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Armoundas AA, Narayan SM, Arnett DK, Spector-Bagdady K, Bennett DA, Celi LA, Friedman PA, Gollob MH, Hall JL, Kwitek AE, Lett E, Menon BK, Sheehan KA, Al-Zaiti SS. Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association. Circulation 2024; 149:e1028-e1050. [PMID: 38415358 PMCID: PMC11042786 DOI: 10.1161/cir.0000000000001201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
A major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.
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Miller RJH, Slomka PJ. Artificial Intelligence in Nuclear Cardiology: An Update and Future Trends. Semin Nucl Med 2024:S0001-2998(24)00015-1. [PMID: 38521708 DOI: 10.1053/j.semnuclmed.2024.02.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 02/19/2024] [Indexed: 03/25/2024]
Abstract
Myocardial perfusion imaging (MPI), using either single photon emission computed tomography (SPECT) or positron emission tomography (PET), is one of the most commonly ordered cardiac imaging tests, with prominent clinical roles for disease diagnosis and risk prediction. Artificial intelligence (AI) could potentially play a role in many steps along the typical MPI workflow, from image acquisition through to clinical reporting and risk estimation. AI can be utilized to improve image quality, reducing radiation exposure and image acquisition times. Once images are acquired, AI can help optimize motion correction and image registration during image reconstruction or provide direct image attenuation correction. Utilizing these image sets, AI can segment a number of anatomic features from associated computed tomographic imaging or even generate synthetic attenuation imaging. Lastly, AI may play an important role in disease diagnosis or risk prediction by combining the large number of potentially important clinical, stress, and imaging-related variables. This review will focus on the most recent developments in the field, providing clinicians and researchers with a timely update on the field. Additionally, it will discuss future trends including applications of AI during multiple points of the typical MPI workflow to maximize clinical utility and methods to maximize the information that can be obtained from hybrid imaging.
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Affiliation(s)
- Robert J H Miller
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA; Department of Cardiac Sciences, University of Calgary, Calgary, AB, Canada
| | - Piotr J Slomka
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA.
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Nakajo M, Hirahara D, Jinguji M, Ojima S, Hirahara M, Tani A, Takumi K, Kamimura K, Ohishi M, Yoshiura T. Machine learning approach using 18F-FDG-PET-radiomic features and the visibility of right ventricle 18F-FDG uptake for predicting clinical events in patients with cardiac sarcoidosis. Jpn J Radiol 2024:10.1007/s11604-024-01546-y. [PMID: 38491333 DOI: 10.1007/s11604-024-01546-y] [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: 12/20/2023] [Accepted: 02/05/2024] [Indexed: 03/18/2024]
Abstract
OBJECTIVES To investigate the usefulness of machine learning (ML) models using pretreatment 18F-FDG-PET-based radiomic features for predicting adverse clinical events (ACEs) in patients with cardiac sarcoidosis (CS). MATERIALS AND METHODS This retrospective study included 47 patients with CS who underwent 18F-FDG-PET/CT scan before treatment. The lesions were assigned to the training (n = 38) and testing (n = 9) cohorts. In total, 49 18F-FDG-PET-based radiomic features and the visibility of right ventricle 18F-FDG uptake were used to predict ACEs using seven different ML algorithms (namely, decision tree, random forest [RF], neural network, k-nearest neighbors, Naïve Bayes, logistic regression, and support vector machine [SVM]) with tenfold cross-validation and the synthetic minority over-sampling technique. The ML models were constructed using the top four features ranked by the decrease in Gini impurity. The AUCs and accuracies were used to compare predictive performances. RESULTS Patients who developed ACEs presented with a significantly higher surface area and gray level run length matrix run length non-uniformity (GLRLM_RLNU), and lower neighborhood gray-tone difference matrix_coarseness and sphericity than those without ACEs (each, p < 0.05). In the training cohort, all seven ML algorithms had a good classification performance with AUC values of > 0.80 (range: 0.841-0.944). In the testing cohort, the RF algorithm had the highest AUC and accuracy (88.9% [8/9]) with a similar classification performance between training and testing cohorts (AUC: 0.945 vs 0.889). GLRLM_RLNU was the most important feature of the modeling process of this RF algorithm. CONCLUSION ML analyses using 18F-FDG-PET-based radiomic features may be useful for predicting ACEs in patients with CS.
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Affiliation(s)
- Masatoyo Nakajo
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan.
| | - Daisuke Hirahara
- Department of Management Planning Division, Harada Academy, 2-54-4 Higashitaniyama, Kagoshima, 890-0113, Japan
| | - Megumi Jinguji
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Satoko Ojima
- Department of Cardiovascular Medicine and Hypertension, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Mitsuho Hirahara
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Atsushi Tani
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Koji Takumi
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Kiyohisa Kamimura
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Mitsuru Ohishi
- Department of Cardiovascular Medicine and Hypertension, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Takashi Yoshiura
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
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Makimoto H, Kohro T. Adopting artificial intelligence in cardiovascular medicine: a scoping review. Hypertens Res 2024; 47:685-699. [PMID: 37907600 DOI: 10.1038/s41440-023-01469-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 09/03/2023] [Accepted: 09/26/2023] [Indexed: 11/02/2023]
Abstract
Recent years have witnessed significant transformations in cardiovascular medicine, driven by the rapid evolution of artificial intelligence (AI). This scoping review was conducted to capture the breadth of AI applications within cardiovascular science. Employing a structured approach, we sourced relevant articles from PubMed, with an emphasis on journals encompassing general cardiology and digital medicine. We applied filters to highlight cardiovascular articles published in journals focusing on general internal medicine, cardiology and digital medicine, thereby identifying the prevailing trends in the field. Following a comprehensive full-text screening, a total of 140 studies were identified. Over the preceding 5 years, cardiovascular medicine's interplay with AI has seen an over tenfold augmentation. This expansive growth encompasses multiple cardiovascular subspecialties, including but not limited to, general cardiology, ischemic heart disease, heart failure, and arrhythmia. Deep learning emerged as the predominant methodology. The majority of AI endeavors in this domain have been channeled toward enhancing diagnostic and prognostic capabilities, utilizing resources such as hospital datasets, electrocardiograms, and echocardiography. A significant uptrend was observed in AI's application for omics data analysis. However, a clear gap persists in AI's full-scale integration into the clinical decision-making framework. AI, particularly deep learning, has demonstrated robust applications across cardiovascular subspecialties, indicating its transformative potential in this field. As we continue on this trajectory, ensuring the alignment of technological progress with medical ethics becomes crucial. The abundant digital health data today further accentuates the need for meticulous systematic reviews, tailoring them to each cardiovascular subspecialty.
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Affiliation(s)
- Hisaki Makimoto
- Data Science Center/Cardiovascular Center, Jichi Medical University, Shimotsuke, Japan.
| | - Takahide Kohro
- Data Science Center/Cardiovascular Center, Jichi Medical University, Shimotsuke, Japan
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Holmstrom L, Bednarski B, Chugh H, Aziz H, Pham HN, Sargsyan A, Uy-Evanado A, Dey D, Salvucci A, Jui J, Reinier K, Slomka PJ, Chugh SS. Artificial Intelligence Model Predicts Sudden Cardiac Arrest Manifesting With Pulseless Electric Activity Versus Ventricular Fibrillation. Circ Arrhythm Electrophysiol 2024; 17:e012338. [PMID: 38284289 PMCID: PMC10876166 DOI: 10.1161/circep.123.012338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 12/13/2023] [Indexed: 01/30/2024]
Abstract
BACKGROUND There is no specific treatment for sudden cardiac arrest (SCA) manifesting as pulseless electric activity (PEA) and survival rates are low; unlike ventricular fibrillation (VF), which is treatable by defibrillation. Development of novel treatments requires fundamental clinical studies, but access to the true initial rhythm has been a limiting factor. METHODS Using demographics and detailed clinical variables, we trained and tested an AI model (extreme gradient boosting) to differentiate PEA-SCA versus VF-SCA in a novel setting that provided the true initial rhythm. A subgroup of SCAs are witnessed by emergency medical services personnel, and because the response time is zero, the true SCA initial rhythm is recorded. The internal cohort consisted of 421 emergency medical services-witnessed out-of-hospital SCAs with PEA or VF as the initial rhythm in the Portland, Oregon metropolitan area. External validation was performed in 220 emergency medical services-witnessed SCAs from Ventura, CA. RESULTS In the internal cohort, the artificial intelligence model achieved an area under the receiver operating characteristic curve of 0.68 (95% CI, 0.61-0.76). Model performance was similar in the external cohort, achieving an area under the receiver operating characteristic curve of 0.72 (95% CI, 0.59-0.84). Anemia, older age, increased weight, and dyspnea as a warning symptom were the most important features of PEA-SCA; younger age, chest pain as a warning symptom and established coronary artery disease were important features associated with VF. CONCLUSIONS The artificial intelligence model identified novel features of PEA-SCA, differentiated from VF-SCA and was successfully replicated in an external cohort. These findings enhance the mechanistic understanding of PEA-SCA with potential implications for developing novel management strategies.
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Affiliation(s)
- Lauri Holmstrom
- Division of Artificial Intelligence in Medicine, Department of Medicine (L.H., B.B., D.D., P.J.S., S.S.C.)
- Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Health System, Los Angeles (L.H., H.C., H.A., H.N.P., A.S., A.U.-E., K.R., S.S.C.)
| | - Bryan Bednarski
- Division of Artificial Intelligence in Medicine, Department of Medicine (L.H., B.B., D.D., P.J.S., S.S.C.)
| | - Harpriya Chugh
- Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Health System, Los Angeles (L.H., H.C., H.A., H.N.P., A.S., A.U.-E., K.R., S.S.C.)
| | - Habiba Aziz
- Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Health System, Los Angeles (L.H., H.C., H.A., H.N.P., A.S., A.U.-E., K.R., S.S.C.)
| | - Hoang Nhat Pham
- Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Health System, Los Angeles (L.H., H.C., H.A., H.N.P., A.S., A.U.-E., K.R., S.S.C.)
| | - Arayik Sargsyan
- Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Health System, Los Angeles (L.H., H.C., H.A., H.N.P., A.S., A.U.-E., K.R., S.S.C.)
| | - Audrey Uy-Evanado
- Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Health System, Los Angeles (L.H., H.C., H.A., H.N.P., A.S., A.U.-E., K.R., S.S.C.)
| | - Damini Dey
- Division of Artificial Intelligence in Medicine, Department of Medicine (L.H., B.B., D.D., P.J.S., S.S.C.)
| | | | - Jonathan Jui
- Department of Emergency Medicine, Oregon Health and Science University, Portland, OR (J.J.)
| | - Kyndaron Reinier
- Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Health System, Los Angeles (L.H., H.C., H.A., H.N.P., A.S., A.U.-E., K.R., S.S.C.)
| | - Piotr J. Slomka
- Division of Artificial Intelligence in Medicine, Department of Medicine (L.H., B.B., D.D., P.J.S., S.S.C.)
| | - Sumeet S. Chugh
- Division of Artificial Intelligence in Medicine, Department of Medicine (L.H., B.B., D.D., P.J.S., S.S.C.)
- Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Health System, Los Angeles (L.H., H.C., H.A., H.N.P., A.S., A.U.-E., K.R., S.S.C.)
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Pieszko K, Hiczkiewicz J, Łojewska K, Uziębło-Życzkowska B, Krzesiński P, Gawałko M, Budnik M, Starzyk K, Wożakowska-Kapłon B, Daniłowicz-Szymanowicz L, Kaufmann D, Wójcik M, Błaszczyk R, Mizia-Stec K, Wybraniec M, Kosmalska K, Fijałkowski M, Szymańska A, Dłużniewski M, Kucio M, Haberka M, Kupczyńska K, Michalski B, Tomaszuk-Kazberuk A, Wilk-Śledziewska K, Wachnicka-Truty R, Koziński M, Kwieciński J, Wolny R, Kowalik E, Kolasa I, Jurek A, Budzianowski J, Burchardt P, Kapłon-Cieślicka A, Slomka PJ. Artificial intelligence in detecting left atrial appendage thrombus by transthoracic echocardiography and clinical features: the Left Atrial Thrombus on Transoesophageal Echocardiography (LATTEE) registry. Eur Heart J 2024; 45:32-41. [PMID: 37453044 PMCID: PMC10757867 DOI: 10.1093/eurheartj/ehad431] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 05/03/2023] [Accepted: 06/22/2023] [Indexed: 07/18/2023] Open
Abstract
AIMS Transoesophageal echocardiography (TOE) is often performed before catheter ablation or cardioversion to rule out the presence of left atrial appendage thrombus (LAT) in patients on chronic oral anticoagulation (OAC), despite associated discomfort. A machine learning model [LAT-artificial intelligence (AI)] was developed to predict the presence of LAT based on clinical and transthoracic echocardiography (TTE) features. METHODS AND RESULTS Data from a 13-site prospective registry of patients who underwent TOE before cardioversion or catheter ablation were used. LAT-AI was trained to predict LAT using data from 12 sites (n = 2827) and tested externally in patients on chronic OAC from two sites (n = 1284). Areas under the receiver operating characteristic curve (AUC) of LAT-AI were compared with that of left ventricular ejection fraction (LVEF) and CHA2DS2-VASc score. A decision threshold allowing for a 99% negative predictive value was defined in the development cohort. A protocol where TOE in patients on chronic OAC is performed depending on the LAT-AI score was validated in the external cohort. In the external testing cohort, LAT was found in 5.5% of patients. LAT-AI achieved an AUC of 0.85 [95% confidence interval (CI): 0.82-0.89], outperforming LVEF (0.81, 95% CI 0.76-0.86, P < .0001) and CHA2DS2-VASc score (0.69, 95% CI: 0.63-0.7, P < .0001) in the entire external cohort. Based on the proposed protocol, 40% of patients on chronic OAC from the external cohort would safely avoid TOE. CONCLUSION LAT-AI allows accurate prediction of LAT. A LAT-AI-based protocol could be used to guide the decision to perform TOE despite chronic OAC.
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Affiliation(s)
- Konrad Pieszko
- ‘Club 30’, Polish Cardiac Society, Poland
- Department of Interventional Cardiology and Cardiac Surgery, University of Zielona Gora, Collegium Medicum, Zielona Gora, Poland
- WSSP ZOZ Nowa Sol, Nowa Sol, Poland
| | - Jarosław Hiczkiewicz
- Department of Interventional Cardiology and Cardiac Surgery, University of Zielona Gora, Collegium Medicum, Zielona Gora, Poland
- WSSP ZOZ Nowa Sol, Nowa Sol, Poland
| | | | - Beata Uziębło-Życzkowska
- ‘Club 30’, Polish Cardiac Society, Poland
- Department of Cardiology and Internal Diseases, Military Institute of Medicine, Warsaw, Poland
| | - Paweł Krzesiński
- ‘Club 30’, Polish Cardiac Society, Poland
- Department of Cardiology and Internal Diseases, Military Institute of Medicine, Warsaw, Poland
| | - Monika Gawałko
- ‘Club 30’, Polish Cardiac Society, Poland
- First Department of Cardiology, Medical University of Warsaw, Warsaw, Poland
- Department of Cardiology, Maastricht University Medical Centre and Cardiovascular Research Institute Maastricht, Maastricht, The Netherlands
- Institute of Pharmacology, West German Heart and Vascular Centre, University Duisburg-Essen, Essen, Germany
| | - Monika Budnik
- ‘Club 30’, Polish Cardiac Society, Poland
- First Department of Cardiology, Medical University of Warsaw, Warsaw, Poland
| | - Katarzyna Starzyk
- 1st Clinic of Cardiology and Electrotherapy, Swietokrzyskie Cardiology Centre, Kielce, Poland
| | - Beata Wożakowska-Kapłon
- 1st Clinic of Cardiology and Electrotherapy, Swietokrzyskie Cardiology Centre, Kielce, Poland
| | | | - Damian Kaufmann
- Department of Cardiology and Electrotherapy, Medical University of Gdansk, Gdansk, Poland
| | - Maciej Wójcik
- Department of Cardiology, Medical University of Lublin, Lublin, Poland
| | - Robert Błaszczyk
- Department of Cardiology, Medical University of Lublin, Lublin, Poland
| | - Katarzyna Mizia-Stec
- 1st Department of Cardiology, School of Medicine in Katowice, Medical University of Silesia, Katowice, Poland
| | - Maciej Wybraniec
- 1st Department of Cardiology, School of Medicine in Katowice, Medical University of Silesia, Katowice, Poland
| | | | | | - Anna Szymańska
- Department of Heart Diseases, Postgraduate Medical School, Warsaw, Poland
| | | | - Michał Kucio
- Department of Cardiology, School of Health Sciences, Medical University of Silesia, Katowice, Poland
| | - Maciej Haberka
- Department of Cardiology, School of Health Sciences, Medical University of Silesia, Katowice, Poland
| | | | - Błażej Michalski
- Department of Cardiology, Medical University of Lodz, Lodz, Poland
| | | | | | - Renata Wachnicka-Truty
- Department of Cardiology and Internal Medicine, Medical University of Gdansk, Gdynia, Poland
| | - Marek Koziński
- ‘Club 30’, Polish Cardiac Society, Poland
- Department of Cardiology and Internal Medicine, Medical University of Gdansk, Gdynia, Poland
| | - Jacek Kwieciński
- Department of Interventional Cardiology and Angiology, Institute of Cardiology, Warsaw, Poland
| | - Rafał Wolny
- Department of Interventional Cardiology and Angiology, Institute of Cardiology, Warsaw, Poland
| | - Ewa Kowalik
- Department of Congenital Heart Diseases, National Institute of Cardiology, Warsaw, Poland
| | - Iga Kolasa
- Department of Interventional Cardiology and Cardiac Surgery, University of Zielona Gora, Collegium Medicum, Zielona Gora, Poland
| | - Agnieszka Jurek
- ‘Club 30’, Polish Cardiac Society, Poland
- Department of Cardiology and Internal Diseases, Military Institute of Medicine, Warsaw, Poland
| | - Jan Budzianowski
- ‘Club 30’, Polish Cardiac Society, Poland
- Department of Interventional Cardiology and Cardiac Surgery, University of Zielona Gora, Collegium Medicum, Zielona Gora, Poland
- WSSP ZOZ Nowa Sol, Nowa Sol, Poland
| | - Paweł Burchardt
- ‘Club 30’, Polish Cardiac Society, Poland
- Department of Biology and Lipid Disorders, Poznan University of Medical Sciences, Poznan, Poland
| | - Agnieszka Kapłon-Cieślicka
- ‘Club 30’, Polish Cardiac Society, Poland
- First Department of Cardiology, Medical University of Warsaw, Warsaw, Poland
| | - Piotr J Slomka
- Department of Medicine (Division of Artificial Intelligence in Medicine), Cedars-Sinai Medical Center, 8700 Beverly Blvd, Suite Metro 203, 90048, Los Angeles, CA, USA
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7
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Miller RJH, Gransar H, Rozanski A, Dey D, Al‐Mallah M, Chow BJW, Kaufmann PA, Cademartiri F, Maffei E, Han D, Slomka PJ, Berman DS. Simplified Approach to Predicting Obstructive Coronary Disease With Integration of Coronary Calcium: Development and External Validation. J Am Heart Assoc 2023; 12:e031601. [PMID: 38108259 PMCID: PMC10863788 DOI: 10.1161/jaha.123.031601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 11/13/2023] [Indexed: 12/19/2023]
Abstract
BACKGROUND The Diamond-Forrester model was used extensively to predict obstructive coronary artery disease (CAD) but overestimates probability in current populations. Coronary artery calcium (CAC) is a useful marker of CAD, which is not routinely integrated with other features. We derived simple likelihood tables, integrating CAC with age, sex, and cardiac chest pain to predict obstructive CAD. METHODS AND RESULTS The training population included patients from 3 multinational sites (n=2055), with 2 sites for external testing (n=3321). We determined associations between age, sex, cardiac chest pain, and CAC with the presence of obstructive CAD, defined as any stenosis ≥50% on coronary computed tomography angiography. Prediction performance was assessed using area under the receiver-operating characteristic curves (AUCs) and compared with the CAD Consortium models with and without CAC, which require detailed calculations, and the updated Diamond-Forrester model. In external testing, the proposed likelihood tables had higher AUC (0.875 [95% CI, 0.862-0.889]) than the CAD Consortium clinical+CAC score (AUC, 0.868 [95% CI, 0.855-0.881]; P=0.030) and the updated Diamond-Forrester model (AUC, 0.679 [95% CI, 0.658-0.699]; P<0.001). The calibration for the likelihood tables was better than the CAD Consortium model (Brier score, 0.116 versus 0.121; P=0.005). CONCLUSIONS We have developed and externally validated simple likelihood tables to integrate CAC with age, sex, and cardiac chest pain, demonstrating improved prediction performance compared with other risk models. Our tool affords physicians with the opportunity to rapidly and easily integrate a small number of important features to estimate a patient's likelihood of obstructive CAD as an aid to clinical management.
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Affiliation(s)
- Robert J. H. Miller
- Departments of Medicine (Division of Artificial Intelligence in Medicine)Imaging and Biomedical SciencesCedars‐Sinai Medical CenterLos AngelesCA
- Libin Cardiovascular Institute of AlbertaUniversity of CalgaryCalgaryAlbertaCanada
| | - Heidi Gransar
- Departments of Medicine (Division of Artificial Intelligence in Medicine)Imaging and Biomedical SciencesCedars‐Sinai Medical CenterLos AngelesCA
| | - Alan Rozanski
- Departments of Medicine (Division of Artificial Intelligence in Medicine)Imaging and Biomedical SciencesCedars‐Sinai Medical CenterLos AngelesCA
- Division of Cardiology and Department of MedicineMount Sinai Morningside HospitalMount Sinai Heart and the Icahn School of Medicine at Mount SinaiNew YorkNY
| | - Damini Dey
- Departments of Medicine (Division of Artificial Intelligence in Medicine)Imaging and Biomedical SciencesCedars‐Sinai Medical CenterLos AngelesCA
| | - Mouaz Al‐Mallah
- Houston Methodist DeBakey Heart and Vascular CenterHoustonTX
| | - Benjamin J. W. Chow
- Departments of Medicine (Cardiology and Nuclear Medicine) and RadiologyUniversity of Ottawa Heart InstituteOttawaOntarioCanada
| | - Philipp A. Kaufmann
- Department of Nuclear MedicineUniversity Hospital Zurich, University of ZurichZurichSwitzerland
| | | | - Erica Maffei
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) SYNLAB SDNNaplesItaly
| | - Donghee Han
- Departments of Medicine (Division of Artificial Intelligence in Medicine)Imaging and Biomedical SciencesCedars‐Sinai Medical CenterLos AngelesCA
| | - Piotr J. Slomka
- Departments of Medicine (Division of Artificial Intelligence in Medicine)Imaging and Biomedical SciencesCedars‐Sinai Medical CenterLos AngelesCA
| | - Daniel S. Berman
- Departments of Medicine (Division of Artificial Intelligence in Medicine)Imaging and Biomedical SciencesCedars‐Sinai Medical CenterLos AngelesCA
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8
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Li J, Yang G, Zhang L. Artificial Intelligence Empowered Nuclear Medicine and Molecular Imaging in Cardiology: A State-of-the-Art Review. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:586-596. [PMID: 38223683 PMCID: PMC10781930 DOI: 10.1007/s43657-023-00137-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 10/13/2023] [Accepted: 10/16/2023] [Indexed: 01/16/2024]
Abstract
Nuclear medicine and molecular imaging plays a significant role in the detection and management of cardiovascular disease (CVD). With recent advancements in computer power and the availability of digital archives, artificial intelligence (AI) is rapidly gaining traction in the field of medical imaging, including nuclear medicine and molecular imaging. However, the complex and time-consuming workflow and interpretation involved in nuclear medicine and molecular imaging, limit their extensive utilization in clinical practice. To address this challenge, AI has emerged as a fundamental tool for enhancing the role of nuclear medicine and molecular imaging. It has shown promising applications in various crucial aspects of nuclear cardiology, such as optimizing imaging protocols, facilitating data processing, aiding in CVD diagnosis, risk classification and prognosis. In this review paper, we will introduce the key concepts of AI and provide an overview of its current progress in the field of nuclear cardiology. In addition, we will discuss future perspectives for AI in this domain.
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Affiliation(s)
- Junhao Li
- Department of Nuclear Medicine, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing, 210002 Jiangsu China
| | - Guifen Yang
- Department of Nuclear Medicine, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing, 210002 Jiangsu China
| | - Longjiang Zhang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing, 210002 Jiangsu China
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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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10
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Williams MC, Bednarski BP, Pieszko K, Miller RJH, Kwiecinski J, Shanbhag A, Liang JX, Huang C, Sharir T, Dorbala S, Di Carli MF, Einstein AJ, Sinusas AJ, Miller EJ, Bateman TM, Fish MB, Ruddy TD, Acampa W, Hauser MT, Kaufmann PA, Dey D, Berman DS, Slomka PJ. Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging. Eur J Nucl Med Mol Imaging 2023; 50:2656-2668. [PMID: 37067586 PMCID: PMC10317876 DOI: 10.1007/s00259-023-06218-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 03/29/2023] [Indexed: 04/18/2023]
Abstract
PURPOSE Patients with known coronary artery disease (CAD) comprise a heterogenous population with varied clinical and imaging characteristics. Unsupervised machine learning can identify new risk phenotypes in an unbiased fashion. We use cluster analysis to risk-stratify patients with known CAD undergoing single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). METHODS From 37,298 patients in the REFINE SPECT registry, we identified 9221 patients with known coronary artery disease. Unsupervised machine learning was performed using clinical (23), acquisition (17), and image analysis (24) parameters from 4774 patients (internal cohort) and validated with 4447 patients (external cohort). Risk stratification for all-cause mortality was compared to stress total perfusion deficit (< 5%, 5-10%, ≥10%). RESULTS Three clusters were identified, with patients in Cluster 3 having a higher body mass index, more diabetes mellitus and hypertension, and less likely to be male, have dyslipidemia, or undergo exercise stress imaging (p < 0.001 for all). In the external cohort, during median follow-up of 2.6 [0.14, 3.3] years, all-cause mortality occurred in 312 patients (7%). Cluster analysis provided better risk stratification for all-cause mortality (Cluster 3: hazard ratio (HR) 5.9, 95% confidence interval (CI) 4.0, 8.6, p < 0.001; Cluster 2: HR 3.3, 95% CI 2.5, 4.5, p < 0.001; Cluster 1, reference) compared to stress total perfusion deficit (≥10%: HR 1.9, 95% CI 1.5, 2.5 p < 0.001; < 5%: reference). CONCLUSIONS Our unsupervised cluster analysis in patients with known CAD undergoing SPECT MPI identified three distinct phenotypic clusters and predicted all-cause mortality better than ischemia alone.
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Affiliation(s)
- Michelle C Williams
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. Metro 203, Los Angeles, CA, 90048, USA
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Bryan P Bednarski
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. Metro 203, Los Angeles, CA, 90048, USA
| | - Konrad Pieszko
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. Metro 203, Los Angeles, CA, 90048, USA
| | - Robert J H Miller
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. Metro 203, Los Angeles, CA, 90048, USA
- Department of Cardiac Sciences, University of Calgary, Calgary, AB, Canada
| | - Jacek Kwiecinski
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. Metro 203, Los Angeles, CA, 90048, USA
- Department of Interventional Cardiology and Angiology, Institute of Cardiology, Warsaw, Poland
| | - Aakash Shanbhag
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. Metro 203, Los Angeles, CA, 90048, USA
| | - Joanna X Liang
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. Metro 203, Los Angeles, CA, 90048, USA
| | - Cathleen Huang
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. Metro 203, Los Angeles, CA, 90048, USA
| | - Tali Sharir
- Department of Nuclear Cardiology, Assuta Medical Centers, Tel Aviv, and Ben Gurion University of the Negev, Beer Sheba, Israel
| | - Sharmila Dorbala
- Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Brigham and Women's Hospital, Boston, MA, USA
| | - Marcelo F Di Carli
- Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Brigham and Women's Hospital, Boston, MA, USA
| | - Andrew J Einstein
- Division of Cardiology, Department of Medicine, and Department of Radiology, Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York, NY, USA
| | - 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
| | | | - 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
| | - Wanda Acampa
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - M Timothy Hauser
- Department of Nuclear Cardiology, Oklahoma Heart Hospital, Oklahoma City, OK, USA
| | - Philipp A Kaufmann
- Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Zurich, Switzerland
| | - Damini Dey
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. Metro 203, Los Angeles, CA, 90048, USA
| | - Daniel S Berman
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. Metro 203, Los Angeles, CA, 90048, USA
| | - Piotr J Slomka
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. Metro 203, Los Angeles, CA, 90048, USA.
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Pieszko K, Shanbhag AD, Singh A, Hauser MT, Miller RJH, Liang JX, Motwani M, Kwieciński J, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Berman DS, Dey D, Slomka PJ. Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging. NPJ Digit Med 2023; 6:78. [PMID: 37127660 PMCID: PMC10151323 DOI: 10.1038/s41746-023-00806-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 03/27/2023] [Indexed: 05/03/2023] Open
Abstract
Standard clinical interpretation of myocardial perfusion imaging (MPI) has proven prognostic value for predicting major adverse cardiovascular events (MACE). However, personalizing predictions to a specific event type and time interval is more challenging. We demonstrate an explainable deep learning model that predicts the time-specific risk separately for all-cause death, acute coronary syndrome (ACS), and revascularization directly from MPI and 15 clinical features. We train and test the model internally using 10-fold hold-out cross-validation (n = 20,418) and externally validate it in three separate sites (n = 13,988) with MACE follow-ups for a median of 3.1 years (interquartile range [IQR]: 1.6, 3.6). We evaluate the model using the cumulative dynamic area under receiver operating curve (cAUC). The best model performance in the external cohort is observed for short-term prediction - in the first six months after the scan, mean cAUC for ACS and all-cause death reaches 0.76 (95% confidence interval [CI]: 0.75, 0.77) and 0.78 (95% CI: 0.78, 0.79), respectively. The model outperforms conventional perfusion abnormality measures at all time points for the prediction of death in both internal and external validations, with improvement increasing gradually over time. Individualized patient explanations are visualized using waterfall plots, which highlight the contribution degree and direction for each feature. This approach allows the derivation of individual event probability as a function of time as well as patient- and event-specific risk explanations that may help draw attention to modifiable risk factors. Such a method could help present post-scan risk assessments to the patient and foster shared decision-making.
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Affiliation(s)
- Konrad Pieszko
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Interventional Cardiology and Cardiac Surgery, Collegium Medicum, University of Zielona Góra, Zielona Góra, Poland
| | - Aakash D Shanbhag
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ananya Singh
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - M Timothy Hauser
- Department of Nuclear Cardiology, Oklahoma Heart Hospital, Oklahoma City, OK, USA
| | - 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
- Department of Cardiac Sciences, University of Calgary and Libin Cardiovascular Institute, Calgary, AB, Canada
| | - Joanna X Liang
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Manish Motwani
- Institute of Cardiovascular Science, University of Manchester, Manchester, UK
- Department of Cardiology, Manchester Heart Institute, Manchester Royal Infirmary, Manchester University NHS Foundation Trust, Manchester, UK
| | - Jacek Kwieciński
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Interventional Cardiology and Angiology, Institute of Cardiology, Warsaw, Poland
| | - Tali Sharir
- Department of Nuclear Cardiology, Assuta Medical Centers, Tel Aviv, Israel
| | - Andrew J Einstein
- Division of Cardiology, Department of Medicine and Department of Radiology, Columbia University Irving Medical Center and NewYork-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
- Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Brigham and Women's Hospital, Boston, MA, USA
| | - Marcelo Di Carli
- Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Brigham and Women's Hospital, Boston, MA, 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
| | - Damini Dey
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, 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.
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Mehrabani-Zeinabad K, Feizi A, Sadeghi M, Roohafza H, Talaei M, Sarrafzadegan N. Cardiovascular disease incidence prediction by machine learning and statistical techniques: a 16-year cohort study from eastern Mediterranean region. BMC Med Inform Decis Mak 2023; 23:72. [PMID: 37076833 PMCID: PMC10116769 DOI: 10.1186/s12911-023-02169-5] [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: 09/19/2022] [Accepted: 04/04/2023] [Indexed: 04/21/2023] Open
Abstract
BACKGROUND Cardiovascular diseases (CVD) are the predominant cause of early death worldwide. Identification of people with a high risk of being affected by CVD is consequential in CVD prevention. This study adopts Machine Learning (ML) and statistical techniques to develop classification models for predicting the future occurrence of CVD events in a large sample of Iranians. METHODS We used multiple prediction models and ML techniques with different abilities to analyze the large dataset of 5432 healthy people at the beginning of entrance into the Isfahan Cohort Study (ICS) (1990-2017). Bayesian additive regression trees enhanced with "missingness incorporated in attributes" (BARTm) was run on the dataset with 515 variables (336 variables without and the remaining with up to 90% missing values). In the other used classification algorithms, variables with more than 10% missing values were excluded, and MissForest imputes the missing values of the remaining 49 variables. We used Recursive Feature Elimination (RFE) to select the most contributing variables. Random oversampling technique, recommended cut-point by precision-recall curve, and relevant evaluation metrics were used for handling unbalancing in the binary response variable. RESULTS This study revealed that age, systolic blood pressure, fasting blood sugar, two-hour postprandial glucose, diabetes mellitus, history of heart disease, history of high blood pressure, and history of diabetes are the most contributing factors for predicting CVD incidence in the future. The main differences between the results of classification algorithms are due to the trade-off between sensitivity and specificity. Quadratic Discriminant Analysis (QDA) algorithm presents the highest accuracy (75.50 ± 0.08) but the minimum sensitivity (49.84 ± 0.25); In contrast, decision trees provide the lowest accuracy (51.95 ± 0.69) but the top sensitivity (82.52 ± 1.22). BARTm.90% resulted in 69.48 ± 0.28 accuracy and 54.00 ± 1.66 sensitivity without any preprocessing step. CONCLUSIONS This study confirmed that building a prediction model for CVD in each region is valuable for screening and primary prevention strategies in that specific region. Also, results showed that using conventional statistical models alongside ML algorithms makes it possible to take advantage of both techniques. Generally, QDA can accurately predict the future occurrence of CVD events with a fast (inference speed) and stable (confidence values) procedure. The combined ML and statistical algorithm of BARTm provide a flexible approach without any need for technical knowledge about assumptions and preprocessing steps of the prediction procedure.
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Affiliation(s)
- Kamran Mehrabani-Zeinabad
- Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Awat Feizi
- Biostatistics and Epidemiology Department, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran.
- Cardiac Rehabilitation Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran.
| | - Masoumeh Sadeghi
- Cardiac Rehabilitation Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Hamidreza Roohafza
- Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mohammad Talaei
- Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Nizal Sarrafzadegan
- Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
- School of Population and Public Health, Faculty of Medicine, University of British Columbia, Vancouver, Canada
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13
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Miller RJ. Artificial Intelligence in Nuclear Cardiology. Cardiol Clin 2023; 41:151-161. [PMID: 37003673 DOI: 10.1016/j.ccl.2023.01.004] [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] [Indexed: 02/22/2023]
Abstract
Artificial intelligence (AI) encompasses a variety of computer algorithms that have a wide range of potential clinical applications in nuclear cardiology. This article will introduce core terminology and concepts for AI including classifications of AI as well as training and testing regimens. We will then highlight the potential role for AI to improve image registration and image quality. Next, we will discuss methods for AI-driven image attenuation correction. Finally, we will review advancements in machine learning and deep-learning applications for disease diagnosis and risk stratification, including efforts to improve clinical translation of this valuable technology with explainable AI models.
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14
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Gimelli A, Lakshmanan S, Della Tommasina V, Liga R. What Is New in Risk Assessment in Nuclear Cardiology? Cardiol Clin 2023; 41:197-205. [PMID: 37003677 DOI: 10.1016/j.ccl.2023.01.008] [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] [Indexed: 02/27/2023]
Abstract
Nuclear cardiology techniques allow in-depth evaluation of cardiac patients. A body of literature has established the use of nuclear cardiology. The results obtained with traditional cameras have been reinforced by those obtained with a series of innovations that have revolutionized the field of nuclear cardiology. This article highlights the role of nuclear cardiology in the risk assessment of patients with cardiac disease and sheds light on advancements of nuclear imaging techniques in the cardiovascular field. Patient risk stratification has a key role in modern precision medicine. Nuclear cardiac imaging techniques may quantitatively investigate major disease mechanisms of different cardiac pathologies.
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15
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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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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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Rios R, Miller RJH, Manral N, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Van Kriekinge SD, Kavanagh PB, Parekh T, Liang JX, Dey D, Berman DS, Slomka PJ. Handling missing values in machine learning to predict patient-specific risk of adverse cardiac events: Insights from REFINE SPECT registry. Comput Biol Med 2022; 145:105449. [PMID: 35381453 PMCID: PMC9117456 DOI: 10.1016/j.compbiomed.2022.105449] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 03/21/2022] [Accepted: 03/22/2022] [Indexed: 12/13/2022]
Abstract
BACKGROUND Machine learning (ML) models can improve prediction of major adverse cardiovascular events (MACE), but in clinical practice some values may be missing. We evaluated the influence of missing values in ML models for patient-specific prediction of MACE risk. METHODS We included 20,179 patients from the multicenter REFINE SPECT registry with MACE follow-up data. We evaluated seven methods for handling missing values: 1) removal of variables with missing values (ML-Remove), 2) imputation with median and unique category for continuous and categorical variables, respectively (ML-Traditional), 3) unique category for missing variables (ML-Unique), 4) cluster-based imputation (ML-Cluster), 5) regression-based imputation (ML-Regression), 6) missRanger imputation (ML-MR), and 7) multiple imputation (ML-MICE). We trained ML models with full data and simulated missing values in testing patients. Prediction performance was evaluated using area under the receiver-operating characteristic curve (AUC) and compared with a model without missing values (ML-All), expert visual diagnosis and total perfusion deficit (TPD). RESULTS During mean follow-up of 4.7 ± 1.5 years, 3,541 patients experienced at least one MACE (3.7% annualized risk). ML-All (reference model-no missing values) had AUC 0.799 for MACE risk prediction. All seven models with missing values had lower AUC (ML-Remove: 0.778, ML-MICE: 0.774, ML-Cluster: 0.771, ML-Traditional: 0.771, ML-Regression: 0.770, ML-MR: 0.766, and ML-Unique: 0.766; p < 0.01 for ML-Remove vs remaining methods). Stress TPD (AUC 0.698) and visual diagnosis (0.681) had the lowest AUCs. CONCLUSION Missing values reduce the accuracy of ML models when predicting MACE risk. Removing variables with missing values and retraining the model may yield superior patient-level prediction performance.
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Affiliation(s)
- Richard Rios
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Universidad Nacional de Colombia, Sede de La Paz, GAUNAL, La Paz, Colombia
| | - 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; Department of Cardiac Sciences, University of Calgary, Calgary, AB, Canada
| | - Nipun Manral
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Tali Sharir
- Department of Nuclear Cardiology, Assuta Medical Center, Tel Aviv, Israel; Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheba, Israel
| | - Andrew J Einstein
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York, NY, USA; Department of Radiology, Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York, NY, USA
| | - Mathews B Fish
- Department of Nuclear Medicine, 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
- Department of Internal Medicine, Section of Cardiovascular Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Edward J Miller
- Department of Internal Medicine, Section of Cardiovascular 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
| | - Serge D Van Kriekinge
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Paul B Kavanagh
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Tejas Parekh
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Joanna X Liang
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Damini Dey
- 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
| | - Piotr J Slomka
- 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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18
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Slomka P. Future of nuclear cardiology is bright: Promise of cardiac PET/CT and artificial intelligence. J Nucl Cardiol 2022; 29:389-391. [PMID: 35244874 DOI: 10.1007/s12350-022-02942-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 02/17/2022] [Indexed: 11/26/2022]
Affiliation(s)
- Piotr Slomka
- Division of Artificial Intelligence, Department of Medicine, Cedars-Sinai, Los Angeles, USA.
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19
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Miller RJH, Sabovčik F, Cauwenberghs N, Vens C, Khush KK, Heidenreich PA, Haddad F, Kuznetsova T. Temporal Shift and Predictive Performance of Machine Learning for Heart Transplant Outcomes. J Heart Lung Transplant 2022; 41:928-936. [DOI: 10.1016/j.healun.2022.03.019] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Revised: 02/25/2022] [Accepted: 03/23/2022] [Indexed: 11/27/2022] Open
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20
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Juarez-Orozco LE, Klén R, Niemi M, Ruijsink B, Daquarti G, van Es R, Benjamins JW, Yeung MW, van der Harst P, Knuuti J. Artificial Intelligence to Improve Risk Prediction with Nuclear Cardiac Studies. Curr Cardiol Rep 2022; 24:307-316. [PMID: 35171443 PMCID: PMC8852880 DOI: 10.1007/s11886-022-01649-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/17/2021] [Indexed: 12/28/2022]
Abstract
Purpose of Review As machine learning-based artificial intelligence (AI) continues to revolutionize the way in which we analyze data, the field of nuclear cardiology provides fertile ground for the implementation of these complex analytics. This review summarizes and discusses the principles regarding nuclear cardiology techniques and AI, and the current evidence regarding its performance and contribution to the improvement of risk prediction in cardiovascular disease. Recent Findings and Summary There is a growing body of evidence on the experimentation with and implementation of machine learning-based AI on nuclear cardiology studies both concerning SPECT and PET technology for the improvement of risk-of-disease (classification of disease) and risk-of-events (prediction of adverse events) estimations. These publications still report objective divergence in methods either utilizing statistical machine learning approaches or deep learning with varying architectures, dataset sizes, and performance. Recent efforts have been placed into bringing standardization and quality to the experimentation and application of machine learning-based AI in cardiovascular imaging to generate standards in data harmonization and analysis through AI. Machine learning-based AI offers the possibility to improve risk evaluation in cardiovascular disease through its implementation on cardiac nuclear studies. Graphical Abstract AI in improving risk evaluation in nuclear cardiology. * Based on the 2019 ESC guidelines ![]()
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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.,Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20520, Turku, Finland.,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
| | - Mikael Niemi
- Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20520, Turku, Finland
| | - Bram Ruijsink
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.,Division of Imaging Sciences and Biomedical Engineering, King's College London, St Thomas' Hospital, London, UK
| | - Gustavo Daquarti
- Department of Artificial Intelligence, UMA-Health, Buenos Aires, Argentina
| | - Rene van Es
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Jan-Walter Benjamins
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Ming Wai Yeung
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - 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
| | - Juhani Knuuti
- Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20520, Turku, Finland.
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21
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The application of artificial intelligence in nuclear cardiology. Ann Nucl Med 2022; 36:111-122. [PMID: 35029816 DOI: 10.1007/s12149-021-01708-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 12/05/2021] [Indexed: 01/17/2023]
Abstract
A decade of unprecedented progress in artificial intelligence (AI) has demonstrated a lot of interest in medical imaging research including nuclear cardiology. AI has a potential to reduce cost, save time and improve image acquisition, interpretation, and decision-making. This review summarizes recent researches and potential applications of AI in nuclear cardiology and discusses the pitfall of AI.
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22
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OUP accepted manuscript. Cardiovasc Res 2022; 118:2037-2038. [DOI: 10.1093/cvr/cvac083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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23
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Neglia D, Maroz-Vadalazhskaya N, Carrabba N, Liga R. Coronary Revascularization in Patients With Stable Coronary Artery Disease: The Role of Imaging. Front Cardiovasc Med 2021; 8:716832. [PMID: 34778391 PMCID: PMC8581143 DOI: 10.3389/fcvm.2021.716832] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Accepted: 10/07/2021] [Indexed: 12/12/2022] Open
Abstract
In the last decades, the effective management of some cardiovascular risk factors in the general population has led to a progressive decrease in the prevalence of coronary artery disease (CAD). Nevertheless, coronary heart disease remains the major cause of death in developed and developing countries and chronic coronary syndromes (CCS) are still a major target of utilization of non-invasive cardiac imaging and invasive procedures. Current guidelines recommend the use of non-invasive imaging in patients with CCS to identify subjects at higher risk to be referred for invasive coronary angiography and possible revascularization. These recommendations are challenged by two opposite lines of evidence. Recent trials have somewhat questioned the efficacy of coronary revascularization as compared with optimal medical therapy in CCS. As a consequence the role of imaging in these patients and in in patients with ischemic cardiomyopathy is under debate. On the other hand, real-life data indicate that a consistent proportion of patients undergo invasive procedure and are revascularized without any previous non-invasive imaging characterization. On top of this, the impact of COVID-19 pandemic on the sanitary systems caused a change in the current management of patients with CAD. In the present review we will discuss these conflicting data analyzing the evidence which has been recently accumulated as well as the gaps of knowledge which should still be filled.
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Affiliation(s)
- Danilo Neglia
- Cardiovascular Department, Fondazione CNR Regione Toscana G. Monasterio, Pisa, Italy.,Sant'Anna School of Advanced Studies, Pisa, Italy
| | | | - Nazario Carrabba
- Cardiothoracovascular Department, Careggi Hospital, Florence, Italy
| | - Riccardo Liga
- Cardiothoracic and Vascular Department, University Hospital of Pisa, Pisa, Italy.,Dipartimento di Patologia Chirurgica, Medica, Molecolare e dell'Area Critica, Università di Pisa, Pisa, Italy
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