Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT

Abstract

Deep learning has the potential to improve automatic interpretation of MPI as compared with current clinical methods.

Rationale and design of the REgistry of Fast Myocardial Perfusion Imaging with NExt generation SPECT (REFINE SPECT)

“…Since this registry includes all the imaging datasets, direct analysis of images from the registry in new projects is possible. In one very recent report, Betancur et al developed a deep learning artificial intelligence model for automatic prediction of obstructive disease utilizing data directly from the registry (13). Further analyses can be conducted aimed to describe end-points and disease per subpopulations (e.g., gender and obesity).…”

Deep Learning in Cardiology

“…Additionally these models are stochastic, meaning that every time a network fits the same data but with different initial weights different features are learned. More specifically, in an extensive review [231] of whether the problem of LV/RV segmentation is solved the authors state that although the classification aspect of the problem achieves near perfect results the use of a 'diagnostic black box' can not be integrated in the clinical practice. Miotto et al [240] mention interpretability as one of the main challenges facing the clinical application of deep learning to healthcare.…”

“…Method Application/Notes a CT Lessman 2016 [195] CNN detect coronary calcium using three independently trained CNNs Shadmi 2018 [196] DenseNet compared DenseNet and u-net for detecting coronary calcium Cano 2018 [197] CNN 3D regression CNN for calculation of the Agatston score Wolterink 2016 [198] CNN detect coronary calcium using three CNNs for localization and two CNNs for detection Santini 2017 [199] CNN coronary calcium detection using a seven layer CNN on image patches Lopez 2017 [200] CNN thrombus volume characterization using a 2D CNN and postprocessing Hong 2016 [201] DBN detection, segmentation, classification of abdominal aortic aneurysm using DBN and image patches Liu 2017 [202] CNN left atrium segmentation using a twelve layer CNN and active shape model (STA13) de Vos 2016 [203] CNN 3D localization of anatomical structures using three CNNs, one for each orthogonal plane Moradi 2016 [204] CNN detection of position for a given CT slice using a pretrained VGGnet, handcrafted features and SVM Zheng 2015 [205] Multiple carotid artery bifurcation detection using multi-layer perceptrons and probabilistic boosting-tree Montoya 2018 [206] ResNet 3D reconstruction of cerebral angiogram using a 30 layer ResNet Zreik 2018 [207] CNN, AE identify coronary artery stenosis using CNN for LV segmentation and an AE, SVM for classification Commandeur 2018 [208] CNN quantification of epicardial and thoracic adipose tissue from non-contrast CT Gulsun 2016 [209] CNN extract coronary centerline using optimal path from computed flow field and a CNN for refinement CNN carotid intima media thickness video interpretation using two CNNs with two layers on Ultrasound Tom 2017 [226] GAN IVUS image generation using two GANs (IV11) Wang 2017 [227] CNN breast arterial calcification using a ten layer CNN on mammograms Liu 2017 [228] CNN CAC detection using CNNs on 1768 X-Rays Pavoni 2017 [229] CNN denoising of percutaneous transluminal coronary angioplasty images using four layer CNN Nirschl 2018 [230] CNN trained a patch-based six layer CNN for identifying heart failure in endomyocardial biopsy images Betancur 2018 [231] CNN trained a three layer CNN for obstructive CAD prediction from myocardial perfusion imaging a Results from these imaging modalities are not reported in this review because they were highly variable in terms of the research question they were trying to solve and highly inconsistent in respect with the use of metrics. Additionally all papers use private databases besides…”

“…This model demonstrated better results than AlexNet, Inception and ResNet50. In [231] the authors trained a three layer CNN for the prediction of obstructive CAD stress myocardial perfusion from 1638 patients and compared it with total perfusion deficit. The model computes a probability per vessel during training, while in testing the maximum probabilities per artery are used per patient score.…”

Do we need dedicated cardiac SPECT systems?

“…There is a growing evidence, including large multicenter studies, that these solid-state SPECT systems demonstrate excellent diagnostic and prognostic utility in cardiac imaging. [6][7][8] In the last few years, the equipment companies have also introduced general-purpose CZT-based SPECT systems to enable applications other than cardiac imaging. These cameras benefit from improved energy and image resolution due to the CZT detectors and have demonstrated improved image quality in early clinical implementation as compared to general-purpose Angerbased systems.…”