningside Hospital, Mount Sinai Heart and the Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Piotr J Slomka
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Donghee Han
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Heidi Gransar
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Sean W Hayes
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - John D Friedman
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Louise E J Thomson
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Daniel S Berman
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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Hijazi W, Miller RJH. Developing a framework for evaluating and comparing risk models. J Nucl Cardiol 2023; 30:59-61. [PMID: 36575282 DOI: 10.1007/s12350-022-03036-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 06/06/2022] [Indexed: 12/28/2022]
Affiliation(s)
- Waseem Hijazi
- Libin Cardiovascular Institute and Department of Cardiac Sciences, University of Calgary, GAA08, 3230 Hospital Drive NW, Calgary, AB, T2N 2T9, Canada
| | - Robert J H Miller
- Libin Cardiovascular Institute and Department of Cardiac Sciences, University of Calgary, GAA08, 3230 Hospital Drive NW, Calgary, AB, T2N 2T9, Canada.
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McCubrey RO, Mason SM, Le VT, Bride DL, Horne BD, Meredith KG, Sekaran NK, Anderson JL, Knowlton KU, Min DB, Knight S. A highly predictive cardiac positron emission tomography (PET) risk score for 90-day and one-year major adverse cardiac events and revascularization. J Nucl Cardiol 2023; 30:46-58. [PMID: 36536088 PMCID: PMC10035554 DOI: 10.1007/s12350-022-03028-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 05/18/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND With the increase in cardiac PET/CT availability and utilization, the development of a PET/CT-based major adverse cardiovascular events, including death, myocardial infarction (MI), and revascularization (MACE-Revasc) risk assessment score is needed. Here we develop a highly predictive PET/CT-based risk score for 90-day and one-year MACE-Revasc. METHODS AND RESULTS 11,552 patients had a PET/CT from 2015 to 2017 and were studied for the training and development set. PET/CT from 2018 was used to validate the derived scores (n = 5049). Patients were on average 65 years old, half were male, and a quarter had a prior MI or revascularization. Baseline characteristics and PET/CT results were used to derive the MACE-Revasc risk models, resulting in models with 5 and 8 weighted factors. The PET/CT 90-day MACE-Revasc risk score trended toward outperforming ischemic burden alone [P = .07 with an area under the curve (AUC) 0.85 vs 0.83]. The PET/CT one-year MACE-Revasc score was better than the use of ischemic burden alone (P < .0001, AUC 0.80 vs 0.76). Both PET/CT MACE-Revasc risk scores outperformed risk prediction by cardiologists. CONCLUSION The derived PET/CT 90-day and one-year MACE-Revasc risk scores were highly predictive and outperformed ischemic burden and cardiologist assessment. These scores are easy to calculate, lending to straightforward clinical implementation and should be further tested for clinical usefulness.
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Affiliation(s)
- Raymond O McCubrey
- Intermountain Medical Center Heart Institute, Intermountain Healthcare, 5121 Cottonwood St Bldg. 1 Floor 4, Murray, UT, 84107, USA
| | - Steve M Mason
- Intermountain Medical Center Heart Institute, Intermountain Healthcare, 5121 Cottonwood St Bldg. 1 Floor 4, Murray, UT, 84107, USA
| | - Viet T Le
- Intermountain Medical Center Heart Institute, Intermountain Healthcare, 5121 Cottonwood St Bldg. 1 Floor 4, Murray, UT, 84107, USA
| | - Daniel L Bride
- Intermountain Medical Center Heart Institute, Intermountain Healthcare, 5121 Cottonwood St Bldg. 1 Floor 4, Murray, UT, 84107, USA
| | - Benjamin D Horne
- Intermountain Medical Center Heart Institute, Intermountain Healthcare, 5121 Cottonwood St Bldg. 1 Floor 4, Murray, UT, 84107, USA
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Kent G Meredith
- Intermountain Medical Center Heart Institute, Intermountain Healthcare, 5121 Cottonwood St Bldg. 1 Floor 4, Murray, UT, 84107, USA
| | - Nishant K Sekaran
- Intermountain Medical Center Heart Institute, Intermountain Healthcare, 5121 Cottonwood St Bldg. 1 Floor 4, Murray, UT, 84107, USA
| | - Jeffrey L Anderson
- Intermountain Medical Center Heart Institute, Intermountain Healthcare, 5121 Cottonwood St Bldg. 1 Floor 4, Murray, UT, 84107, USA
- Department of Internal Medicine, School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Kirk U Knowlton
- Intermountain Medical Center Heart Institute, Intermountain Healthcare, 5121 Cottonwood St Bldg. 1 Floor 4, Murray, UT, 84107, USA
| | - David B Min
- Intermountain Medical Center Heart Institute, Intermountain Healthcare, 5121 Cottonwood St Bldg. 1 Floor 4, Murray, UT, 84107, USA
| | - Stacey Knight
- Intermountain Medical Center Heart Institute, Intermountain Healthcare, 5121 Cottonwood St Bldg. 1 Floor 4, Murray, UT, 84107, USA.
- Department of Internal Medicine, School of Medicine, University of Utah, Salt Lake City, UT, USA.
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Ko CL, Lin SS, Huang CW, Chang YH, Ko KY, Cheng MF, Wang SY, Chen CM, Wu YW. Polar map-free 3D deep learning algorithm to predict obstructive coronary artery disease with myocardial perfusion CZT-SPECT. Eur J Nucl Med Mol Imaging 2023; 50:376-386. [PMID: 36102963 DOI: 10.1007/s00259-022-05953-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 08/23/2022] [Indexed: 01/10/2023]
Abstract
PURPOSE Deep learning (DL) models have been shown to outperform total perfusion deficit (TPD) quantification in predicting obstructive coronary artery disease (CAD) from myocardial perfusion imaging (MPI). However, previously published methods have depended on polar maps, required manual correction, and normal database. In this study, we propose a polar map-free 3D DL algorithm to predict obstructive disease. METHODS We included 1861 subjects who underwent MPI using cadmium-zinc-telluride camera and subsequent coronary angiography. The subjects were divided into parameterization and external validation groups. We implemented a fully automatic algorithm to segment myocardium, perform registration, and apply normalization. We further flattened the image based on spherical coordinate system transformation. The proposed model consisted of a component to predict patent arteries and a component to predict disease in each vessel. The model was cross-validated in the parameterization group, and then further tested using the external validation group. The performance was assessed by area under receiver operating characteristic curves (AUCs) and compared with TPD. RESULTS Our algorithm preprocessed all images accurately as confirmed by visual inspection. In patient-based analysis, the AUC of the proposed model was significantly higher than that for stress-TPD (0.84 vs 0.76, p < 0.01). In vessel-based analysis, the proposed model also outperformed regional stress-TPD (AUC = 0.80 vs 0.72, p < 0.01). The addition of quantitative images did not improve the performance. CONCLUSIONS Our proposed polar map-free 3D DL algorithm to predict obstructive CAD from MPI outperformed TPD and did not require manual correction or a normal database.
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Affiliation(s)
- Chi-Lun Ko
- Department of Biomedical Engineering, National Taiwan University, Taipei, Taiwan
- Department of Nuclear Medicine, National Taiwan University Hospital, Taipei, Taiwan
- College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Shau-Syuan Lin
- Department of Biomedical Engineering, National Taiwan University, Taipei, Taiwan
| | - Cheng-Wen Huang
- Department of Biomedical Engineering, National Taiwan University, Taipei, Taiwan
| | - Yu-Hui Chang
- Department of Biomedical Engineering, National Taiwan University, Taipei, Taiwan
| | - Kuan-Yin Ko
- Department of Nuclear Medicine, National Taiwan University Cancer Center, Taipei, Taiwan
| | - Mei-Fang Cheng
- Department of Nuclear Medicine, National Taiwan University Hospital, Taipei, Taiwan
- College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Shan-Ying Wang
- Department of Nuclear Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan
| | - Chung-Ming Chen
- Department of Biomedical Engineering, National Taiwan University, Taipei, Taiwan
| | - Yen-Wen Wu
- Department of Nuclear Medicine, National Taiwan University Hospital, Taipei, Taiwan.
- College of Medicine, National Taiwan University, Taipei, Taiwan.
- Department of Nuclear Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan.
- Division of Cardiology, Cardiovascular Medical Center, Far Eastern Memorial Hospital, No. 21, Sec. 2, Nanya S. Rd., Banciao Dist., New Taipei City, 220, Taiwan.
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
- Graduate Institute of Medicine, Yuan Ze University, Taoyuan, Taiwan.
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Miller RJH, Hauser MT, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Huang C, Liang JX, Han D, Dey D, Berman DS, Slomka PJ. Machine learning to predict abnormal myocardial perfusion from pre-test features. J Nucl Cardiol 2022; 29:2393-2403. [PMID: 35672567 PMCID: PMC9588501 DOI: 10.1007/s12350-022-03012-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/22/2022] [Accepted: 04/22/2022] [Indexed: 11/24/2022]
Abstract
BACKGROUND Accurately predicting which patients will have abnormal perfusion on MPI based on pre-test clinical information may help physicians make test selection decisions. We developed and validated a machine learning (ML) model for predicting abnormal perfusion using pre-test features. METHODS We included consecutive patients who underwent SPECT MPI, with 20,418 patients from a multi-center (5 sites) international registry in the training population and 9019 patients (from 2 separate sites) in the external testing population. The ML (extreme gradient boosting) model utilized 30 pre-test features to predict the presence of abnormal myocardial perfusion by expert visual interpretation. RESULTS In external testing, the ML model had higher prediction performance for abnormal perfusion (area under receiver-operating characteristic curve [AUC] 0.762, 95% CI 0.750-0.774) compared to the clinical CAD consortium (AUC 0.689) basic CAD consortium (AUC 0.657), and updated Diamond-Forrester models (AUC 0.658, p < 0.001 for all). Calibration (validation of the continuous risk prediction) was superior for the ML model (Brier score 0.149) compared to the other models (Brier score 0.165 to 0.198, all p < 0.001). CONCLUSION ML can predict abnormal myocardial perfusion using readily available pre-test information. This model could be used to help guide physician decisions regarding non-invasive test selection.
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Affiliation(s)
- Robert J H Miller
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Suite Metro 203, Los Angeles, CA, 90048, USA
- Department of Cardiac Sciences, University of Calgary, Calgary, AB, Canada
- Libin Cardiovascular Institute, Calgary, AB, Canada
| | - M Timothy Hauser
- Section of Nuclear Cardiology, Department of Clinical Imaging, Oklahoma Heart Hospital, Oklahoma City, OK, USA
| | - Tali Sharir
- Department of Nuclear Cardiology, Assuta Medical Centers, Tel Aviv, Israel
- Ben Gurion University of the Negev, Beer Sheba, Israel
| | - Andrew J Einstein
- Division of Cardiology, Department of Medicine and Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA
- New York-Presbyterian Hospital, New York, NY, USA
| | - Mathews B Fish
- Oregon Heart and Vascular Institute, Sacred Heart Medical Center, Springfield, OR, USA
| | - Terrence D Ruddy
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, ON, Canada
| | - Philipp A Kaufmann
- Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Zurich, Switzerland
| | - Albert J Sinusas
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Edward J Miller
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | | | - Sharmila Dorbala
- Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Marcelo Di Carli
- Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Cathleen Huang
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Suite Metro 203, Los Angeles, CA, 90048, USA
| | - Joanna X Liang
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Suite Metro 203, Los Angeles, CA, 90048, USA
| | - Donghee Han
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Suite Metro 203, Los Angeles, CA, 90048, USA
| | - Damini Dey
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Suite Metro 203, Los Angeles, CA, 90048, USA
| | - Daniel S Berman
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Suite Metro 203, Los Angeles, CA, 90048, USA
| | - Piotr J Slomka
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Suite Metro 203, Los Angeles, CA, 90048, USA.
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10
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Baldwin J, Rodeghero J, Werneke MW, Mioduski JE, Jeffries L, Kucksdorf J, Shepherd M, Randall K, Dionne C, Dionne C. The effectiveness of post-professional physical therapist training in the treatment of chronic low back pain using a propensity score approach with machine learning. Musculoskeletal Care 2022; 20:625-640. [PMID: 35226394 PMCID: PMC9951186 DOI: 10.1002/msc.1626] [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: 02/01/2022] [Revised: 02/10/2022] [Accepted: 02/12/2022] [Indexed: 11/07/2022]
Abstract
RATIONALE Low back pain (LBP) is a leading cause of disability in the United States creating substantial hardships through negative social, financial, and health effects. Chronic low back pain (CLBP) accounted for above half of patients treated in physical therapy (PT) clinics for LBP. However, research shows small benefit from PT in CLBP treatment. Preliminary evidence suggests clinician-level training variables may affect outcomes, but requires further investigation to determine whether patients with CLBP benefit from treatment by providers with post-professional training. This study examined the relationship between clinician training levels and patient-reported outcomes in CLBP treatment. METHODS Physical therapies were surveyed using a large patient outcome assessment system to determine and categorise them by level of post-professional education. To account for the possibility that clinicians with higher levels of training are referred more-complex patients, a machine learning approach was used to identify predictive variables for clinician group, then to construct propensity scores to account for differences between groups. Differences in functional status score change among pooled data were analysed using linear models adjusted for propensity scores. RESULTS There were no clinically meaningful differences in patient outcomes when comparing clinician post-professional training level. The propensity score method proved to be a valuable way to account for differences at baseline between groups. CONCLUSION Post-professional training does not appear to contribute to improved patient outcomes in the treatment of CLBP. This study demonstrates that propensity score analysis can be used to ensure that differences observed are true and not due to differences at baseline.
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Affiliation(s)
- Jonathan Baldwin
- The University of Oklahoma Health Sciences Center, College of Allied Health, Department of Medical Imaging and Radiation Sciences, Oklahoma City OK, USA
| | - Jason Rodeghero
- Tufts University, Department of Public Health & Community Medicine, School of Medicine, Boston MA, USA
| | | | | | - Lynn Jeffries
- The University of Oklahoma Health Sciences Center, College of Allied Health, Department of Medical Imaging and Radiation Sciences, Oklahoma City OK, USA
| | - Joseph Kucksdorf
- Bellin Health, Orthopedics and Sports Medicine, Green Bay WI, USA
| | - Mark Shepherd
- Bellin College, Physical Therapy Department, Green Bay WI, USA
| | - Ken Randall
- The University of Oklahoma Health Sciences Center, College of Allied Health, Department of Medical Imaging and Radiation Sciences, Oklahoma City OK, USA
| | - Carol Dionne
- The University of Oklahoma Health Sciences Center, College of Allied Health, Department of Medical Imaging and Radiation Sciences, Oklahoma City OK, USA
| | - Carol Dionne
- College of Allied Health, Department of Medical Imaging and Radiation Sciences, The University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA
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11
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Miller RJH, Huang C, Liang JX, Slomka PJ. Artificial intelligence for disease diagnosis and risk prediction in nuclear cardiology. J Nucl Cardiol 2022; 29:1754-1762. [PMID: 35508795 DOI: 10.1007/s12350-022-02977-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 03/29/2022] [Indexed: 10/18/2022]
Abstract
Artificial intelligence (AI) techniques have emerged as a highly efficient approach to accurately and rapidly interpret diagnostic imaging and may play a vital role in nuclear cardiology. In nuclear cardiology, there are many clinical, stress, and imaging variables potentially available, which need to be optimally integrated to predict the presence of obstructive coronary artery disease (CAD) or predict the risk of cardiovascular events. In spite of clinical awareness of a large number of potential variables, it is difficult for physicians to integrate multiple features consistently and objectively. Machine learning (ML) is particularly well suited to integrating this vast array of information to provide patient-specific predictions. Deep learning (DL), a branch of ML characterized by a multi-layered convolutional model architecture, can extract information directly from images and identify latent image features associated with a specific prediction. This review will discuss the latest AI applications to disease diagnosis and risk prediction in nuclear cardiology with a focus on potential clinical applications.
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Affiliation(s)
- Robert J H Miller
- Division of Artificial Intelligence in Medicine, Departments of Medicine, Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Suite Metro 203, Los Angeles, CA, 90048, USA
- Department of Cardiac Sciences, University of Calgary and Libin Cardiovascular Institute, Calgary, AB, Canada
| | - Cathleen Huang
- Division of Artificial Intelligence in Medicine, Departments of Medicine, Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Suite Metro 203, Los Angeles, CA, 90048, USA
| | - Joanna X Liang
- Division of Artificial Intelligence in Medicine, Departments of Medicine, Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Suite Metro 203, Los Angeles, CA, 90048, USA
| | - Piotr J Slomka
- Division of Artificial Intelligence in Medicine, Departments of Medicine, Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Suite Metro 203, Los Angeles, CA, 90048, USA.
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12
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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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13
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Teuho J, Schultz J, Klén R, Knuuti J, Saraste A, Ono N, Kanaya S. Classification of ischemia from myocardial polar maps in 15O-H 2O cardiac perfusion imaging using a convolutional neural network. Sci Rep 2022; 12:2839. [PMID: 35181681 PMCID: PMC8857225 DOI: 10.1038/s41598-022-06604-x] [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: 06/08/2021] [Accepted: 02/03/2022] [Indexed: 12/02/2022] Open
Abstract
We implemented a two-dimensional convolutional neural network (CNN) for classification of polar maps extracted from Carimas (Turku PET Centre, Finland) software used for myocardial perfusion analysis. 138 polar maps from 15O–H2O stress perfusion study in JPEG format from patients classified as ischemic or non-ischemic based on finding obstructive coronary artery disease (CAD) on invasive coronary artery angiography were used. The CNN was evaluated against the clinical interpretation. The classification accuracy was evaluated with: accuracy (ACC), area under the receiver operating characteristic curve (AUC), F1 score (F1S), sensitivity (SEN), specificity (SPE) and precision (PRE). The CNN had a median ACC of 0.8261, AUC of 0.8058, F1S of 0.7647, SEN of 0.6500, SPE of 0.9615 and PRE of 0.9286. In comparison, clinical interpretation had ACC of 0.8696, AUC of 0.8558, F1S of 0.8333, SEN of 0.7500, SPE of 0.9615 and PRE of 0.9375. The CNN classified only 2 cases differently than the clinical interpretation. The clinical interpretation and CNN had similar accuracy in classifying false positives and true negatives. Classification of ischemia is feasible in 15O–H2O stress perfusion imaging using JPEG polar maps alone with a custom CNN and may be useful for the detection of obstructive CAD.
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Affiliation(s)
- Jarmo Teuho
- Data Science Center, Nara University of Science and Technology, Nara, Japan. .,Turku PET Centre, University of Turku, Turku, Finland. .,Turku PET Centre, Turku University Hospital, Turku, Finland.
| | - Jussi Schultz
- Turku PET Centre, Turku University Hospital, Turku, Finland
| | - Riku Klén
- Turku PET Centre, University of Turku, Turku, Finland.,Turku PET Centre, Turku University Hospital, Turku, Finland
| | - Juhani Knuuti
- Turku PET Centre, University of Turku, Turku, Finland.,Turku PET Centre, Turku University Hospital, Turku, Finland
| | - Antti Saraste
- Turku PET Centre, Turku University Hospital, Turku, Finland.,Heart Centre, Turku University Hospital and University of Turku, Turku, Finland
| | - Naoaki Ono
- Data Science Center, Nara University of Science and Technology, Nara, Japan.,Department of Science and Technology, Nara University of Science and Technology, Nara, Japan
| | - Shigehiko Kanaya
- Department of Science and Technology, Nara University of Science and Technology, Nara, Japan
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14
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Aleman R, Patel S, Sleiman J, Navia J, Sheffield C, Brozzi NA. Cardiogenic shock and machine learning: A systematic review on prediction through clinical decision support softwares. J Card Surg 2021; 36:4153-4159. [PMID: 34463361 DOI: 10.1111/jocs.15934] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 08/03/2021] [Indexed: 11/29/2022]
Abstract
BACKGROUND AND AIM Cardiogenic shock (CS) withholds a significantly high mortality rate between 40% and 60% despite advances in diagnosis and medical/surgical intervention. To date, machine learning (ML) is being implemented to integrate numerous data to optimize early diagnostic predictions and suggest clinical courses. This systematic review summarizes the area under the curve (AUC) receiver operating characteristics (ROCs) accuracy for the early prediction of CS. METHODS A systematic review was conducted within databases of PubMed, ScienceDirect, Clinical Key/MEDLINE, Embase, GoogleScholar, and Cochrane. Cohort studies that assessed the accuracy of early detection of CS using ML software were included. Data extraction was focused on AUC-ROC values directed towards the early detection of CS. RESULTS A total of 943 studies were included for systematic review. From the reviewed studies, 2.2% (N = 21) evaluated patient outcomes, of which 14.3% (N = 3) were assessed. The collective patient cohort (N = 698) consisted of 314 (45.0%) females, with an average age and body mass index of 64.1 years and 28.1 kg/m2 , respectively. Collectively, 159 (22.8%) mortalities were reported following early CS detection. Altogether, the AUC-ROC value was 0.82 (α = .05), deeming it of superb sensitivity and specificity. CONCLUSIONS From the present comprehensively gathered data, this study accounts the use of ML software for the early detection of CS in a clinical setting as a valid tool to predict patients at risk of CS. The complexity of ML and its parallel lack of clinical evidence implies that further prospective randomized control trials are needed to draw definitive conclusions before standardizing the use of these technologies. BRIEF SUMMARY The catastrophic risk of developing CS continues to be a concern in the management of critical cardiac care. The use of ML predictive models have the potential to provide the accurate and necessary feedback for the early detection and proper management of CS. This systematic review summarizes the AUC-ROCs accuracy for the early prediction of CS.
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Affiliation(s)
- Rene Aleman
- Department of Cardio-Thoracic Surgery, Heart, Vascular and Thoracic Institute, Cleveland Clinic Florida, Weston, Florida, USA
| | - Sinal Patel
- Department of Cardio-Thoracic Surgery, Heart, Vascular and Thoracic Institute, Cleveland Clinic Florida, Weston, Florida, USA
| | - Jose Sleiman
- Department of Cardiology, Cleveland Clinic Florida, Weston, Florida, USA
| | - Jose Navia
- Department of Cardio-Thoracic Surgery, Heart, Vascular and Thoracic Institute, Cleveland Clinic Florida, Weston, Florida, USA
| | - Cedric Sheffield
- Department of Cardio-Thoracic Surgery, Heart, Vascular and Thoracic Institute, Cleveland Clinic Florida, Weston, Florida, USA
| | - Nicolas A Brozzi
- Department of Cardio-Thoracic Surgery, Heart, Vascular and Thoracic Institute, Cleveland Clinic Florida, Weston, Florida, USA
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15
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Garcia EV. Artificial intelligence in nuclear cardiology: Preparing for the fifth industrial revolution. J Nucl Cardiol 2021; 28:1199-1202. [PMID: 34342863 DOI: 10.1007/s12350-021-02671-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 05/05/2021] [Indexed: 01/28/2023]
Affiliation(s)
- Ernest V Garcia
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 101 Woodruff Circle, Room 1203, Atlanta, GA, 30322, USA.
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16
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Juarez-Orozco LE, Knuuti J. Machine learning in defining computed tomography-derived fractional flow reserve. Eur Heart J Cardiovasc Imaging 2021; 22:1007-1008. [PMID: 34166504 DOI: 10.1093/ehjci/jeab124] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Luis Eduardo Juarez-Orozco
- AIOS Cardiologie, Divisie Hart en Longen, Universitair Medisch Centrum Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Juhani Knuuti
- Turku PET Centre, University of Turku and Turku University Hospital, Kiinalyllynkatu 4-8, 20520 Turku, Finland
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17
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Benjamins JW, Yeung MW, Maaniitty T, Saraste A, Klén R, van der Harst P, Knuuti J, Juarez-Orozco LE. Improving patient identification for advanced cardiac imaging through machine learning-integration of clinical and coronary CT angiography data. Int J Cardiol 2021; 335:130-136. [PMID: 33831505 DOI: 10.1016/j.ijcard.2021.04.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 03/04/2021] [Accepted: 04/02/2021] [Indexed: 11/15/2022]
Abstract
BACKGROUND Standard computed tomography angiography (CTA) outputs a myriad of interrelated variables in the evaluation of suspected coronary artery disease (CAD). But an important proportion of obstructive lesions does not cause significant myocardial ischemia. Nowadays, machine learning (ML) allows integration of numerous variables through complex interdependencies that optimize classification and prediction at the individual level. We evaluated ML performance in integrating CTA and clinical variables to identify patients that demonstrate myocardial ischemia through PET and those who ultimately underwent early revascularization. METHODS AND RESULTS 830 patients with CTA and selective PET were analyzed. Nine clinical and 58 CTA variables were integrated through ensemble-boosting ML to identify patients with ischemia and those who underwent early revascularization. ML performance was compared against expert CTA interpretation, calcium score and clinical variables. While ML using all CTA variables achieved an AUC = 0.85, it was outperformed by expert CTA interpretation (AUC = 0.87, p < 0.01 for comparison), comparable to ML integration of CTA variables with clinical variables. However, the best performance was achieved by ML integration of expert CTA interpretation and clinical variables for both dependent variables (AUCs = 0.91 and 0.90, p < 0.001). CONCLUSIONS Machine learning integration of diagnostic CTA and clinical data may improve identification of patients with myocardial ischemia and those requiring early revascularization at the individual level. This could potentially aid in sparing the need for subsequent advanced imaging and better identifying patients in ultimate need for revascularization. While ML integrating all CTA variables did not outperform expert CTA interpretation, ML data integration from different sources consistently improves diagnostic performance.
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Affiliation(s)
- Jan Walter Benjamins
- University of Groningen, University Medical Center Groningen, Department of Cardiology, Hanzeplein 1, 9700RB Groningen, the Netherlands
| | - Ming Wai Yeung
- University of Groningen, University Medical Center Groningen, Department of Cardiology, Hanzeplein 1, 9700RB Groningen, the Netherlands
| | - Teemu Maaniitty
- Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20520 Turku, Finland
| | - Antti Saraste
- Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20520 Turku, Finland
| | - Riku Klén
- Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20520 Turku, Finland
| | - Pim van der Harst
- University of Groningen, University Medical Center Groningen, Department of Cardiology, Hanzeplein 1, 9700RB Groningen, the Netherlands; Department of Cardiology, Hart and Lung Division, University Medical Centre Utrecht, Heidelberglaan 100, 3508, GA, Utrecht, the Netherlands
| | - Juhani Knuuti
- Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20520 Turku, Finland
| | - Luis Eduardo Juarez-Orozco
- University of Groningen, University Medical Center Groningen, Department of Cardiology, Hanzeplein 1, 9700RB Groningen, the Netherlands; Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20520 Turku, Finland; Department of Cardiology, Hart and Lung Division, University Medical Centre Utrecht, Heidelberglaan 100, 3508, GA, Utrecht, the Netherlands.
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18
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Slomka PJ, Miller RJH, Isgum I, Dey D. Application and Translation of Artificial Intelligence to Cardiovascular Imaging in Nuclear Medicine and Noncontrast CT. Semin Nucl Med 2020; 50:357-366. [DOI: 10.1053/j.semnuclmed.2020.03.004] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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19
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Calvillo-Argüelles O, Sierra-Fernández CR, Padilla-Ibarra J, Rodriguez-Zanella H, Balderas-Muñoz K, Arias-Mendoza MA, Martínez-Sánchez C, Selmen-Chattaj S, Dominguez-Mendez BE, van der Harst P, Juarez-Orozco LE. Integrating the STOP-BANG Score and Clinical Data to Predict Cardiovascular Events After Infarction: A Machine Learning Study. Chest 2020; 158:1669-1679. [PMID: 32343966 DOI: 10.1016/j.chest.2020.03.074] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Revised: 01/26/2020] [Accepted: 03/06/2020] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND OSA conveys worse clinical outcomes in patients with coronary artery disease. The STOP-BANG score is a simple tool that evaluates the risk of OSA and can be added to the large number of clinical variables and scores that are obtained during the management of patients with myocardial infarction (MI). Currently, machine learning (ML) is able to select and integrate numerous variables to optimize prediction tasks. RESEARCH QUESTION Can the integration of STOP-BANG score with clinical data and scores through ML better identify patients who experienced an in-hospital cardiovascular event after acute MI? STUDY DESIGN AND METHODS This is a prospective observational cohort study of 124 patients with acute MI of whom the STOP-BANG score classified 34 as low (27.4%), 30 as intermediate (24.2%), and 60 as high (48.4%) OSA-risk patients who were followed during hospitalization. ML implemented feature selection and integration across 47 variables (including STOP-BANG score, Killip class, GRACE score, and left ventricular ejection fraction) to identify those patients who experienced an in-hospital cardiovascular event (ie, death, ventricular arrhythmias, atrial fibrillation, recurrent angina, reinfarction, stroke, worsening heart failure, or cardiogenic shock) after definitive MI treatment. Receiver operating characteristic curves were used to compare ML performance against STOP-BANG score, Killip class, GRACE score, and left ventricular ejection fraction, independently. RESULTS There were an increasing proportion of cardiovascular events across the low, intermediate, and high OSA risk groups (P = .005). ML selected 7 accessible variables (ie, Killip class, leukocytes, GRACE score, c reactive protein, oxygen saturation, STOP-BANG score, and N-terminal prohormone of B-type natriuretic peptide); their integration outperformed all comparators (area under the curve, 0.83 [95% CI, 0.74-0.90]; P < .01). INTERPRETATION The integration of the STOP-BANG score into clinical evaluation (considering Killip class, GRACE score, and simple laboratory values) of subjects who were admitted for an acute MI because of ML can significantly optimize the identification of patients who will experience an in-hospital cardiovascular event.
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Affiliation(s)
- Oscar Calvillo-Argüelles
- Division of Cardiology, Peter Munk Cardiac Center, Toronto General Hospital, University Health Network, University of Toronto, Toronto, ON, Canada; Department of Clinical Cardiology, National Institute of Cardiology "Ignacio Chávez,", Mexico City, Mexico
| | - Carlos R Sierra-Fernández
- Acute Cardiovascular and Coronary Care Unit, National Institute of Cardiology "Ignacio Chávez,", Mexico City, Mexico
| | - Jorge Padilla-Ibarra
- Department of Clinical Cardiology, National Institute of Cardiology "Ignacio Chávez,", Mexico City, Mexico
| | - Hugo Rodriguez-Zanella
- Echocardiography Laboratory, National Institute of Cardiology "Ignacio Chávez," Mexico City, Mexico
| | - Karla Balderas-Muñoz
- Department of Clinical Cardiology, National Institute of Cardiology "Ignacio Chávez,", Mexico City, Mexico
| | - Maria Alexandra Arias-Mendoza
- Acute Cardiovascular and Coronary Care Unit, National Institute of Cardiology "Ignacio Chávez,", Mexico City, Mexico
| | - Carlos Martínez-Sánchez
- Acute Cardiovascular and Coronary Care Unit, National Institute of Cardiology "Ignacio Chávez,", Mexico City, Mexico
| | - Sharon Selmen-Chattaj
- Clinical Pharmacology Master Program, Faculty of Chemical Sciences, La Salle University. Mexico City, Mexico, Mexico City, Mexico
| | | | - Pim van der Harst
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Luis Eduardo Juarez-Orozco
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
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20
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Abstract
Cardiac imaging has a pivotal role in the prevention, diagnosis and treatment of ischaemic heart disease. SPECT is most commonly used for clinical myocardial perfusion imaging, whereas PET is the clinical reference standard for the quantification of myocardial perfusion. MRI does not involve exposure to ionizing radiation, similar to echocardiography, which can be performed at the bedside. CT perfusion imaging is not frequently used but CT offers coronary angiography data, and invasive catheter-based methods can measure coronary flow and pressure. Technical improvements to the quantification of pathophysiological parameters of myocardial ischaemia can be achieved. Clinical consensus recommendations on the appropriateness of each technique were derived following a European quantitative cardiac imaging meeting and using a real-time Delphi process. SPECT using new detectors allows the quantification of myocardial blood flow and is now also suited to patients with a high BMI. PET is well suited to patients with multivessel disease to confirm or exclude balanced ischaemia. MRI allows the evaluation of patients with complex disease who would benefit from imaging of function and fibrosis in addition to perfusion. Echocardiography remains the preferred technique for assessing ischaemia in bedside situations, whereas CT has the greatest value for combined quantification of stenosis and characterization of atherosclerosis in relation to myocardial ischaemia. In patients with a high probability of needing invasive treatment, invasive coronary flow and pressure measurement is well suited to guide treatment decisions. In this Consensus Statement, we summarize the strengths and weaknesses as well as the future technological potential of each imaging modality.
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21
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Nappi C, Cuocolo A. The machine learning approach: Artificial intelligence is coming to support critical clinical thinking. J Nucl Cardiol 2020; 27:156-158. [PMID: 29923100 DOI: 10.1007/s12350-018-1344-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Accepted: 06/08/2018] [Indexed: 12/15/2022]
Affiliation(s)
- Carmela Nappi
- Department of Advanced Biomedical Sciences, University Federico II, Via Pansini 5, 80131, Naples, Italy
| | - Alberto Cuocolo
- Department of Advanced Biomedical Sciences, University Federico II, Via Pansini 5, 80131, Naples, Italy.
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22
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Martin-Isla C, Campello VM, Izquierdo C, Raisi-Estabragh Z, Baeßler B, Petersen SE, Lekadir K. Image-Based Cardiac Diagnosis With Machine Learning: A Review. Front Cardiovasc Med 2020; 7:1. [PMID: 32039241 PMCID: PMC6992607 DOI: 10.3389/fcvm.2020.00001] [Citation(s) in RCA: 75] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Accepted: 01/06/2020] [Indexed: 01/28/2023] Open
Abstract
Cardiac imaging plays an important role in the diagnosis of cardiovascular disease (CVD). Until now, its role has been limited to visual and quantitative assessment of cardiac structure and function. However, with the advent of big data and machine learning, new opportunities are emerging to build artificial intelligence tools that will directly assist the clinician in the diagnosis of CVDs. This paper presents a thorough review of recent works in this field and provide the reader with a detailed presentation of the machine learning methods that can be further exploited to enable more automated, precise and early diagnosis of most CVDs.
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Affiliation(s)
- Carlos Martin-Isla
- Departament de Matemàtiques & Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Victor M Campello
- Departament de Matemàtiques & Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Cristian Izquierdo
- Departament de Matemàtiques & Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Zahra Raisi-Estabragh
- Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom.,William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Bettina Baeßler
- Department of Diagnostic & Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Steffen E Petersen
- Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom.,William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Karim Lekadir
- Departament de Matemàtiques & Informàtica, Universitat de Barcelona, Barcelona, Spain
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23
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Benjamins JW, Hendriks T, Knuuti J, Juarez-Orozco LE, van der Harst P. A primer in artificial intelligence in cardiovascular medicine. Neth Heart J 2019; 27:392-402. [PMID: 31111458 PMCID: PMC6712147 DOI: 10.1007/s12471-019-1286-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Driven by recent developments in computational power, algorithms and web-based storage resources, machine learning (ML)-based artificial intelligence (AI) has quickly gained ground as the solution for many technological and societal challenges. AI education has become very popular and is oversubscribed at Dutch universities. Major investments were made in 2018 to develop and build the first AI-driven hospitals to improve patient care and reduce healthcare costs. AI has the potential to greatly enhance traditional statistical analyses in many domains and has been demonstrated to allow the discovery of 'hidden' information in highly complex datasets. As such, AI can also be of significant value in the diagnosis and treatment of cardiovascular disease, and the first applications of AI in the cardiovascular field are promising. However, many professionals in the cardiovascular field involved in patient care, education or science are unaware of the basics behind AI and the existing and expected applications in their field. In this review, we aim to introduce the broad cardiovascular community to the basics of modern ML-based AI and explain several of the commonly used algorithms. We also summarise their initial and future applications relevant to the cardiovascular field.
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Affiliation(s)
- J W Benjamins
- University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen, The Netherlands
| | - T Hendriks
- University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen, The Netherlands
| | - J Knuuti
- Turku PET Center, Turku University Hospital and University of Turku, Turku, Finland
| | - L E Juarez-Orozco
- University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen, The Netherlands
- Turku PET Center, Turku University Hospital and University of Turku, Turku, Finland
| | - P van der Harst
- University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen, The Netherlands.
- Durrer Center for Cardiovascular Research, Netherlands Heart Institute, Utrecht, The Netherlands.
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands.
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24
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Siegersma KR, Leiner T, Chew DP, Appelman Y, Hofstra L, Verjans JW. Artificial intelligence in cardiovascular imaging: state of the art and implications for the imaging cardiologist. Neth Heart J 2019; 27:403-413. [PMID: 31399886 PMCID: PMC6712136 DOI: 10.1007/s12471-019-01311-1] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Healthcare, conceivably more than any other area of human endeavour, has the greatest potential to be affected by artificial intelligence (AI). This potential has been shown by several reports that demonstrate equal or superhuman performance in medical tasks that aim to improve efficiency, diagnosis and prognosis. This review focuses on the state of the art of AI applications in cardiovascular imaging. It provides an overview of the current applications and studies performed, including the potential value, implications, limitations and future directions of AI in cardiovascular imaging.It is envisioned that AI will dramatically change the way doctors practise medicine. In the short term, it will assist physicians with easy tasks, such as automating measurements, making predictions based on big data, and putting clinical findings into an evidence-based context. In the long term, AI will not only assist doctors, it has the potential to significantly improve access to health and well-being data for patients and their caretakers. This empowers patients. From a physician's perspective, reliable AI assistance will be available to support clinical decision-making. Although cardiovascular studies implementing AI are increasing in number, the applications have only just started to penetrate contemporary clinical care.
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Affiliation(s)
- K R Siegersma
- Department of Cardiology, location VUmc, Amsterdam University Medical Centres, Amsterdam, The Netherlands.,Department of Experimental Cardiology, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
| | - T Leiner
- Department of Radiology, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
| | - D P Chew
- Department of Cardiovascular Medicine, Flinders Medical Centre, Bedford Park, SA, Australia.,South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Y Appelman
- Department of Cardiology, location VUmc, Amsterdam University Medical Centres, Amsterdam, The Netherlands
| | - L Hofstra
- Department of Cardiology, location VUmc, Amsterdam University Medical Centres, Amsterdam, The Netherlands.,Cardiologie Centra Nederland, Amsterdam, The Netherlands
| | - J W Verjans
- South Australian Health and Medical Research Institute, Adelaide, SA, Australia. .,Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia. .,Dept of Cardiology, Royal Adelaide Hospital, Adelaide, SA, Australia.
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25
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Nappi C, Gaudieri V, Cuocolo A. Behind Traditional Semi-quantitative Scores of Myocardial Perfusion Imaging: An Eye on Niche Parameters. Eur Cardiol 2019; 14:13-17. [PMID: 31131032 PMCID: PMC6523048 DOI: 10.15420/ecr.2019.5.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
The evaluation of stress-induced myocardial perfusion defects by non-invasive myocardial perfusion imaging (MPI) modalities has a leading role in the identification of coronary artery disease, and has excellent diagnostic and prognostic value. Non-invasive MPI can be performed using conventional and novel gamma cameras or by PET/CT. New software has allowed novel parameters that may have a role in the identification of early marks of cardiac impairment to be evaluated. We aim to give an overview of niche parameters obtainable by single photon emission CT (SPECT) and PET/CT MPI that may help practitioners to detect initial signs of cardiac damage and identify new therapy targets. In particular, we summarise the role of left ventricular geometry indices for remodelling, phase analysis parameters to evaluate mechanical dyssynchrony, the concept of relative flow reserve in the evaluation of flow-limiting epicardial stenosis, vascular age and epicardial adipose tissue as early markers of atherosclerotic burden, and emerging parameters for the evaluation of myocardial innervation, such as the total defect score.
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Affiliation(s)
- Carmela Nappi
- Department of Advanced Biomedical Sciences, University Federico II Naples, Italy
| | - Valeria Gaudieri
- Department of Advanced Biomedical Sciences, University Federico II Naples, Italy
| | - Alberto Cuocolo
- Department of Advanced Biomedical Sciences, University Federico II Naples, Italy
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26
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Benjamins JW, van Leeuwen K, Hofstra L, Rienstra M, Appelman Y, Nijhof W, Verlaat B, Everts I, den Ruijter HM, Isgum I, Leiner T, Vliegenthart R, Asselbergs FW, Juarez-Orozco LE, van der Harst P. Enhancing cardiovascular artificial intelligence (AI) research in the Netherlands: CVON-AI consortium. Neth Heart J 2019; 27:414-425. [PMID: 31111459 PMCID: PMC6712143 DOI: 10.1007/s12471-019-1281-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Background Machine learning (ML) allows the exploration and progressive improvement of very complex high-dimensional data patterns that can be utilised to optimise specific classification and prediction tasks, outperforming traditional statistical approaches. An enormous acceleration of ready-to-use tools and artificial intelligence (AI) applications, shaped by the emergence, refinement, and application of powerful ML algorithms in several areas of knowledge, is ongoing. Although such progress has begun to permeate the medical sciences and clinical medicine, implementation in cardiovascular medicine and research is still in its infancy. Objectives To lay out the theoretical framework, purpose, and structure of a novel AI consortium. Methods We have established a new Dutch research consortium, the CVON-AI, supported by the Netherlands Heart Foundation, to catalyse and facilitate the development and utilisation of AI solutions for existing and emerging cardiovascular research initiatives and to raise AI awareness in the cardiovascular research community. CVON-AI will connect to previously established CVON consortia and apply a cloud-based AI platform to supplement their planned traditional data-analysis approach. Results A pilot experiment on the CVON-AI cloud was conducted using cardiac magnetic resonance data. It demonstrated the feasibility of the platform and documented excellent correlation between AI-generated ventricular function estimates as compared to expert manual annotations. The resulting AI solution was then integrated in a web application. Conclusion CVON-AI is a new consortium meant to facilitate the implementation and raise awareness of AI in cardiovascular research in the Netherlands. CVON-AI will create an accessible cloud-based platform for cardiovascular researchers, demonstrate the clinical applicability of AI, optimise the analytical methodology of other ongoing CVON consortia, and promote AI awareness through education and training.
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Affiliation(s)
- J W Benjamins
- University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen, The Netherlands
| | | | - L Hofstra
- Cardiologie Centra Nederland B.V., Utrecht, The Netherlands.,Department of Cardiology, Amsterdam Universities Medical Centre, location VU Medical Centre, Amsterdam, The Netherlands
| | - M Rienstra
- University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen, The Netherlands
| | - Y Appelman
- Department of Cardiology, Amsterdam Universities Medical Centre, location VU Medical Centre, Amsterdam, The Netherlands
| | - W Nijhof
- Siemens Healthcare Nederland B.V., Den Haag, The Netherlands
| | - B Verlaat
- Binx.io B.V., Amsterdam, The Netherlands
| | - I Everts
- Go Data Driven, Amsterdam, The Netherlands
| | - H M den Ruijter
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands
| | - I Isgum
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands
| | - T Leiner
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands
| | - R Vliegenthart
- University of Groningen, University Medical Center Groningen, Department of Radiology, Groningen, The Netherlands
| | - F W Asselbergs
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands.,Durrer Center for Cardiovascular Research, Netherlands Heart Institute, Utrecht, The Netherlands.,Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK.,Institute of Health Informatics, University College London, London, UK
| | - L E Juarez-Orozco
- University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen, The Netherlands.,Turku PET Centre, Turku University Hospital and University of Turku, Turku, Finland
| | - P van der Harst
- University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen, The Netherlands. .,Durrer Center for Cardiovascular Research, Netherlands Heart Institute, Utrecht, The Netherlands. .,University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands.
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27
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Park YJ, Bae JH, Shin MH, Hyun SH, Cho YS, Choe YS, Choi JY, Lee KH, Kim BT, Moon SH. Development of Predictive Models in Patients with Epiphora Using Lacrimal Scintigraphy and Machine Learning. Nucl Med Mol Imaging 2019; 53:125-135. [PMID: 31057684 PMCID: PMC6473022 DOI: 10.1007/s13139-019-00574-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Revised: 09/19/2018] [Accepted: 01/07/2019] [Indexed: 12/22/2022] Open
Abstract
PURPOSE We developed predictive models using different programming languages and different computing platforms for machine learning (ML) and deep learning (DL) that classify clinical diagnoses in patients with epiphora. We evaluated the diagnostic performance of these models. METHODS Between January 2016 and September 2017, 250 patients with epiphora who underwent dacryocystography (DCG) and lacrimal scintigraphy (LS) were included in the study. We developed five different predictive models using ML tools, Python-based TensorFlow, R, and Microsoft Azure Machine Learning Studio (MAMLS). A total of 27 clinical characteristics and parameters including variables related to epiphora (VE) and variables related to dacryocystography (VDCG) were used as input data. Apart from this, we developed two predictive convolutional neural network (CNN) models for diagnosing LS images. We conducted this study using supervised learning. RESULTS Among 500 eyes of 250 patients, 59 eyes had anatomical obstruction, 338 eyes had functional obstruction, and the remaining 103 eyes were normal. For the data set that excluded VE and VDCG, the test accuracies in Python-based TensorFlow, R, multiclass logistic regression in MAMLS, multiclass neural network in MAMLS, and nuclear medicine physician were 81.70%, 80.60%, 81.70%, 73.10%, and 80.60%, respectively. The test accuracies of CNN models in three-class classification diagnosis and binary classification diagnosis were 72.00% and 77.42%, respectively. CONCLUSIONS ML-based predictive models using different programming languages and different computing platforms were useful for classifying clinical diagnoses in patients with epiphora and were similar to a clinician's diagnostic ability.
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Affiliation(s)
- Yong-Jin Park
- Departments of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50, Irwon-dong, Gangnam-gu, Seoul, 135-710 South Korea
| | - Ji Hoon Bae
- Departments of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50, Irwon-dong, Gangnam-gu, Seoul, 135-710 South Korea
| | - Mu Heon Shin
- Departments of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50, Irwon-dong, Gangnam-gu, Seoul, 135-710 South Korea
| | - Seung Hyup Hyun
- Departments of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50, Irwon-dong, Gangnam-gu, Seoul, 135-710 South Korea
| | - Young Seok Cho
- Departments of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50, Irwon-dong, Gangnam-gu, Seoul, 135-710 South Korea
| | - Yearn Seong Choe
- Departments of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50, Irwon-dong, Gangnam-gu, Seoul, 135-710 South Korea
| | - Joon Young Choi
- Departments of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50, Irwon-dong, Gangnam-gu, Seoul, 135-710 South Korea
| | - Kyung-Han Lee
- Departments of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50, Irwon-dong, Gangnam-gu, Seoul, 135-710 South Korea
| | - Byung-Tae Kim
- Departments of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50, Irwon-dong, Gangnam-gu, Seoul, 135-710 South Korea
| | - Seung Hwan Moon
- Departments of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50, Irwon-dong, Gangnam-gu, Seoul, 135-710 South Korea
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28
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Juarez-Orozco LE, Martinez-Manzanera O, Storti AE, Knuuti J. Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology. CURRENT CARDIOVASCULAR IMAGING REPORTS 2019. [DOI: 10.1007/s12410-019-9480-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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29
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Selection of abstracts from the scientific sessions of the Society of Nuclear Medicine and Molecular Imaging, Philadelphia PA June 23–26, 2018. J Nucl Cardiol 2018. [DOI: 10.1007/s12350-018-1405-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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