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2,086 results on '"Di Carli, Marcelo"'

1. Atlas for reporting PET myocardial perfusion imaging and myocardial blood flow in clinical practice: an information statement from the American Society of Nuclear Cardiology

Author

Chareonthaitawee, Panithaya, Bateman, Timothy M., Beanlands, Rob S., Berman, Daniel S., Calnon, Dennis A., Di Carli, Marcelo F., Heller, Gary V., Murthy, Venkatesh L., Patel, Krishna K., Schindler, Thomas H., Taqueti, Viviany R., Wiefels, Christiane C., and Al-Mallah, Mouaz H.

Published
2023
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5. Handling missing values in machine learning to predict patient-specific risk of adverse cardiac events: Insights from REFINE SPECT registry

Author

Rios, Richard, Miller, Robert JH, Manral, Nipun, Sharir, Tali, Einstein, Andrew J, Fish, Mathews B, Ruddy, Terrence D, Kaufmann, Philipp A, Sinusas, Albert J, Miller, Edward J, Bateman, Timothy M, Dorbala, Sharmila, Di Carli, Marcelo, Van Kriekinge, Serge D, Kavanagh, Paul B, Parekh, Tejas, Liang, Joanna X, Dey, Damini, Berman, Daniel S, and Slomka, Piotr J

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Health Services and Systems, Health Sciences, Information and Computing Sciences, Applied Computing, Patient Safety, Cardiovascular, Heart Disease, Good Health and Well Being, Humans, Machine Learning, Myocardial Perfusion Imaging, Registries, Tomography, Emission-Computed, Single-Photon, Machine learning, Clinical implementation, Missing values, Prognosis, Myocardial perfusion imaging, Engineering, Medical and Health Sciences, Biomedical Engineering, Bioinformatics and computational biology, Health services and systems, Applied computing
Abstract

BackgroundMachine 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.MethodsWe 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).ResultsDuring 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

Published
2022

6. Accuracy and Reproducibility of Myocardial Blood Flow Quantification by Single Photon Emission Computed Tomography Imaging in Patients With Known or Suspected Coronary Artery Disease

Author

de Souza, Ana Carolina do AH, Harms, Hendrik J, Martell, Laurel, Bibbo, Courtney, Harrington, Meagan, Sullivan, Kyle, Hainer, Jon, Dorbala, Sharmila, Blankstein, Ron, Taqueti, Viviany R, Kijewski, Marie Foley, Park, Mi-Ae, Meretta, Alejandro, Breault, Christopher, Roth, Nathaniel, Poitrasson-Rivière, Alexis, Soman, Prem, Gullberg, Grant T, and Di Carli, Marcelo F

Subjects
Heart Disease - Coronary Heart Disease, Cardiovascular, Biomedical Imaging, Clinical Research, Heart Disease, Prevention, Detection, screening and diagnosis, 4.2 Evaluation of markers and technologies, Ammonia, Cadmium, Coronary Artery Disease, Coronary Circulation, Humans, Myocardial Perfusion Imaging, Positron-Emission Tomography, Reproducibility of Results, Technetium Tc 99m Sestamibi, Tomography, Emission-Computed, Single-Photon, Zinc, myocardial blood flow, myocardial flow reserve, positron emission tomography, tomography, emission-computed, single-photon, tomography, emission-computed, single-photon, Clinical Sciences, Cardiovascular System & Hematology
Abstract

BackgroundSingle photon emission computed tomography (SPECT) has limited ability to identify multivessel and microvascular coronary artery disease. Gamma cameras with cadmium zinc telluride detectors allow the quantification of absolute myocardial blood flow (MBF) and myocardial flow reserve (MFR). However, evidence of its accuracy is limited, and of its reproducibility is lacking. We aimed to validate 99mTc-sestamibi SPECT MBF and MFR using standard and spline-fitted reconstruction algorithms compared with 13N-ammonia positron emission tomography in a cohort of patients with known or suspected coronary artery disease and to evaluate the reproducibility of this technique.MethodsAccuracy was assessed in 34 participants who underwent dynamic 99mTc-sestamibi SPECT and 13N-ammonia positron emission tomography and reproducibility in 14 participants who underwent 2 99mTc-sestamibi SPECT studies, all within 2 weeks. A rest/pharmacological stress single-day SPECT protocol was performed. SPECT images were reconstructed using a standard ordered subset expectation maximization (OSEM) algorithm with (N=21) and without (N=30) application of spline fitting. SPECT MBF was quantified using a net retention kinetic model' and MFR was derived as the stress/rest MBF ratio.ResultsSPECT global MBF with splines showed good correlation with 13N-ammonia positron emission tomography (r=0.81, P0.05 for all).ConclusionsMBF and MFR quantification using 99mTc-sestamibi cadmium zinc telluride SPECT with spatiotemporal spline fitting improved the correlation with 13N-ammonia positron emission tomography flow estimates and test/retest reproducibility. The use of splines may represent an important step toward the standardization of SPECT flow estimation.

Published
2022

9. Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging

Author

Pieszko, Konrad, Shanbhag, Aakash D., Singh, Ananya, Hauser, M. Timothy, Miller, Robert J. H., Liang, Joanna X., Motwani, Manish, Kwieciński, Jacek, Sharir, Tali, Einstein, Andrew J., Fish, Mathews B., Ruddy, Terrence D., Kaufmann, Philipp A., Sinusas, Albert J., Miller, Edward J., Bateman, Timothy M., Dorbala, Sharmila, Di Carli, Marcelo, Berman, Daniel S., Dey, Damini, and Slomka, Piotr J.

Published
2023
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11. Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging

Author

Williams, Michelle C., Bednarski, Bryan P., Pieszko, Konrad, Miller, Robert J. H., Kwiecinski, Jacek, Shanbhag, Aakash, Liang, Joanna X., Huang, Cathleen, Sharir, Tali, Dorbala, Sharmila, Di Carli, Marcelo F., Einstein, Andrew J., Sinusas, Albert J., Miller, Edward J., Bateman, Timothy M., Fish, Mathews B., Ruddy, Terrence D., Acampa, Wanda, Hauser, M. Timothy, Kaufmann, Philipp A., Dey, Damini, Berman, Daniel S., and Slomka, Piotr J.

Published
2023
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13. Clinical phenotypes among patients with normal cardiac perfusion using unsupervised learning: a retrospective observational study

Author

Miller, Robert J.H., Bednarski, Bryan P., Pieszko, Konrad, Kwiecinski, Jacek, Williams, Michelle C., Shanbhag, Aakash, Liang, Joanna X., Huang, Cathleen, Sharir, Tali, Hauser, M. Timothy, Dorbala, Sharmila, Di Carli, Marcelo F., Fish, Mathews B., Ruddy, Terrence D., Bateman, Timothy M., Einstein, Andrew J., Kaufmann, Philipp A., Miller, Edward J., Sinusas, Albert J., Acampa, Wanda, Han, Donghee, Dey, Damini, Berman, Daniel S., and Slomka, Piotr J.

Published
2024
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14. Rationale and design of the RAPID-WATER-FLOW trial: Radiolabeled perfusion to identify coronary artery disease using water to evaluate responses of myocardial FLOW

Author

Di Carli, Marcelo F., Gormsen, Lars C., Chareonthaitawee, Panithaya, Johnson, Geoffrey B., Beanlands, Rob, DeKemp, Rob, Schindler, Thomas, Gropler, Robert, Kulkarni, Harshad, McNeely, Parren, Soman, Prem, Oz, Orhan, Zaha, Vlad, Sorensen, Jens, Harms, Heinrich, Orlandi, Cesare, Vandenbroucke, Emily, and Udelson, James

Published
2024
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15. Evaluation of CV Inflammation and Infection

Author

Giubbini, Raffaele, Milan, E., Singh, V., Di Carli, Marcelo F., Dondi, Maurizio, Carvajal, I., Alexanderson, E., Rodella, C., Paez, Diana, Di Carli, Marcelo F., editor, Dondi, Maurizio, editor, Giubbini, Raffaele, editor, and Paez, Diana, editor

Published
2022
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16. Emerging Applications

Author

Di Carli, Marcelo F., Giubbini, Raffaele, Williams, M., Bertoli, M., Dondi, Maurizio, Paez, Diana, Milan, E., Di Carli, Marcelo F., editor, Dondi, Maurizio, editor, Giubbini, Raffaele, editor, and Paez, Diana, editor

Published
2022
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17. Evaluation of Ischemic Heart Disease

Author

Di Carli, Marcelo F., Giubbini, Raffaele, Raggi, P., Singh, V., Albano, D., Camoni, L., Bertoli, M., Rodella, C., Dondi, Maurizio, Endozo, R., Thornton, A., Paez, Diana, Di Carli, Marcelo F., editor, Dondi, Maurizio, editor, Giubbini, Raffaele, editor, and Paez, Diana, editor

Published
2022
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18. Evaluation of Infiltrative Cardiomyopathies

Author

Di Carli, Marcelo F., Singh, V., Divakaran, S., Cuddy, S., Dorbala, S., Giubbini, Raffaele, Dondi, Maurizio, Di Carli, Marcelo F., editor, Dondi, Maurizio, editor, Giubbini, Raffaele, editor, and Paez, Diana, editor

Published
2022
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19. Technical Considerations for Cardiac PET/CT

Author

Di Carli, Marcelo F., Giubbini, Raffaele, Albano, D., Milan, E., Carvajal, I., Alexanderson, E., Paez, Diana, Dondi, Maurizio, Di Carli, Marcelo F., editor, Dondi, Maurizio, editor, Giubbini, Raffaele, editor, and Paez, Diana, editor

Published
2022
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20. Combined Computed Tomography Angiography-Computed Tomography Perfusion in the Identification and Prognostic Assessment of Myocardial Bridging from the CORE320 Study: 5-Year Follow-Up

Author

Gannon, Michael P., Cerci, Rodrigo J., Valdiviezo, Carolina, Ostovaneh, Mohammad R., Vavere, Andrea L., de Vasconcellos, Henrique Doria, Matheson, Matthew B., Cox, Christopher, Miller, Julie M., di Carli, Marcelo, Arbab-Zadeh, Armin, George, Richard T., Lima, João A.C., and Chen, Marcus Y.

Published
2023
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25. Mitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images

Author

Miller, Robert J. H., Singh, Ananya, Otaki, Yuka, Tamarappoo, Balaji K., Kavanagh, Paul, Parekh, Tejas, Hu, Lien-Hsin, Gransar, Heidi, Sharir, Tali, Einstein, Andrew J., Fish, Mathews B., Ruddy, Terrence D., Kaufmann, Philipp A., Sinusas, Albert J., Miller, Edward J., Bateman, Timothy M., Dorbala, Sharmila, Di Carli, Marcelo F., Liang, Joanna X., Dey, Damini, Berman, Daniel S., and Slomka, Piotr J.

Published
2023
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27. Comparison of diabetes to other prognostic predictors among patients referred for cardiac stress testing: A contemporary analysis from the REFINE SPECT Registry

Author

Han, Donghee, Rozanski, Alan, Gransar, Heidi, Tzolos, Evangelos, Miller, Robert J. H., Sharir, Tali, Einstein, Andrew J., Fish, Mathews B., Ruddy, Terrence D., Kaufmann, Philipp A., Sinusas, Albert J., Miller, Edward J., Bateman, Timothy M., Dorbala, Sharmila, Di Carli, Marcelo, Liang, Joanna X., Hu, Lien-Hsin, Dey, Damini, Berman, Daniel S., and Slomka, Piotr J.

Published
2022
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29. Myocardial Ischemic Burden and Differences in Prognosis Among Patients With and Without Diabetes: Results From the Multicenter International REFINE SPECT Registry

Author

Han, Donghee, Rozanski, Alan, Gransar, Heidi, Sharir, Tali, Einstein, Andrew J, Fish, Mathews B, Ruddy, Terrence D, Kaufmann, Philipp A, Sinusas, Albert J, Miller, Edward J, Bateman, Timothy M, Dorbala, Sharmila, Di Carli, Marcelo, Liang, Joanna X, Hu, Lien-Hsin, Germano, Guido, Dey, Damini, Berman, Daniel S, and Slomka, Piotr J

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Cardiovascular, Heart Disease, Biomedical Imaging, Heart Disease - Coronary Heart Disease, Diabetes, Metabolic and endocrine, Good Health and Well Being, Aged, Angina, Unstable, Cohort Studies, Coronary Artery Disease, Diabetes Mellitus, Diabetic Angiopathies, Female, Humans, Male, Middle Aged, Myocardial Infarction, Myocardial Ischemia, Myocardial Perfusion Imaging, Prevalence, Prognosis, Propensity Score, Registries, Risk Factors, Tomography, Emission-Computed, Single-Photon, Medical and Health Sciences, Endocrinology & Metabolism, Biomedical and clinical sciences, Health sciences
Abstract

ObjectivePrevalence and prognostic impact of cardiovascular disease differ between patients with or without diabetes. We aimed to explore differences in the prevalence and prognosis of myocardial ischemia by automated quantification of total perfusion deficit (TPD) among patients with and without diabetes.Research design and methodsOf 20,418 individuals who underwent single-photon emission computed tomography myocardial perfusion imaging, 2,951 patients with diabetes were matched to 2,951 patients without diabetes based on risk factors using propensity score. TPD was categorized as TPD = 0%, 0% < TPD < 1%, 1% ≤ TPD < 5%, 5% ≤ TPD ≤ 10%, and TPD >10%. Major adverse cardiovascular events (MACE) were defined as a composite of all-cause mortality, myocardial infarction, unstable angina, or late revascularization.ResultsMACE risk was increased in patients with diabetes compared with patients without diabetes at each level of TPD above 0 (P < 0.001 for interaction). In patients with TPD >10%, patients with diabetes had greater than twice the MACE risk compared with patients without diabetes (annualized MACE rate 9.4 [95% CI 6.7-11.6] and 3.9 [95% CI 2.8-5.6], respectively, P < 0.001). Patients with diabetes with even very minimal TPD (0% < TPD < 1%) experienced a higher risk for MACE than those with 0% TPD (hazard ratio 2.05 [95% CI 1.21-3.47], P = 0.007). Patients with diabetes with a TPD of 0.5% had a similar MACE risk as patients without diabetes with a TPD of 8%.ConclusionsFor every level of TPD >0%, even a very minimal deficit of 0% < TPD < 1%, the MACE risk was higher in the patients with diabetes compared with patients without diabetes. Patients with diabetes with minimal ischemia had comparable MACE risk as patients without diabetes with significant ischemia.

Published
2020

33. Diagnostic safety of a machine learning-based automatic patient selection algorithm for stress-only myocardial perfusion SPECT

Author

Eisenberg, Evann, Miller, Robert J. H., Hu, Lien-Hsin, Rios, Richard, Betancur, Julian, Azadani, Peyman, Han, Donghee, Sharir, Tali, Einstein, Andrew J., Bokhari, Sabahat, Fish, Mathews B., Ruddy, Terrence D., Kaufmann, Philipp A., Sinusas, Albert J., Miller, Edward J., Bateman, Timothy M., Dorbala, Sharmila, Di Carli, Marcelo, Liang, Joanna X., Otaki, Yuka, Tamarappoo, Balaji K., Dey, Damini, Berman, Daniel S., and Slomka, Piotr J.

Published
2022
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36. Abstract 16897: The Association of Lipoprotein(a) With Major Adverse Cardiovascular Events Among Individuals With and Without Baseline Atherosclerotic Cardiovascular Disease: The Mass General Brigham Lp(a) Registry

Author

Berman, Adam N, Biery, David, Besser, Stephanie, Singh, Avinainder, Weber, Brittany N, Huck, Daniel M, Divakaran, Sanjay, Hainer, Jon, Shiyovich, Arthur, Kaur, Gurleen, Blaha, Michael J, Cannon, Christopher P, Plutzky, Jorge, Januzzi, James L, Booth, John N, Lopez, J. A, Kent, Shia, Nasir, Khurram, Di Carli, Marcelo, Bhatt, Deepak L, and Blankstein, Ron

Published
2023
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37. Abstract 16773: The Association of Lp(a) and Standard Modifiable Cardiovascular Risk Factors (SMuRFs) With Incident Myocardial Infarction: The Mass General Brigham Lp(a) Registry

Author

Shiyovich, Arthur, Berman, Adam N, Besser, Stephanie A, Biery, David W, Kaur, Gurleen, Divakaran, Sanjay, Singh, Avinainder, Huck, Daniel M, Weber, Brittany N, Plutzky, Jorge, Di Carli, Marcelo, Nasir, Khurram, Cannon, Christopher P, Januzzi, James L, Bhatt, Deepak L, and Blankstein, Ron

Published
2023
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40. Coronary Flow Reserve, Inflammation, and Myocardial Strain: The CIRT-CFR Trial

Author

Taqueti, Viviany R., Shah, Amil M., Everett, Brendan M., Pradhan, Aruna D., Piazza, Gregory, Bibbo, Courtney, Hainer, Jon, Morgan, Victoria, Carolina do A. H. de Souza, Ana, Skali, Hicham, Blankstein, Ron, Dorbala, Sharmila, Goldhaber, Samuel Z., Le May, Michel R., Chow, Benjamin J.W., deKemp, Robert A., Hage, Fadi G., Beanlands, Rob S., Libby, Peter, Glynn, Robert J., Solomon, Scott D., Ridker, Paul M., and Di Carli, Marcelo F.

Published
2023
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41. Direct Risk Assessment From Myocardial Perfusion Imaging Using Explainable Deep Learning

Author

Singh, Ananya, Miller, Robert J.H., Otaki, Yuka, Kavanagh, Paul, Hauser, Michael T., Tzolos, Evangelos, Kwiecinski, Jacek, Van Kriekinge, Serge, Wei, Chih-Chun, Sharir, Tali, Einstein, Andrew J., Fish, Mathews B., Ruddy, Terrence D., Kaufmann, Philipp A., Sinusas, Albert J., Miller, Edward J., Bateman, Timothy M., Dorbala, Sharmila, Di Carli, Marcelo, Liang, Joanna X., Huang, Cathleen, Han, Donghee, Dey, Damini, Berman, Daniel S., and Slomka, Piotr J.

Published
2023
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44. Myocarditis in the Setting of Cancer Therapeutics: Proposed Case Definitions for Emerging Clinical Syndromes in Cardio-Oncology.

Author

Bonaca, Marc, Olenchock, Benjamin, Salem, Joe-Elie, Wiviott, Stephen, Ederhy, Stephane, Cohen, Ariel, Stewart, Garrick, Choueiri, Toni, Di Carli, Marcelo, Allenbach, Yves, Kumbhani, Dharam, Heinzerling, Lucie, Amiri-Kordestani, Laleh, Lyon, Alexander, Thavendiranathan, Paaladinesh, Padera, Robert, Lichtman, Andrew, Liu, Peter, Johnson, Douglas, and Moslehi, Javid

Subjects
oncology, medical, adjudication, immunotherapy, cardiovascular disease, myocarditis, immunosuppressive agents, Antineoplastic Agents, Immunological, Cardiology, Clinical Trials as Topic, Humans, Immunotherapy, Medical Oncology, Myocarditis, Neoplasms
Abstract

Recent developments in cancer therapeutics have improved outcomes but have also been associated with cardiovascular complications. Therapies harnessing the immune system have been associated with an immune-mediated myocardial injury described as myocarditis. Immune checkpoint inhibitors are one such therapy with an increasing number of case and cohort reports describing a clinical syndrome of immune checkpoint inhibitor–associated myocarditis. Although the full spectrum of immune checkpoint inhibitor–associated cardiovascular disease still needs to be fully defined, described cases of myocarditis range from syndromes with mild signs and symptoms to fatal events. These observations in the clinical setting stand in contrast to outcomes from randomized clinical trials in which myocarditis is a rare event that is investigator reported and lacking in a specific case definition. The complexities associated with diagnosis, as well as the heterogeneous clinical presentation of immune checkpoint inhibitor–associated myocarditis, have made ascertainment and identification of myocarditis with high specificity challenging in clinical trials and other data sets, limiting the ability to better understand the incidence, outcomes, and predictors of these rare events. Therefore, establishing a uniform definition of myocarditis for application in clinical trials of cancer immunotherapies will enable greater understanding of these events. We propose an operational definition of cancer therapy-associated myocarditis that may facilitate case ascertainment and report and therefore may enhance the understanding of the incidence, outcomes, and risk factors of this novel clinical syndrome.

Published
2019

45. Deep Learning Analysis of Upright-Supine High-Efficiency SPECT Myocardial Perfusion Imaging for Prediction of Obstructive Coronary Artery Disease: A Multicenter Study

Author

Betancur, Julian, Hu, Lien-Hsin, Commandeur, Frederic, Sharir, Tali, Einstein, Andrew J, Fish, Mathews B, Ruddy, Terrence D, Kaufmann, Philipp A, Sinusas, Albert J, Miller, Edward J, Bateman, Timothy M, Dorbala, Sharmila, Di Carli, Marcelo, Germano, Guido, Otaki, Yuka, Liang, Joanna X, Tamarappoo, Balaji K, Dey, Damini, Berman, Daniel S, and Slomka, Piotr J

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Heart Disease, Cardiovascular, Heart Disease - Coronary Heart Disease, Clinical Research, Biomedical Imaging, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, Aged, Coronary Artery Disease, Deep Learning, Female, Heart Ventricles, Humans, Image Processing, Computer-Assisted, Male, Middle Aged, Myocardial Perfusion Imaging, Stress, Physiological, Tomography, Emission-Computed, Single-Photon, obstructive coronary artery disease, SPECT myocardial perfusion imaging, deep learning, convolutional neural network, total perfusion deficit, Nuclear Medicine & Medical Imaging, Clinical sciences
Abstract

Combined analysis of SPECT myocardial perfusion imaging (MPI) performed with a solid-state camera on patients in 2 positions (semiupright, supine) is routinely used to mitigate attenuation artifacts. We evaluated the prediction of obstructive disease from combined analysis of semiupright and supine stress MPI by deep learning (DL) as compared with standard combined total perfusion deficit (TPD). Methods: 1,160 patients without known coronary artery disease (64% male) were studied. Patients underwent stress 99mTc-sestamibi MPI with new-generation solid-state SPECT scanners in 4 different centers. All patients had on-site clinical reads and invasive coronary angiography correlations within 6 mo of MPI. Obstructive disease was defined as at least 70% narrowing of the 3 major coronary arteries and at least 50% for the left main coronary artery. Images were quantified at Cedars-Sinai. The left ventricular myocardium was segmented using standard clinical nuclear cardiology software. The contour placement was verified by an experienced technologist. Combined stress TPD was computed using sex- and camera-specific normal limits. DL was trained using polar distributions of normalized radiotracer counts, hypoperfusion defects, and hypoperfusion severities and was evaluated for prediction of obstructive disease in a novel leave-one-center-out cross-validation procedure equivalent to external validation. During the validation procedure, 4 DL models were trained using data from 3 centers and then evaluated on the 1 center left aside. Predictions for each center were merged to have an overall estimation of the multicenter performance. Results: 718 (62%) patients and 1,272 of 3,480 (37%) arteries had obstructive disease. The area under the receiver operating characteristics curve for prediction of disease on a per-patient and per-vessel basis by DL was higher than for combined TPD (per-patient, 0.81 vs. 0.78; per-vessel, 0.77 vs. 0.73; P < 0.001). With the DL cutoff set to exhibit the same specificity as the standard cutoff for combined TPD, per-patient sensitivity improved from 61.8% (TPD) to 65.6% (DL) (P < 0.05), and per-vessel sensitivity improved from 54.6% (TPD) to 59.1% (DL) (P < 0.01). With the threshold matched to the specificity of a normal clinical read (56.3%), DL had a sensitivity of 84.8%, versus 82.6% for an on-site clinical read (P = 0.3). Conclusion: DL improves automatic interpretation of MPI as compared with current quantitative methods.

Published
2019

47. Clinical Deployment of Explainable Artificial Intelligence of SPECT for Diagnosis of Coronary Artery Disease

Author

Otaki, Yuka, Singh, Ananya, Kavanagh, Paul, Miller, Robert J.H., Parekh, Tejas, Tamarappoo, Balaji K., Sharir, Tali, Einstein, Andrew J., Fish, Mathews B., Ruddy, Terrence D., Kaufmann, Philipp A., Sinusas, Albert J., Miller, Edward J., Bateman, Timothy M., Dorbala, Sharmila, Di Carli, Marcelo, Cadet, Sebastien, Liang, Joanna X., Dey, Damini, Berman, Daniel S., and Slomka, Piotr J.

Published
2022
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50. Automated quantitative analysis of CZT SPECT stratifies cardiovascular risk in the obese population: Analysis of the REFINE SPECT registry

Author

Klein, Eyal, Miller, Robert J. H., Sharir, Tali, Einstein, Andrew J., Fish, Mathews B., Ruddy, Terrence D., Kaufmann, Philipp A., Sinusas, Albert J., Miller, Edward J., Bateman, Timothy M., Dorbala, Sharmila, Di Carli, Marcelo, Otaki, Yuka, Gransar, Heidi, Liang, Joanna X., Dey, Damini, Berman, Daniel S., and Slomka, Piotr J.

Published
2022
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