{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T22:20:23Z","timestamp":1780611623661,"version":"3.54.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000050","name":"U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["R01-HL089765"],"award-info":[{"award-number":["R01-HL089765"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000050","name":"U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["R35-HL161195"],"award-info":[{"award-number":["R35-HL161195"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]},{"name":"U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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 (<jats:italic>n<\/jats:italic>\u2009=\u200920,418) and externally validate it in three separate sites (<jats:italic>n<\/jats:italic>\u2009=\u200913,988) 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 \u2013 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.<\/jats:p>","DOI":"10.1038\/s41746-023-00806-x","type":"journal-article","created":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T10:01:32Z","timestamp":1682935292000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging"],"prefix":"10.1038","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5514-7750","authenticated-orcid":false,"given":"Konrad","family":"Pieszko","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aakash D.","family":"Shanbhag","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ananya","family":"Singh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. Timothy","family":"Hauser","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert J. H.","family":"Miller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joanna X.","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4483-5168","authenticated-orcid":false,"given":"Manish","family":"Motwani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacek","family":"Kwieci\u0144ski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tali","family":"Sharir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew J.","family":"Einstein","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mathews B.","family":"Fish","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0686-5449","authenticated-orcid":false,"given":"Terrence D.","family":"Ruddy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9451-5210","authenticated-orcid":false,"given":"Philipp A.","family":"Kaufmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0972-9589","authenticated-orcid":false,"given":"Albert J.","family":"Sinusas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edward J.","family":"Miller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timothy M.","family":"Bateman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sharmila","family":"Dorbala","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcelo","family":"Di Carli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3793-9578","authenticated-orcid":false,"given":"Daniel S.","family":"Berman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Damini","family":"Dey","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6110-938X","authenticated-orcid":false,"given":"Piotr J.","family":"Slomka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,5,1]]},"reference":[{"key":"806_CR1","doi-asserted-by":"publisher","first-page":"e486","DOI":"10.1016\/S2589-7500(20)30160-6","volume":"2","author":"O Oren","year":"2020","unstructured":"Oren, O., Gersh, B. J. & Bhatt, D. L. Artificial intelligence in medical imaging: switching from radiographic pathological data to clinically meaningful endpoints. Lancet Digital Health 2, e486\u2013e488 (2020).","journal-title":"Lancet Digital Health"},{"key":"806_CR2","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1093\/eurheartj\/ehz425","volume":"41","author":"J Knuuti","year":"2020","unstructured":"Knuuti, J. et al. 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes: The Task Force for the diagnosis and management of chronic coronary syndromes of the European Society of Cardiology (ESC). Eur. Heart J. 41, 407\u2013477 (2020).","journal-title":"Eur. Heart J."},{"key":"806_CR3","unstructured":"Otaki, Y. et al. Clinical Deployment of Explainable Artificial Intelligence for Diagnosis of Coronary Artery Disease. JACC Cardiovasc Imaging In press. (2021)."},{"key":"806_CR4","first-page":"1282","volume":"141","author":"KD Knott","year":"2020","unstructured":"Knott, K. D. et al. The Prognostic Significance of Quantitative Myocardial Perfusion: An Artificial Intelligence\u2013Based Approach Using Perfusion Mapping. Circulation 141, 1282\u20131291 (2020).","journal-title":"Circulation"},{"key":"806_CR5","doi-asserted-by":"publisher","first-page":"3529","DOI":"10.1093\/eurheartj\/ehz592","volume":"40","author":"EK Oikonomou","year":"2019","unstructured":"Oikonomou, E. K. et al. A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography. Eur. Heart J. 40, 3529\u20133543 (2019).","journal-title":"Eur. Heart J."},{"key":"806_CR6","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1016\/j.jacc.2007.10.034","volume":"51","author":"KE Kip","year":"2008","unstructured":"Kip, K. E., Hollabaugh, K., Marroquin, O. C. & Williams, D. O. The Problem With Composite End Points in Cardiovascular Studies. The Story of Major Adverse Cardiac Events and Percutaneous Coronary Intervention. J. Am. Coll. Cardiol. 51, 701\u2013707 (2008).","journal-title":"J. Am. Coll. Cardiol."},{"key":"806_CR7","doi-asserted-by":"crossref","unstructured":"Mobadersany, P. et al. Predicting cancer outcomes from histology and genomics using convolutional networks. Proc. Natl. Acad. Sci. 115, E2970\u2013E2979 (2018).","DOI":"10.1073\/pnas.1717139115"},{"key":"806_CR8","doi-asserted-by":"publisher","first-page":"3163","DOI":"10.1109\/JBHI.2021.3052441","volume":"25","author":"C Nagpal","year":"2021","unstructured":"Nagpal, C., Li, X. & Dubrawski, A. Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data With Competing Risks. IEEE J. Biomed. Health Inf. 25, 3163\u20133175 (2021).","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"806_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12874-018-0482-1","volume":"18","author":"JL Katzman","year":"2018","unstructured":"Katzman, J. L. et al. DeepSurv: Personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med. Res. Methodol. 18, 1\u201312 (2018).","journal-title":"BMC Med. Res. Methodol."},{"key":"806_CR10","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1111\/j.2517-6161.1972.tb00899.x","volume":"34","author":"DR Cox","year":"1972","unstructured":"Cox, D. R. Regression Models and Life-Tables. J. R. Stat. Soc. Ser. B (Methodol.) 34, 187\u2013220 (1972).","journal-title":"J. R. Stat. Soc. Ser. B (Methodol.)"},{"key":"806_CR11","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-017-11817-6","volume":"7","author":"S Yousefi","year":"2017","unstructured":"Yousefi, S. et al. Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models. Sci. Rep. 7, 11707 (2017).","journal-title":"Sci. Rep."},{"key":"806_CR12","doi-asserted-by":"publisher","first-page":"6994","DOI":"10.1038\/s41598-019-43372-7","volume":"9","author":"DW Kim","year":"2019","unstructured":"Kim, D. W. et al. Deep learning-based survival prediction of oral cancer patients. Sci. Rep. 9, 6994\u20136994 (2019).","journal-title":"Sci. Rep."},{"key":"806_CR13","doi-asserted-by":"crossref","unstructured":"Adeoye, J. et al. Deep Learning Predicts the Malignant-Transformation-Free Survival of Oral Potentially Malignant Disorders. Cancers 13, (2021).","DOI":"10.3390\/cancers13236054"},{"key":"806_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-018-0029-1","volume":"1","author":"A Rajkomar","year":"2018","unstructured":"Rajkomar, A. et al. Scalable and accurate deep learning with electronic health records. npj Digital Med. 1, 1\u201310 (2018).","journal-title":"npj Digital Med."},{"key":"806_CR15","first-page":"1","volume":"1","author":"L-H Hu","year":"2020","unstructured":"Hu, L.-H. et al. Prognostically safe stress-only single-photon emission computed tomography myocardial perfusion imaging guided by machine learning: report from REFINE SPECT. Eur. Heart J. - Cardiovascular Imaging 1, 1\u201310 (2020).","journal-title":"Eur. Heart J. - Cardiovascular Imaging"},{"key":"806_CR16","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1161\/CIRCULATIONAHA.121.057480","volume":"145","author":"S Khurshid","year":"2022","unstructured":"Khurshid, S. et al. ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation. Circulation 145, 122\u2013133 (2022).","journal-title":"Circulation"},{"key":"806_CR17","doi-asserted-by":"publisher","first-page":"1361","DOI":"10.1007\/s11606-012-2077-6","volume":"27","author":"G Elwyn","year":"2012","unstructured":"Elwyn, G. et al. Shared decision making: a model for clinical practice. J. Gen. Intern Med 27, 1361\u20131367 (2012).","journal-title":"J. Gen. Intern Med"},{"key":"806_CR18","doi-asserted-by":"publisher","first-page":"1359","DOI":"10.1161\/CIRCULATIONAHA.121.056742","volume":"144","author":"IJ Neeland","year":"2021","unstructured":"Neeland, I. J., McGuire, D. K. & Sattar, N. Cardiovascular Outcomes Trials for Weight Loss Interventions: Another Tool for Cardiovascular Prevention? Circulation 144, 1359\u20131361 (2021).","journal-title":"Circulation"},{"key":"806_CR19","doi-asserted-by":"publisher","first-page":"e127","DOI":"10.1016\/j.jacc.2017.11.006","volume":"71","author":"K Whelton Paul","year":"2018","unstructured":"Whelton Paul, K. et al. 2017 ACC\/AHA\/AAPA\/ABC\/ACPM\/AGS\/APhA\/ASH\/ASPC\/NMA\/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults. J. Am. Coll. Cardiol. 71, e127\u2013e248 (2018).","journal-title":"J. Am. Coll. Cardiol."},{"key":"806_CR20","first-page":"e1082","volume":"139","author":"SM Grundy","year":"2019","unstructured":"Grundy, S. M. et al. 2018 AHA\/ACC\/AACVPR\/AAPA\/ABC\/ACPM\/ADA\/AGS\/APhA\/ASPC\/NLA\/PCNA Guideline on the Management of Blood Cholesterol: A Report of the American College of Cardiology\/American Heart Association Task Force on Clinical Practice Guidelines. Circulation 139, e1082\u2013e1143 (2019).","journal-title":"Circulation"},{"key":"806_CR21","unstructured":"Kumar, I. E., Venkatasubramanian, S., Scheidegger, C. & Friedler, S. In International Conference on Machine Learning. 5491\u20135500 (PMLR)."},{"key":"806_CR22","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1007\/s12350-014-9957-6","volume":"22","author":"G Romero-Farina","year":"2015","unstructured":"Romero-Farina, G. et al. Warranty periods for normal myocardial perfusion stress SPECT. J. Nucl. Cardiol. 22, 44\u201354 (2015).","journal-title":"J. Nucl. Cardiol."},{"key":"806_CR23","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1016\/j.jacc.2008.04.035","volume":"52","author":"GJ Zoghbi","year":"2008","unstructured":"Zoghbi, G. J., Dorfman, T. A. & Iskandrian, A. E. The Effects of Medications on Myocardial Perfusion. J. Am. Coll. Cardiol. 52, 401\u2013416 (2008).","journal-title":"J. Am. Coll. Cardiol."},{"key":"806_CR24","first-page":"644","volume":"14","author":"PN Azadani","year":"2021","unstructured":"Azadani, P. N. et al. Impact of Early Revascularization on Major Adverse Cardiovascular Events in Relation to Automatically Quantified Ischemia. JACC: Cardiovascular Imaging 14, 644\u2013653 (2021).","journal-title":"JACC: Cardiovascular Imaging"},{"key":"806_CR25","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1016\/j.jacc.2022.04.052","volume":"80","author":"A Rozanski","year":"2022","unstructured":"Rozanski, A. et al. Benefit of Early Revascularization Based on Inducible Ischemia and Left Ventricular Ejection Fraction. J. Am. Coll. Cardiol. 80, 202\u2013215 (2022).","journal-title":"J. Am. Coll. Cardiol."},{"key":"806_CR26","doi-asserted-by":"crossref","unstructured":"Nudi, F., Schillaci, O., Biondi-Zoccai, G. & Iskandrian, A. E. Hybrid cardiac imaging for clinical decision-making. 1 edn, (Springer Nature, 2022).","DOI":"10.1007\/978-3-030-99391-7"},{"key":"806_CR27","first-page":"264423","volume":"122","author":"RJH Miller","year":"2022","unstructured":"Miller, R. J. H. et al. Deep learning coronary artery calcium scores from SPECT\/CT attenuation maps improves prediction of major adverse cardiac events. J. Nucl. Med. 122, 264423 (2022).","journal-title":"J. Nucl. Med."},{"key":"806_CR28","doi-asserted-by":"publisher","first-page":"1010","DOI":"10.1007\/s12350-018-1326-4","volume":"27","author":"PJ Slomka","year":"2020","unstructured":"Slomka, P. J. et al. Rationale and design of the REgistry of Fast Myocardial Perfusion Imaging with NExt generation SPECT (REFINE SPECT). J. Nucl. Cardiol. 27, 1010\u20131021 (2020).","journal-title":"J. Nucl. Cardiol."},{"key":"806_CR29","first-page":"e368","volume":"144","author":"M Gulati","year":"2021","unstructured":"Gulati, M. et al. 2021 AHA\/ACC\/ASE\/CHEST\/SAEM\/SCCT\/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain: A Report of the American College of Cardiology\/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation 144, e368\u2013e454 (2021).","journal-title":"Circulation"},{"key":"806_CR30","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.nuclcard.2004.10.006","volume":"12","author":"PJ Slomka","year":"2005","unstructured":"Slomka, P. J. et al. Automated quantification of myocardial perfusion SPECT using simplified normal limits. J. Nucl. Cardiol. 12, 66\u201377 (2005).","journal-title":"J. Nucl. Cardiol."},{"key":"806_CR31","doi-asserted-by":"crossref","unstructured":"Lee, C., Zame, W. R., Yoon, J. & Van Der Schaar, M. DeepHit: A deep learning approach to survival analysis with competing risks. 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, 2314\u20132321 (2018).","DOI":"10.1609\/aaai.v32i1.11842"},{"key":"806_CR32","first-page":"1","volume":"20","author":"H Kvamme","year":"2019","unstructured":"Kvamme, H., Borgan, O. & Scheel, I. Time-to-event prediction with neural networks and cox regression. J. Mach. Learn. Res. 20, 1\u201330 (2019).","journal-title":"J. Mach. Learn. Res."},{"key":"806_CR33","doi-asserted-by":"crossref","unstructured":"Rios, R. et al. Determining a minimum set of variables for machine learning cardiovascular event prediction: results from REFINE SPECT registry. Cardiovasc Res. 118, 2152\u20132164 (2021).","DOI":"10.1093\/cvr\/cvab236"},{"key":"806_CR34","doi-asserted-by":"publisher","first-page":"105449","DOI":"10.1016\/j.compbiomed.2022.105449","volume":"145","author":"R Rios","year":"2022","unstructured":"Rios, R. et al. Handling missing values in machine learning to predict patient-specific risk of adverse cardiac events: Insights from REFINE SPECT registry. Computers Biol. Med. 145, 105449 (2022).","journal-title":"Computers Biol. Med."},{"key":"806_CR35","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1161\/CIRCULATIONAHA.115.017719","volume":"133","author":"PC Austin","year":"2016","unstructured":"Austin, P. C., Lee, D. S. & Fine, J. P. Introduction to the Analysis of Survival Data in the Presence of Competing Risks. Circulation 133, 601\u2013609 (2016).","journal-title":"Circulation"},{"key":"806_CR36","unstructured":"Lundberg, S. M. & Lee, S.-I. In Advances in Neural Information Processing Systems 30 (eds Guyon, I. et al.) (Curran Associates, Inc., 2017)."},{"key":"806_CR37","doi-asserted-by":"publisher","first-page":"1105","DOI":"10.1002\/sim.4154","volume":"30","author":"H Uno","year":"2011","unstructured":"Uno, H., Cai, T., Pencina, M. J., D\u2019Agostino, R. B. & Wei, L. J. On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Stat. Med 30, 1105\u20131117 (2011).","journal-title":"Stat. Med"},{"key":"806_CR38","doi-asserted-by":"publisher","first-page":"2088","DOI":"10.1177\/0962280213515571","volume":"25","author":"J Lambert","year":"2014","unstructured":"Lambert, J. & Chevret, S. Summary measure of discrimination in survival models based on cumulative\/dynamic time-dependent ROC curves. Stat. Methods Med. Res. 25, 2088\u20132102 (2014).","journal-title":"Stat. Methods Med. Res."},{"key":"806_CR39","first-page":"774","volume":"13","author":"Y Otaki","year":"2020","unstructured":"Otaki, Y. et al. 5-Year Prognostic Value of Quantitative Versus Visual MPI in Subtle Perfusion Defects. JACC: Cardiovascular Imaging 13, 774\u2013785 (2020).","journal-title":"JACC: Cardiovascular Imaging"},{"key":"806_CR40","doi-asserted-by":"publisher","first-page":"W1","DOI":"10.7326\/M14-0698","volume":"162","author":"KGM Moons","year":"2015","unstructured":"Moons, K. G. M. et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann. Intern. Med. 162, W1\u2013W73 (2015).","journal-title":"Ann. Intern. Med."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-023-00806-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-023-00806-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-023-00806-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,19]],"date-time":"2024-10-19T14:41:59Z","timestamp":1729348919000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-023-00806-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,1]]},"references-count":40,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["806"],"URL":"https:\/\/doi.org\/10.1038\/s41746-023-00806-x","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,1]]},"assertion":[{"value":"27 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 March 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 May 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Drs. Berman and Slomka participate in software royalties for QPS software at Cedars-Sinai Medical Center. Dr. Slomka has received research grant support from Siemens Medical Systems and has received consulting honoraria form Synektik, SA. Dr. Berman has served as a consultant for GE Healthcare. Dr. Robert Miller has served as a consultant for Pfizer. Dr. Einstein has served as a consultant to W. L. Gore & Associates. Dr. Di Carli has received research grant support from Spectrum Dynamics and consulting honoraria from Sanofi and GE Healthcare. Dr. Ruddy has received research grant support from GE Healthcare. Dr. Einstein\u2019s institution has received research support from GE Healthcare, International Atomic Energy Agency, Eidos Therapeutics, Roche Medical Systems, Pfizer, Attralus, and W. L. Gore & Associates. Dr. Robert Miller\u2019s institution has received research support from Pfizer. The remaining authors have nothing to disclose.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"78"}}