Direct Risk Assessment From Myocardial Perfusion Imaging Using Explainable Deep Learning

Singh, Ananya; Miller, Robert J.H.; Otaki, Yuka; Kavanagh, Paul; Hauser, M Timothy; Tzolos, Evangelos; Kwieciński, Jacek; Kriekinge, Serge Van; Cui, Wei; Sharir, Tali; Fish, Mathews B.; Ruddy, Terrence D.; Kaufmann, Philipp; Sinusas, Albert J.; Miller, Edward J.; Bateman, Timothy M.; Dorbala, Sharmila; Carli, Marcelo Di; Liang, Jiaming; Huang, Cathleen; Han, Donghee; Dey, Damini; Berman, Daniel S.; Slomka, Piotr J.

doi:10.1016/j.jcmg.2022.07.017

Cited by 19 publications

(12 citation statements)

References 33 publications

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“…Singh et al [ 29 ] developed an explainable deep learning model to predict nonfatal myocardial infarction (MI) or death, which also provides highlighted image regions related to obstructive CAD. The study included 20,401 patients, who went under SPECT MPI procedure for training and internal testing purposes and 9019 patients were added from external testing group gathered from two different sites.…”

Section: Resultsmentioning

confidence: 99%

Deep learning-enhanced nuclear medicine SPECT imaging applied to cardiac studies

et al. 2023

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Deep learning (DL) has a growing popularity and is a well-established method of artificial intelligence for data processing, especially for images and videos. Its applications in nuclear medicine are broad and include, among others, disease classification, image reconstruction, and image de-noising. Positron emission tomography (PET) and single-photon emission computerized tomography (SPECT) are major image acquisition technologies in nuclear medicine. Though several studies have been conducted to apply DL in many nuclear medicine domains, such as cancer detection and classification, few studies have employed such methods for cardiovascular disease applications. The present paper reviews recent DL approaches focused on cardiac SPECT imaging. Extensive research identified fifty-five related studies, which are discussed. The review distinguishes between major application domains, including cardiovascular disease diagnosis, SPECT attenuation correction, image denoising, full-count image estimation, and image reconstruction. In addition, major findings and dominant techniques employed for the mentioned task are revealed. Current limitations of DL approaches and future research directions are discussed.

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Section: Resultsmentioning

confidence: 99%

Deep learning-enhanced nuclear medicine SPECT imaging applied to cardiac studies

et al. 2023

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Section: Examples Of Deep Learning In Cardiovascular Imagingmentioning

confidence: 99%

“…Predict major adverse cardiovascular events directly from single-photon emission computed tomography stress polar perfusion maps. 3 High-throughput phenotyping Deeply phenotype a large cohort of patients with left ventricular hypertrophy by echocardiography. 10 exposing the model to new examples and adjusting model parameters via gradient descent to improve performance over time.…”

Section: Risk Predictionmentioning

confidence: 99%

“…The following survey of notable efforts in DL for different modalities in CVI is not meant to be exhaustive, but rather to highlight practical examples of previously discussed concepts (Table 2 [2][3][4][5][6][7][8][9][10][11][15][16][17][18]20,[24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39] ).…”

Section: Examples Of Deep Learning In Cardiovascular Imagingmentioning

confidence: 99%

“…1 In CVI, DL applications have been developed for echocardiography, cardiac magnetic resonance (CMR) imaging, cardiac computed tomography (CCT), and cardiac nuclear imaging. From image plane prescription on CMR 2 to outcome prediction directly from myocardial perfusion polar maps, 3,4 DL models have been used to assist with image planning, simplify image processing or acquisition, automate measurements, generate reports, augment interpretation, predict outcomes, and deeply phenotype cohorts (Box 2,3,[5][6][7][8][9][10][11][12] ). As DL systems become more ubiquitous in CVI, clinicians should have a general understanding of the underlying methodology.…”

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Deep Learning for Cardiovascular Imaging

Wehbe,

Katsaggelos,

Hammond

et al. 2023

JAMA Cardiol

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ImportanceArtificial intelligence (AI), driven by advances in deep learning (DL), has the potential to reshape the field of cardiovascular imaging (CVI). While DL for CVI is still in its infancy, research is accelerating to aid in the acquisition, processing, and/or interpretation of CVI across various modalities, with several commercial products already in clinical use. It is imperative that cardiovascular imagers are familiar with DL systems, including a basic understanding of how they work, their relative strengths compared with other automated systems, and possible pitfalls in their implementation. The goal of this article is to review the methodology and application of DL to CVI in a simple, digestible fashion toward demystifying this emerging technology.ObservationsAt its core, DL is simply the application of a series of tunable mathematical operations that translate input data into a desired output. Based on artificial neural networks that are inspired by the human nervous system, there are several types of DL architectures suited to different tasks; convolutional neural networks are particularly adept at extracting valuable information from CVI data. We survey some of the notable applications of DL to tasks across the spectrum of CVI modalities. We also discuss challenges in the development and implementation of DL systems, including avoiding overfitting, preventing systematic bias, improving explainability, and fostering a human-machine partnership. Finally, we conclude with a vision of the future of DL for CVI.Conclusions and RelevanceDeep learning has the potential to meaningfully affect the field of CVI. Rather than a threat, DL could be seen as a partner to cardiovascular imagers in reducing technical burden and improving efficiency and quality of care. High-quality prospective evidence is still needed to demonstrate how the benefits of DL CVI systems may outweigh the risks.

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A scoping review of interpretability and explainability concerning artificial intelligence methods in medical imaging

Champendal,

Müller,

Prior

et al. 2023

European Journal of Radiology

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Direct Risk Assessment From Myocardial Perfusion Imaging Using Explainable Deep Learning

Cited by 19 publications

References 33 publications

Deep learning-enhanced nuclear medicine SPECT imaging applied to cardiac studies

Deep learning-enhanced nuclear medicine SPECT imaging applied to cardiac studies

Deep Learning for Cardiovascular Imaging

A scoping review of interpretability and explainability concerning artificial intelligence methods in medical imaging

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