Improved diagnostic accuracy for myocardial perfusion imaging using artificial neural networks on different input variables including clinical and quantification data

Rahmani, Reza; Niazi, P.; Naseri, Maryam; Neishabouri, M.; Farzanefar, Saeed; Eftekhari, Mohammad; Derakhshan, Farhang; Mollazadeh, Reza; Meysami, Ali Pasha; Abbasi, Moslem

doi:10.1016/j.remnie.2019.04.005

Cited by 10 publications

(9 citation statements)

References 20 publications

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“…Visual assessment of SPECT heart perfusion images faces several challenges, including the lack of reproducibility due to the intra-observer and interobserver variability, dependency on nuclear medicine expert or radiologist experience, and increased time and cost of the interpretation process. Machine learning and Deep learning techniques exhibited promising potential for the detection and classi cation of coronary artery disease from SPECT MPI images compared with other approaches, including human observer diagnosis and quantitative analysis of perfusion defects [4,6,[9][10][11][12][13][14][15][18][19][20][21]. A comparison to the related studies is listed in Table 5.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Analysis of Polar Maps from SPECT Myocardial Perfusion Imaging for Prediction of Coronary Artery Disease

Zahiri

Asgari

Razavi-Ratki

et al. 2021

Preprint

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Purpose: This study aimed to investigate the diagnostic accuracy of deep convolutional neural networks for classifying the polar map images in Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) by considering the physician’s diagnosis as reference.Methods: 3318 images of stress and rest polar maps related to patients (67% women and 33% men) who underwent 99mTc-sestamibi MPI were collected. The images were manually labeled with normal and abnormal labels according to the doctor’s diagnosis reports. The proposed deep learning model was trained using stress and rest polar maps and evaluated for prediction of obstructive disease in a stratified 5-fold cross-validation procedure.Results: The mean values of accuracy, sensitivity, accuracy, specificity, f1 score, and the area under the roc curve were 0.7562, 0.7856, 0.5748, 0.7434, 0.6646, and, 0.8450, respectively over 5 folds using both stress and rest scans. The inclusion of rest perfusion maps significantly improved AUC of the deep learning model (AUC: 0.845; 95% CI: 0.832-0.857), compared with using stress polar maps only (AUC: 0.827; 95% CI: 0.814-0.840); P < 0.05.Conclusion: The results of the present work reveal the possible applications of deep learning for polar map images classification in SPECT MPI.

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Section: Discussionmentioning

confidence: 99%

Deep Learning Analysis of Polar Maps from SPECT Myocardial Perfusion Imaging for Prediction of Coronary Artery Disease

Zahiri

Asgari

Razavi-Ratki

et al. 2021

Preprint

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“…Various ML algorithms based on stress imaging, particularly single-photon emission computed tomography (SPECT), have been devised to facilitate the prediction of CAD. These models combined the clinical and demographic characteristics with the quantitative variables, as evaluated via SPECT to better predict CAD compared with the visual interpretation or quantitative variables alone [ 39 , 40 , 41 , 42 , 43 , 44 ]. More details about the parameters used to develop these models have been provided in Section 4 , and a summary of the study results is included in Table 1 .…”

Section: Risk Prediction Models and Imaging Modalities For Estimating...mentioning

confidence: 99%

Current and Future Applications of Artificial Intelligence in Coronary Artery Disease

et al. 2022

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Cardiovascular diseases (CVDs) carry significant morbidity and mortality and are associated with substantial economic burden on healthcare systems around the world. Coronary artery disease, as one disease entity under the CVDs umbrella, had a prevalence of 7.2% among adults in the United States and incurred a financial burden of 360 billion US dollars in the years 2016–2017. The introduction of artificial intelligence (AI) and machine learning over the last two decades has unlocked new dimensions in the field of cardiovascular medicine. From automatic interpretations of heart rhythm disorders via smartwatches, to assisting in complex decision-making, AI has quickly expanded its realms in medicine and has demonstrated itself as a promising tool in helping clinicians guide treatment decisions. Understanding complex genetic interactions and developing clinical risk prediction models, advanced cardiac imaging, and improving mortality outcomes are just a few areas where AI has been applied in the domain of coronary artery disease. Through this review, we sought to summarize the advances in AI relating to coronary artery disease, current limitations, and future perspectives.

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“…Research on deploying ML in supporting CAD diagnosis using SPECT MPI data has been noticed for a decade. From the beginning, the typical ML algorithms such as support vector machine (SVM) [13], artificial neural network (ANN) [14], and ensemble learning [15,16] have been commonly investigated. However, since these ML algorithms require features engineering processes from the raw data (clinical data and SPECT MPI data), ML-based diagnostic models' performance thoroughly depends on the quality of the features engineering processes.…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning for Disease Diagnosis from Myocardial Perfusion SPECT營maging

Phung¹,

Thành²,

Trung³

et al. 2022

Computers, Materials &Amp; Continua

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Coronary artery disease (CAD) is one of the most common pathological conditions and the major global cause of death. Myocardial perfusion imaging (MPI) using single-photon emission computed tomography (SPECT) is a non-invasive method and plays an essential role in diagnosing CAD. However, there is currently a shortage of doctors who can diagnose using SPECT-MPI in developing countries, especially Vietnam. Research on deploying machine learning and deep learning in supporting CAD diagnosis has been noticed for a long time. However, these methods require a large dataset and are therefore time-consuming and labor-intensive. This study aims to develop a cost-effective and high-performance CAD classification model to support doctors in these countries. In this paper, we propose a transfer learning framework for a multi-stage training process with different learning rates. The process consists of two training stages: a warming up stage in which all layers of a pre-trained model (on ImageNet dataset) are frozen; and a finetuning stage in which a small amount of the top layers are unfrozen and then retrained with a lower learning rate. The dataset for this study consists of the polar maps from 218 patients. Various popular CNN-based pre-trained models have been investigated, and ResNet152V2-based model has obtained the highest performances with an accuracy of 95.5%, area under the receiver operating characteristic curve (AUC) score 0.932, sensitivity 94.4%, precision 96.4%, and F1-score 95.2%. These performances are competitive or even better than all state-of-the-art approaches in terms of classification accuracy and sensitivity. We also apply the class activation mapping technique to help explain the model's predictions and increase the model's reliability, proving capable of assisting the SPECT image readers in the CAD diagnosis.

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Improved diagnostic accuracy for myocardial perfusion imaging using artificial neural networks on different input variables including clinical and quantification data

Cited by 10 publications

References 20 publications

Deep Learning Analysis of Polar Maps from SPECT Myocardial Perfusion Imaging for Prediction of Coronary Artery Disease

Deep Learning Analysis of Polar Maps from SPECT Myocardial Perfusion Imaging for Prediction of Coronary Artery Disease

Current and Future Applications of Artificial Intelligence in Coronary Artery Disease

Transfer Learning for Disease Diagnosis from Myocardial Perfusion SPECT營maging

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