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OtherAI/Advanced Image Analysis

Explainable Deep Learning Improves Physician Interpretation of Myocardial Perfusion Imaging

Robert J.H. Miller, Keiichiro Kuronuma, Ananya Singh, Yuka Otaki, Sean Hayes, Panithaya Chareonthaitawee, Paul Kavanagh, Tejas Parekh, Balaji K Tamarappoo, Tali Sharir, Andrew J Einstein, Mathews B Fish, Terrence D Ruddy, Philipp A. Kaufmann, Albert J Sinusas, Edward J Miller, Timothy Bateman, Sharmila Dorbala, Marcelo F Di Carli, Sebastien Cadet, Joanna X Liang, Damini Dey, Daniel S. Berman and Piotr J. Slomka
Journal of Nuclear Medicine May 2022, jnumed.121.263686; DOI: https://doi.org/10.2967/jnumed.121.263686
Robert J.H. Miller
1 Cedars-Sinai Medical Center, United States;
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Keiichiro Kuronuma
1 Cedars-Sinai Medical Center, United States;
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Ananya Singh
1 Cedars-Sinai Medical Center, United States;
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Yuka Otaki
1 Cedars-Sinai Medical Center, United States;
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Sean Hayes
1 Cedars-Sinai Medical Center, United States;
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Panithaya Chareonthaitawee
2 Mayo Clinic;
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Paul Kavanagh
1 Cedars-Sinai Medical Center, United States;
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Tejas Parekh
1 Cedars-Sinai Medical Center, United States;
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Balaji K Tamarappoo
1 Cedars-Sinai Medical Center, United States;
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Tali Sharir
3 Assuta Medical Centers;
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Andrew J Einstein
4 Columbia University Irving Medical Center and New York-Presbyterian Hospital;
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Mathews B Fish
5 Sacred Heart Medical Center;
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Terrence D Ruddy
6 University of Ottawa Heart Institute;
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Philipp A. Kaufmann
7 University Hospital Zurich;
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Albert J Sinusas
8 Yale University School of Medicine;
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Edward J Miller
9 Yale University;
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Timothy Bateman
10 Cardiovascular Imaging Technologies LLC;
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Sharmila Dorbala
11 Brigham and Women's Hospital
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Marcelo F Di Carli
11 Brigham and Women's Hospital
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Sebastien Cadet
1 Cedars-Sinai Medical Center, United States;
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Joanna X Liang
1 Cedars-Sinai Medical Center, United States;
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Damini Dey
1 Cedars-Sinai Medical Center, United States;
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Daniel S. Berman
1 Cedars-Sinai Medical Center, United States;
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Piotr J. Slomka
1 Cedars-Sinai Medical Center, United States;
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Abstract

Rationale: Artificial intelligence may improve accuracy of myocardial perfusion imaging (MPI) but will likely be implemented as an aid to physician interpretation rather than an autonomous tool. Deep learning (DL) has high standalone diagnostic accuracy for obstructive coronary artery disease (CAD), but its influence on physician interpretation is unknown. We assessed whether access to explainable DL predictions improves physician interpretation of MPI. Methods: We selected a representative cohort of patients who underwent MPI with reference invasive coronary angiography. Obstructive CAD, defined as stenosis ≥ 50% in the left main artery or ≥70% in other coronary segments, was present in half of patients. We utilized an explainable DL model (CAD-DL), which was previously developed in a separate population from different sites. Three physicians interpreted studies first with clinical history, stress, and quantitative perfusion, then with all the data plus the DL results. Diagnostic accuracy was assessed using area under the receiver-operating characteristic curve (AUC). Results: In total, 240 patients were included with median age 65 (IQR 58 – 73). The diagnostic accuracy of physician interpretation with CAD-DL (AUC 0.779) was significantly higher compared to physician interpretation without CAD-DL (AUC 0.747, P = 0.003) and stress total perfusion deficit (AUC 0.718, p<0.001). With matched specificity, CAD-DL had higher sensitivity when operating autonomously compared to readers without DL results (p<0.001), but not compared to readers interpreting with DL results (P = 0.122). All readers had numerically higher accuracy with the use of CAD-DL, with AUC improvement 0.02 to 0.05, and interpretation with DL resulted in overall net reclassification improvement of 17.5% (95% CI 9.8% – 24.7%, p<0.001). Conclusion: Explainable DL predictions lead to meaningful improvements in physician interpretation; however, the improvement varied across the readers reflecting the acceptance of this new technology. This technique could be implemented as an aid to physician diagnosis, improving the diagnostic accuracy of MPI.

  • Cardiology (clinical)
  • SPECT
  • Other
  • Artificial intelligence
  • deep learning
  • implementation
  • Copyright © 2022 by the Society of Nuclear Medicine and Molecular Imaging, Inc.
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Journal of Nuclear Medicine: 64 (12)
Journal of Nuclear Medicine
Vol. 64, Issue 12
December 1, 2023
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Explainable Deep Learning Improves Physician Interpretation of Myocardial Perfusion Imaging
Robert J.H. Miller, Keiichiro Kuronuma, Ananya Singh, Yuka Otaki, Sean Hayes, Panithaya Chareonthaitawee, Paul Kavanagh, Tejas Parekh, Balaji K Tamarappoo, Tali Sharir, Andrew J Einstein, Mathews B Fish, Terrence D Ruddy, Philipp A. Kaufmann, Albert J Sinusas, Edward J Miller, Timothy Bateman, Sharmila Dorbala, Marcelo F Di Carli, Sebastien Cadet, Joanna X Liang, Damini Dey, Daniel S. Berman, Piotr J. Slomka
Journal of Nuclear Medicine May 2022, jnumed.121.263686; DOI: 10.2967/jnumed.121.263686

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Explainable Deep Learning Improves Physician Interpretation of Myocardial Perfusion Imaging
Robert J.H. Miller, Keiichiro Kuronuma, Ananya Singh, Yuka Otaki, Sean Hayes, Panithaya Chareonthaitawee, Paul Kavanagh, Tejas Parekh, Balaji K Tamarappoo, Tali Sharir, Andrew J Einstein, Mathews B Fish, Terrence D Ruddy, Philipp A. Kaufmann, Albert J Sinusas, Edward J Miller, Timothy Bateman, Sharmila Dorbala, Marcelo F Di Carli, Sebastien Cadet, Joanna X Liang, Damini Dey, Daniel S. Berman, Piotr J. Slomka
Journal of Nuclear Medicine May 2022, jnumed.121.263686; DOI: 10.2967/jnumed.121.263686
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Keywords

  • Cardiology (clinical)
  • SPECT
  • Other
  • artificial intelligence
  • deep learning
  • implementation
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