Photo credit: Image created by Singh et al., Cedars-Sinai Medical Center, Los Angeles, CA.

Reston, VA (Embargoed until 6:15 p.m. EDT, Friday, June 11, 2021) – An advanced artificial intelligence technique known as deep learning can predict major adverse cardiac events more accurately than current standard imaging protocols, according to the Society presented examinations for nuclear medicine and molecular imaging 2021 annual meeting. Using data from a registry of more than 20,000 patients, the researchers developed a novel deep learning network that has the potential to enable patients to individually predict their annual risk of adverse events such as heart attack or death.

Deep learning is a subset of artificial intelligence that mimics how the human brain works to process data. Deep learning algorithms use multiple layers of “neurons” or non-linear processing units to learn representations and identify latent features relevant to a particular task in order to understand multiple types of data. It can be used for tasks such as cardiovascular disease prediction and segmentation of the lungs, among others.

The study used information from the largest available multicenter SPECT data set, the REgistry of Fast myocardial perfusion Imaging with NExt generation SPECT (REFINE SPECT) with 20,401 patients. All patients in the registry underwent SPECT-MPI and a deep learning network was used to assess how likely they were to experience a major adverse cardiac event during the follow-up period. The subjects were observed for an average of 4.7 years.

The deep learning network highlighted regions of the heart at risk of major adverse cardiac events and provided a risk assessment in less than a second during the test. Patients with the highest deep learning scores had an annual rate of major adverse cardiac events of 9.7 percent, a 10.2-fold increased risk compared to those with the lowest scores.

“These results show that artificial intelligence could be incorporated into standard clinical workplaces to help clinicians accurately and quickly assess the risk of patients undergoing SPECT-MPI scans,” said Ananya Singh, MS, a research software engineer at Slomka Lab Cedars-Sinai Medical Center in Los Angeles, California. “This work demonstrates the potential benefit of incorporating artificial intelligence techniques into standard imaging protocols to aid readers in stratifying risk.”

Summary 50. “Improved risk assessment of myocardial SPECT through deep learning: Report from the REFINE SPECT registry”, Ananya Singh, Yuka Otaki, Paul Kavanagh, Serge Van Kriekinge, Wei Chih-Chun, Tejas Parekh, Joanna Liang, Damini Dey, Daniel Berman and Piotr Slomka, Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California; Robert Miller, Department of Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada and Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California; Tali Sharir, Department of Nuclear Cardiology, Assuta Medical Centers, Tel Aviv, and Ben Gurion University of the Negev, Beer Sheba, Israel; Andrew Einstein, Department of Cardiology, Department of Medicine, and Department of Radiology, Columbia University, Irving Medical Center, and New York-Presbyterian Hospital, New York, New York; Mathews Fish, Oregon Heart and Vascular Institute, Sacred Heart Medical Center, Springfield, Oregon; Terrence Ruddy, Department of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada; Philipp Kaufmann, Clinic for Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Zurich, Switzerland; Albert Sinusas and Edward Miller, Department of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine New Haven, Connecticut; Timothy Bateman, Department of Imaging, Cardiovascular Imaging Technologies LLC, Kansas City, Missouri; Sharmila Dorbala and Marcelo Di Carli, Department of Radiology, Department of Nuclear Medicine and Molecular Imaging, Brigham and Women’s Hospital, Boston, Massachusetts.


All abstracts of the SNMMI annual conference 2021 are available online at https: //sb.snmjournale.Organization/Content/62 /Supplement_1.

About the Society for Nuclear Medicine and Molecular Imaging

The Society of Nuclear Medicine and Molecular Imaging (SNMMI) is an international scientific and medical organization dedicated to advancing nuclear medicine and molecular imaging results.

SNMMI members set the standard for molecular imaging and nuclear medicine practice by creating guidelines, sharing information through journals and meetings, and advocating key issues affecting the research and practice of molecular imaging and therapy. Further information is available at http: // .


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