Machine Learning Algorithms to Distinguish Myocardial Perfusion SPECT Polar Maps

Filho, Érito Marques de Souza; Fernandes, Fernando de Amorim; Wiefels, Christiane; Carvalho, Lucas Nunes Dalbonio de; Santos, Tadeu Francisco dos; Santos, Alair Augusto Sarmet Moreira Damas dos; Mesquita, Evandro Tinoco; Seixas, Flávio Luiz; Chow, Benjamin J.W.; Mesquita, Cláudio Tinoco; Gismondi, Ronaldo Altenburg

doi:10.3389/fcvm.2021.741667

Cited by 10 publications

(7 citation statements)

References 49 publications

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“…Souza Filho et al [ 38 ] explored the potential of developing different ML models like Adaptive Boosting (AB), Gradient Boosting (BG), Random Forest (RF), and Extreme Gradient Boosting (XGB) to find the ideal model for efficient differentiation between normal and abnormal cases of SPECT Polar Maps labelled by human readers. The stress and rest conditions included a total of 1007 Polar Maps.…”

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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“…The sensitivity and specificity of the 8 ML methods in the test set were inconsistent, showing different advantages. We used a 10-fold cross-validation approach for the training data (42)(43)(44)(45). Ten-fold cross-validation divides the training set into 10 subsamples; 1 single subsample is retained as the data to validate the model, and the other 9 samples are used for training.…”

Section: Discussionmentioning

confidence: 99%

Detecting obstructive coronary artery disease with machine learning: rest-only gated single photon emission computed tomography myocardial perfusion imaging combined with coronary artery calcium score and cardiovascular risk factors

Liu¹,

Yu²,

Zhang³

et al. 2023

Quant Imaging Med Surg

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Background: The rest-only single photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) has low diagnostic performance for obstructive coronary artery disease (CAD). Coronary artery calcium score (CACS) is strongly associated with obstructive CAD. The aim of this study was to investigate the performance of rest-only gated SPECT MPI combined with CACS and cardiovascular risk factors in diagnosing obstructive CAD through machine learning (ML). Methods: We enrolled 253 suspected CAD patients who underwent the 1-stop rest-only SPECT MPI and computed tomography (CT) scan due to stress test-related contraindications. Myocardial perfusion and wall motion were assessed using quantitative perfusion SPECT + quantitative gated SPECT (QPS + QGS) automated quantification software. The Agatston algorithm was used to calculate CACS. The clinical data of patients, including cardiovascular risk factors, were collected. Based on feature selection and clinical experience, 8 factors were identified as modeling variables. Subsequently, patients were divided randomly into 2 groups: the training (70%) and test (30%) groups. The performance of 8 supervised ML algorithms was evaluated in the training and test groups.Results: Obstructive CAD was diagnosed by coronary angiography in 94 (37.2%, 94/253) patients. In the training group, the area under the receiver operator characteristic (ROC) curve (AUC) of the random forest was the highest, and the AUCs of Logistic, extreme gradient boosting (XGBoost), support vector machine (SVM), and adaptive boosting (AdaBoost) were all above 0.9. In the test group, the AUC of recursive partitioning and regression trees (Rpart) was the highest (0.911). Rpart and Naïve Bayes had the highest accuracy (0.840). Rpart had a sensitivity and specificity of 0.851 and 0.821, respectively; Naïve Bayes had a sensitivity and specificity of 0.809 and 0.893, respectively. Next was Logistic, with an accuracy of 0.827, a sensitivity of 0.872, and a specificity of 0.750. The random forest and XGBoost algorithms also had high accuracy, which was 0.813 for each algorithm.Conclusions: Rest-only SPECT MPI combined with CACS and cardiovascular risk factors using an ML algorithm to detect obstructive CAD is feasible. Among the algorithms validated in the test group, Rpart, ^ ORCID: 0000-0003-2859-8625. Resting image acquisition and analysis99m Technetium-sestamibi ( 99m Tc-MIBI) was administered intravenously at rest. Scanning was started after 60-90 minutes of rest. A dual-head 90 o gamma camera (Symbia T16; Siemens Medical Systems, Erlangen, Germany) equipped with a parallel-hole collimator with low energy and high resolution was used for image acquisition.Naïve Bayes, XGBoost, Logistic, and random forest are all highly accurate for diagnosing obstructive CAD.The application of ML in resting MPI and CACS may be used for screening obstructive CAD.

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“…The applied ANN outperformed with 0.92% AUC. Filho et al in [20] compared four ensemble ML algorithms, namely: adaptive boosting (AB), gradient boosting (GB), eXtreme gradient boosting (XGB), and RF for the classification of SPECT images into normal and abnormal. The proposed model was evaluated with the utilization of the cross-validation approach and achieved an AUC of 0.853%, accuracy of 0.938%, and sensitivity of 0.963%.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Automated Diagnosis for Coronary Artery Disease Using SPECT-MPI Images

Παπανδριανός

Feleki

Papageorgiou

et al. 2022

JCM

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(1) Background: Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is a long-established estimation methodology for medical diagnosis using image classification illustrating conditions in coronary artery disease. For these procedures, convolutional neural networks have proven to be very beneficial in achieving near-optimal accuracy for the automatic classification of SPECT images. (2) Methods: This research addresses the supervised learning-based ideal observer image classification utilizing an RGB-CNN model in heart images to diagnose CAD. For comparison purposes, we employ VGG-16 and DenseNet-121 pre-trained networks that are indulged in an image dataset representing stress and rest mode heart states acquired by SPECT. In experimentally evaluating the method, we explore a wide repertoire of deep learning network setups in conjunction with various robust evaluation and exploitation metrics. Additionally, to overcome the image dataset cardinality restrictions, we take advantage of the data augmentation technique expanding the set into an adequate number. Further evaluation of the model was performed via 10-fold cross-validation to ensure our model’s reliability. (3) Results: The proposed RGB-CNN model achieved an accuracy of 91.86%, while VGG-16 and DenseNet-121 reached 88.54% and 86.11%, respectively. (4) Conclusions: The abovementioned experiments verify that the newly developed deep learning models may be of great assistance in nuclear medicine and clinical decision-making.

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Machine Learning Algorithms to Distinguish Myocardial Perfusion SPECT Polar Maps

Cited by 10 publications

References 49 publications

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

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

Detecting obstructive coronary artery disease with machine learning: rest-only gated single photon emission computed tomography myocardial perfusion imaging combined with coronary artery calcium score and cardiovascular risk factors

Deep Learning-Based Automated Diagnosis for Coronary Artery Disease Using SPECT-MPI Images

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