Diagnostic accuracy of myocardial perfusion imaging with czt technology. Systemic review and meta-analysis of comparison with invasive coronary angiography

OBJECTIVES: This study sought to summarize the evidence on stress myocardial perfusion imaging (MPI) using cadmium-zinc-telluride (CZT) technology for the diagnosis of obstructive coronary artery disease (CAD). The CZT cameras are newly introduced, and comparative data with the conventional Anger technology (Anger-MPI) are lacking. BACKGROUND: The diagnostic accuracy of Anger-MPI for detection of angiographically significant CAD is well established; however, less evidence is available on the diagnostic accuracy of CZT-MPI. METHODS: Clinical studies comparing CZT-MPI and invasive coronary angiography were systematically searched and abstracted. Calculations of diagnostic accuracy, including sensitivity, specificity, likelihood ratios, and diagnostic odds ratio, were obtained with fixed and random effects, reporting point estimates and 95% confidence intervals. RESULTS: Based on our search, a total of 16 studies (N = 2,092) were included. The sensitivity of CZT-MPI was 0.84 (95% confidence interval [CI]: 0.78 to 0.89), whereas the specificity of 0.69 (95% CI: 0.62 to 0.76) was significantly reduced. The positive likelihood ratio was 2.73 (95% CI: 2.21 to 3.39), the negative likelihood ratio was 0.24 (95% CI: 0.17 to 0.31), and the diagnostic odds ratio was 11.93 (95% CI: 7.84 to 17.42). At subgroup and meta-regression analyses, the diagnostic accuracy between D-SPECT and Discovery cameras was similar (p = 0.711) and not impacted upon by smaller sample size studies (p = 0.573). CONCLUSIONS: CZT-MPI has satisfactory sensitivity for angiographically significant CAD, but its suboptimal specificity warrants further development and research

Image-Based Cardiac Diagnosis With Machine Learning: A Review

Cardiac imaging plays an important role in the diagnosis of cardiovascular disease (CVD). Until now, its role has been limited to visual and quantitative assessment of cardiac structure and function. However, with the advent of big data and machine learning, new opportunities are emerging to build artificial intelligence tools that will directly assist the clinician in the diagnosis of CVDs. This paper presents a thorough review of recent works in this field and provide the reader with a detailed presentation of the machine learning methods that can be further exploited to enable more automated, precise and early diagnosis of most CVDs

On the Investigation of Biological Phenomena through Computational Intelligence

This paper is largely devoted for building a novel approach which is able to explain biological phenomena like splicing promoter gene identification disease and disorder identification and to acquire and exploit biological data This paper also presents an overview on the artificial neural network based computational intelligence technique to infer and analyze biological information from wide spectrum of complex problems Bioinformatics and computational intelligence are new research area which integrates many core subjects such as chemistry biology medical science mathematics computer and information science Since most of the problems in bioinformatics are inherently hard ill defined and possesses overlapping boundaries Neural networks have proved to be effective in solving those problems where conventional com-putation tools failed to provide solution Our experiments demonstrate the endeavor of biological phenomena as an effec-tive description for many intelligent applications Having a computational tool to predict genes and other meaningful in-formation is therefore of great value and can save a lot of expensive and time consuming experiments for biologists This paper will focus on issues related to design methodology comprising neural network to analyze biological information and investigate them for powerful application

Applying Transfer Learning in Classification of Ischemia from Myocardial Polar Maps in PET Cardiac Perfusion Imaging

Introduction: Ischemia is defined as the restriction of blood flow to a body organ, such as the heart, resulting in a cutback in oxygen supply. Myocardial ischemia is characterized by an imbalance between myocardial oxygen supply and demand, causing cardiac dysfunction, arrhythmia, myocardial infarction, and sudden death. Positron emission tomography myocardial perfusion imaging (PET-MPI) is an examination for accurately evaluating blood circulation to the heart muscle at stress and rest. Images obtained from this technique can be interpreted by experts or potentially classified by deep learning for the diagnosis of cardiac ischemia. Although deep learning has proved to be effective for medical image classification tasks, the challenge of small medical image datasets for model training remains to exist. Transfer learning is a state-of-the-art technique for resolving this challenge by utilizing pre-trained models for a new task. Pre-trained models are deep convolutional neural networks (CNNs) trained on a vast dataset, such as ImageNet, capable of transferring learned weights to a new classification problem. Objective: To study the effectiveness of image classification using transfer learning and benchmarking pre-trained CNN models for the classification of myocardial ischemia from myocardial polar maps in PET 15O-H2O cardiac perfusion imaging. Subject and methods: 138 JPEG polar maps from a 15O-H2O stress perfusion test from patients classified as ischemic or non-ischemic were used. Experiments for comparing a total of 20 pre-trained CNN models were performed. The results were compared against a custom CNN developed on the same dataset. Python programming language and its relevant libraries for deep learning were used. Results and discussion: Pre-trained models showed reliable performance compared to a custom-built CNN. VGG19, VGG16, DenseNet169, and Xception were superior among all pre-trained models. Ensemble learning improved overall performance, closest to the clinical interpretation level

Comparing the prognostic value of stress myocardial perfusion imaging by conventional and cadmium-zinc telluride single-photon emission computed tomography through a machine learning approach

We compared the prognostic value of myocardial perfusion imaging (MPI) by conventional- (C-) single-photon emission computed tomography (SPECT) and cadmium-zinc-telluride- (CZT-) SPECT in a cohort of patients with suspected or known coronary artery disease (CAD) using machine learning (ML) algorithms. A total of 453 consecutive patients underwent stress MPI by both C-SPECT and CZT-SPECT. The outcome was a composite end point of all-cause death, cardiac death, nonfatal myocardial infarction, or coronary revascularization procedures whichever occurred first. ML analysis performed through the implementation of random forest (RF) and k-nearest neighbors (KNN) algorithms proved that CZT-SPECT has greater accuracy than C-SPECT in detecting CAD. For both algorithms, the sensitivity of CZT-SPECT (96% for RF and 60% for KNN) was greater than that of C-SPECT (88% for RF and 53% for KNN). A preliminary univariate analysis was performed through Mann-Whitney tests separately on the features of each camera in order to understand which ones could distinguish patients who will experience an adverse event from those who will not. Then, a machine learning analysis was performed by using Matlab (v. 2019b). Tree, KNN, support vector machine (SVM), Naïve Bayes, and RF were implemented twice: first, the analysis was performed on the as-is dataset; then, since the dataset was imbalanced (patients experiencing an adverse event were lower than the others), the analysis was performed again after balancing the classes through the Synthetic Minority Oversampling Technique. According to KNN and SVM with and without balancing the classes, the accuracy (p value = 0.02 and p value = 0.01) and recall (p value = 0.001 and p value = 0.03) of the CZT-SPECT were greater than those obtained by C-SPECT in a statistically significant way. ML approach showed that although the prognostic value of stress MPI by C-SPECT and CZT-SPECT is comparable, CZT-SPECT seems to have higher accuracy and recall

Convolutional neural networks for detection of transthyretin amyloidosis in 2D scintigraphy images

Background Transthyretin amyloidosis (ATTR) is a progressive disease which can be diagnosed non-invasively using bone avid [Tc-99m]-labeled radiotracers. Thus, ATTR is also an occasional incidental finding on bone scintigraphy. In this study, we trained convolutional neural networks (CNN) to automatically detect and classify ATTR from scintigraphy images. The study population consisted of 1334 patients who underwent [Tc-99m]-labeled hydroxymethylene diphosphonate (HMDP) scintigraphy and were visually graded using Perugini grades (grades 0-3). A total of 47 patients had visual grade >= 2 which was considered positive for ATTR. Two custom-made CNN architectures were trained to discriminate between the four Perugini grades of cardiac uptake. The classification performance was compared to four state-of-the-art CNN models. Results Our CNN models performed better than, or equally well as, the state-of-the-art models in detection and classification of cardiac uptake. Both models achieved area under the curve (AUC) >= 0.85 in the four-class Perugini grade classification. Accuracy was good in detection of negative vs. positive ATTR patients (grade = 2, AUC > 0.88) and high-grade cardiac uptake vs. other patients (grade < 3 vs. grade 3, AUC = 0.94). Maximum activation maps demonstrated that the automated deep learning models were focused on detecting the myocardium and not extracardiac features. Conclusion Automated convolutional neural networks can accurately detect and classify different grades of cardiac uptake on bone scintigraphy. The CNN models are focused on clinically relevant image features. Automated screening of bone scintigraphy images using CNN could improve the early diagnosis of ATTR.Peer reviewe

Perspectives on Nuclear Medicine for Molecular Diagnosis and Integrated Therapy

nuclear medicine; diagnostic radiolog