RF-CNN-F: random forest with convolutional neural network features for coronary artery disease diagnosis based on cardiac magnetic resonance

Coronary artery disease (CAD) is a prevalent disease with high morbidity and mortality rates. Invasive coronary angiography is the reference standard for diagnosing CAD but is costly and associated with risks. Noninvasive imaging like cardiac magnetic resonance (CMR) facilitates CAD assessment and can serve as a gatekeeper to downstream invasive testing. Machine learning methods are increasingly applied for automated interpretation of imaging and other clinical results for medical diagnosis. In this study, we proposed a novel CAD detection method based on CMR images by utilizing the feature extraction ability of deep neural networks and combining the features with the aid of a random forest for the very first time. It is necessary to convert image data to numeric features so that they can be used in the nodes of the decision trees. To this end, the predictions of multiple stand-alone convolutional neural networks (CNNs) were considered as input features for the decision trees. The capability of CNNs in representing image data renders our method a generic classification approach applicable to any image dataset. We named our method RF-CNN-F, which stands for Random Forest with CNN Features. We conducted experiments on a large CMR dataset that we have collected and made publicly accessible. Our method achieved excellent accuracy (99.18%) using Adam optimizer compared to a stand-alone CNN trained using fivefold cross validation (93.92%) tested on the same dataset.

Managing Myocardial Infarction in the COVID-19 Epidemic: A Case Report

Coronaviruses can cause viral pneumonia with extrapulmonary manifestations and complications. Many patients have either underlying cardiovascular disease or cardiac risk factors. Acute heart attacks are also frequent in severe cases of coronavirus disease 2019 (COVID-19), which is associated with high mortality.

In this paper, we describe a patient with COVID-19 who presented with myocardial infarction (MI) symptoms but lacked the initial symptoms of the infection such as fever and cough. COVID-19 and myocardial infarction were diagnosed. The patient underwent thrombolytic treatment and fully recovered.

FCM-DNN: diagnosing coronary artery disease by deep accuracy fuzzy C-means clustering model

Cardiovascular disease is one of the most challenging diseases in middle-aged and older people, which causes high mortality. Coronary artery disease (CAD) is known as a common cardiovascular disease. A standard clinical tool for diagnosing CAD is angiography. The main challenges are dangerous side effects and high angiography costs. Today, the development of artificial intelligence-based methods is a valuable achievement for diagnosing disease. Hence, in this paper, artificial intelligence methods such as neural network (NN), deep neural network (DNN), and fuzzy C-means clustering combined with deep neural network (FCM-DNN) are developed for diagnosing CAD on a cardiac magnetic resonance imaging (CMRI) dataset. The original dataset is used in two different approaches. First, the labeled dataset is applied to the NN and DNN to create the NN and DNN models. Second, the labels are removed, and the unlabeled dataset is clustered via the FCM method, and then, the clustered dataset is fed to the DNN to create the FCM-DNN model. By utilizing the second clustering and modeling, the training process is improved, and consequently, the accuracy is increased. As a result, the proposed FCM-DNN model achieves the best performance with a 99.91% accuracy specifying 10 clusters, i.e., 5 clusters for healthy subjects and 5 clusters for sick subjects, through the 10-fold cross-validation technique compared to the NN and DNN models reaching the accuracies of 92.18% and 99.63%, respectively. To the best of our knowledge, no study has been conducted for CAD diagnosis on the CMRI dataset using artificial intelligence methods. The results confirm that the proposed FCM-DNN model can be helpful for scientific and research centers.

Objective evaluation of deep uncertainty predictions for COVID-19 detection

Deep neural networks (DNNs) have been widely applied for detecting COVID-19 in medical images. Existing studies mainly apply transfer learning and other data representation strategies to generate accurate point estimates. The generalization power of these networks is always questionable due to being developed using small datasets and failing to report their predictive confidence. Quantifying uncertainties associated with DNN predictions is a prerequisite for their trusted deployment in medical settings. Here we apply and evaluate three uncertainty quantification techniques for COVID-19 detection using chest X-Ray (CXR) images. The novel concept of uncertainty confusion matrix is proposed and new performance metrics for the objective evaluation of uncertainty estimates are introduced. Through comprehensive experiments, it is shown that networks pertained on CXR images outperform networks pretrained on natural image datasets such as ImageNet. Qualitatively and quantitatively evaluations also reveal that the predictive uncertainty estimates are statistically higher for erroneous predictions than correct predictions. Accordingly, uncertainty quantification methods are capable of flagging risky predictions with high uncertainty estimates. We also observe that ensemble methods more reliably capture uncertainties during the inference. DNN-based solutions for COVID-19 detection have been mainly proposed without any principled mechanism for risk mitigation. Previous studies have mainly focused on on generating single-valued predictions using pretrained DNNs. In this paper, we comprehensively apply and comparatively evaluate three uncertainty quantification techniques for COVID-19 detection using chest X-Ray images. The novel concept of uncertainty confusion matrix is proposed and new performance metrics for the objective evaluation of uncertainty estimates are introduced for the first time. Using these new uncertainty performance metrics, we quantitatively demonstrate when we could trust DNN predictions for COVID-19 detection from chest X-rays. It is important to note the proposed novel uncertainty evaluation metrics are generic and could be applied for evaluation of probabilistic forecasts in all classification problems.

CNN-KCL: Automatic myocarditis diagnosis using convolutional neural network combined with k-means clustering

Myocarditis is the form of an inflammation of the middle layer of the heart wall which is caused by a viral infection and can affect the heart muscle and its electrical system. It has remained one of the most challenging diagnoses in cardiology. Myocardial is the prime cause of unexpected death in approximately 20% of adults less than 40 years of age. Cardiac MRI (CMR) has been considered a noninvasive and golden standard diagnostic tool for suspected myocarditis and plays an indispensable role in diagnosing various cardiac diseases. However, the performance of CMR depends heavily on the clinical presentation and features such as chest pain, arrhythmia, and heart failure. Besides, other imaging factors like artifacts, technical errors, pulse sequence, acquisition parameters, contrast agent dose, and more importantly qualitatively visual interpretation can affect the result of the diagnosis. This paper introduces a new deep learning-based model called Convolutional Neural Network-Clustering (CNN-KCL) to diagnose Myocarditis. In this study, we used 47 subjects with a total number of 98,898 images to diagnose myocarditis disease. Our results demonstrate that the proposed method achieves an accuracy of 97.41% based on 10 fold-cross validation technique with 4 clusters for diagnosis of Myocarditis. To the best of our knowledge, this research is the first to use deep learning algorithms for the diagnosis of myocarditis.

Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients

COVID-19 has caused many deaths worldwide. The automation of the diagnosis of this virus is highly desired. Convolutional neural networks (CNNs) have shown outstanding classification performance on image datasets. To date, it appears that COVID computer-aided diagnosis systems based on CNNs and clinical information have not yet been analysed or explored. We propose a novel method, named the CNN-AE, to predict the survival chance of COVID-19 patients using a CNN trained with clinical information. Notably, the required resources to prepare CT images are expensive and limited compared to those required to collect clinical data, such as blood pressure, liver disease, etc. We evaluated our method using a publicly available clinical dataset that we collected. The dataset properties were carefully analysed to extract important features and compute the correlations of features. A data augmentation procedure based on autoencoders (AEs) was proposed to balance the dataset. The experimental results revealed that the average accuracy of the CNN-AE (96.05%) was higher than that of the CNN (92.49%). To demonstrate the generality of our augmentation method, we trained some existing mortality risk prediction methods on our dataset (with and without data augmentation) and compared their performances. We also evaluated our method using another dataset for further generality verification. To show that clinical data can be used for COVID-19 survival chance prediction, the CNN-AE was compared with multiple pre-trained deep models that were tuned based on CT images.

Non diagnosed PAPVC induce large reverse venovenous shunt after modified Fontan surgery: A case report of a rare anomaly and embolization therapy

Fontan operation is a reliable palliative surgery for patients with single ventricle physiology. Still, the development of complication is common; one of these complications that need to interventional approach is veno-venous collaterals between systemic and pulmonary veins. A 16-yearoldgirl with a history of modified Fontan operation at 9 years ago was referred with progressive cyanosis and dyspnea on exertion. In contrast trans-thoracic echocardiography (TTE), no fenestration was seen in Fontan circulation. Cardiac magnetic resonance revealed partial anomalous pulmonary vein connection (PAPVC) from left upper pulmonary vein to vertical vein and then into the in nominate vein and SVC with the reverse flow from superior vena cava (SVC) to left upper pulmonary vein(LUPV). This anomalous vein became severe engorged and tortuous. Possibly, LUPV and the verticalvein was dilated gradually as a result of increased pressure in the Fontan circuit. Finally, she underwent successful coil embolization in the midpart of the vertical vein. The oxygen saturation increased from80% to 93%.

Risk factors prediction, clinical outcomes, and mortality in COVID-19 patients

Preventing communicable diseases requires understanding the spread, epidemiology, clinical features, progression, and prognosis of the disease. Early identification of risk factors and clinical outcomes might help in identifying critically ill patients, providing appropriate treatment, and preventing mortality. We conducted a prospective study in patients with flu-like symptoms referred to the imaging department of a tertiary hospital in Iran between March 3, 2020, and April 8, 2020. Patients with COVID-19 were followed up after two months to check their health condition. The categorical data between groups were analyzed by Fisher's exact test and continuous data by Wilcoxon rank-sum test. Three hundred and nineteen patients (mean age 45.48 ± 18.50 years, 177 women) were enrolled. Fever, dyspnea, weakness, shivering, C-reactive protein, fatigue, dry cough, anorexia, anosmia, ageusia, dizziness, sweating, and age were the most important symptoms of COVID-19 infection. Traveling in the past 3 months, asthma, taking corticosteroids, liver disease, rheumatological disease, cough with sputum, eczema, conjunctivitis, tobacco use, and chest pain did not show any relationship with COVID-19. To the best of our knowledge, a number of factors associated with mortality due to COVID-19 have been investigated for the first time in this study. Our results might be helpful in early prediction and risk reduction of mortality in patients infected with COVID-19.

Arrhythmogenic right ventricular cardiomyopathy in patients with biallelic JUP-associated skin fragility

Arrhythmogenic right ventricular cardiomyopathy (ARVC), with skin manifestations, has been associated with mutations in JUP encoding plakoglobin. Genotype–phenotype correlations regarding the penetrance of cardiac involvement, and age of onset have not been well established. We examined a cohort of 362 families with skin fragility to screen for genetic mutations with next-generation sequencing-based methods. In two unrelated families, a previously unreported biallelic mutation, JUP: c.201delC; p.Ser68Alafs^*92, was disclosed. The consequences of this mutation were determined by expression profiling both at tissue and ultrastructural levels, and the patients were evaluated by cardiac and cutaneous work-up. Whole-transcriptome sequencing by RNA-Seq revealed JUP as the most down-regulated gene among 21 skin fragility-associated genes. Immunofluorescence showed the lack of plakoglobin in the epidermis. Two probands, 2.5 and 22-year-old, with the same homozygous mutation, allowed us to study the cross-sectional progression of cardiac involvements in relation to age. The older patient had anterior T wave inversions, prolonged terminal activation duration (TAD), and RV enlargement by echocardiogram, and together with JUP mutation met definite ARVC diagnosis. The younger patient had no evidence of cardiac disease, but met possible ARVC diagnosis with one major criterion (the JUP mutation). In conclusion, we identified the same biallelic homozygous JUP mutation in two unrelated families with skin fragility, but cardiac findings highlighted age-dependent penetrance of ARVC. Thus, young, phenotypically normal patients with biallelic JUP mutations should be monitored for development of ARVC.

Motion-compensated frame rate up-conversion in carotid ultrasound images using optical flow and manifold learning

OBJECTIVE

Carotid ultrasonography is a reliable and non-invasive method to evaluate atherosclerosis disease and its complications. B-mode cineloops are widely used to assess the severity of atherosclerosis and its progression; ho- wever, tracking rapid wall motions of the carotid artery is still a challenging issue due the low frame rate. The aim of this paper was to present a new hybrid frame rate up-conversion (FRUC) method that accounts for motion based on manifold learning and optical flow.

METHODS

In the last decade, manifold learning technique has been used to pseudo-increase the frame rate of carotid ultrasound images, but due to the dependence of this method to the number of recorded cardiac cycles and frames, a new hybrid method based on manifold learning and optical flow was proposed in this paper.

RESULTS

Locally linear embedding (LLE) algorithm was first applied to find the relation between the frames of consecutive cardiac cycles in a low dimensional manifold. Then by applying the optical flow motion estimation algorithm, a motion compensated frame was reconstructed.

CONCLUSION

Consequently, a cycle with more frames was created to provide a more accurate consideration of carotid wall motion compared to the typical B-mode ultrasound ima-ges. The results revealed that our new hybrid method outperforms the pseudo-increasing frame rate scheme based on manifold learning.

Evaluation of pulmonary hypertension and its relationship with serum parathyroid hormone levels in hemodialysis patients

Introduction: Pulmonary hypertension is a progressive and severe disease associated with left or right ventricular dysfunction and is common in patients under hemodialysis. One of the factors in its development is parathormone but this relationship is unclear. Objectives: In this study, the difference between serum levels of parathyroid hormone (PTH) in hemodialysis patients with and without pulmonary artery hypertension (PAH) was investigated. Patients and Methods: We conducted a cross-sectional study among hemodialysis patients referred to a tertiary hospital in Iran. Characteristics of coronary artery and pulmonary arteries were recorded by echocardiography. The laboratory data were measured and recorded. Statistical analysis was performed at 95% confidence level and with a significance level of less than 5%. Results: Of 65 enrolled patients, 41 had normal pulmonary artery pressure, and 24 had pulmonary hypertension. The mean age in patients with and without pulmonary hypertension was significantly different (P=0.010). There was no significant difference in serum PTH levels between patients with and without pulmonary hypertension (P=0.496). The mean serum levels of calcium, albumin, triglyceride, and cholesterol in two groups of patients with and without pulmonary hypertension was not significantly different (P=0.906). Conclusion: In our study, no significant correlation between pulmonary hypertension and PTH was detected. The prevalence of pulmonary arterial hypertension in our study was relatively high, suggesting the need to pay attention to pulmonary hypertension in hemodialysis patients

Value of pathologic Q wave in surface electrocardiography in the prediction of myocardial nonviability: A cardiac magnetic resonance imaging-based study

In surface electrocardiography (ECG), Q wave is often considered as a sign of irreversibly scarred myocardium. Cardiac magnetic resonance (CMR) imaging is an accurate mean for the detection of myocardial viability. Herein, we study the predictive value of Q wave in nonviable (scarred) myocardium by CMR study. Retrospective analysis of the ECG and CMR data of 35 coronary artery disease patients was performed. The delayed enhancement CMR protocol was used for the detection of viability. The presence of a pathologic Q wave in surface ECG was negatively related to myocardial viability with a kappa measurement of agreement of −0.544 and P < 0.0001. Pathologic Q wave in surface ECG can be used as a simple tool for myocardial viability prediction.

The correlation between cardiac magnetic resonance T2* and left ventricular global longitudinal strain in people with β-thalassemia

BACKGROUND

Heart failure is the biggest cause of mortality and morbidity in people with thalassemia, and iron deposition in cardiac tissue impairs cardiovascular function. Therefore, early detection of cardiac involvement is important to improve the prognosis in these individuals.

METHOD

Two- and three-dimensional echocardiography was performed to evaluate left ventricular ejection fraction (LVEF), left ventricular volumes and diameters, and global longitudinal strain (GLS) in 130 individuals with β-thalassemia using the speckle tracking method. Magnetic resonance imaging (MRI) was carried out on both the heart and liver. The participants were divided into 2 groups based on cardiac T2* values (normal and abnormal cardiac iron load), and the correlation between cardiac T2* MRI and GLS was evaluated.

RESULTS

The statistical analysis showed a significant correlation between cardiac T2* MRI and left ventricular global longitudinal strain. There was a significant difference in global longitudinal strain (P < .0001), liver MRI T2*( P < .0001), and left ventricular ejection fraction (P < .001) between the 2 groups. The optimal cutoff value for GLS was -18.5% with sensitivity and specificity 73.0% and 63.0%, respectively (postitive predictive value = 50%, negative predictive value = 82.3%, AUC = 0.742, std. error = 0.046) which predicts T2* value of <20 ms, according to cardiac MRI.

CONCLUSIONS

The participants with cardiac iron overload had a lower GLS than those without one. This suggests that GLS may be a useful method to predict myocardial iron overload particularly in β-thalassemia patients with subclinical cardiac involvement.

Congenital partial absence of the pericardium presenting with a rare concurrent abnormality of vascular ring diagnosed by cardiac magnetic resonance imaging

Congenital absence of the pericardium is a rare abnormality that can be diagnosed by cardiac imaging procedures. A 49-year-old male needed medical attention due to the appearance of palpitation with a systolic murmur, and a notable aortic arch deviation was seen in the chest X-ray. In the echocardiogram, a poor echo window was detected. A cardiac magnetic resonance imaging (MRI) showed a rare concomitant anomaly of partial absence of the pericardium including a rare defect of the right-sided aortic arch. Using cardiac MRI, the pericardium can be easily visualized, and thus, its absence more easily detected, aiding appropriate clinical decision-making.

Left ventricular apical hypoplasia: Case report on cardiomyopathy and a history of sudden cardiac death

BACKGROUND

Isolated left ventricular apical hypoplasia with several different unrecognized dimensions is a newly discovered congenital anomaly of the heart.

CASE REPORT

In this report, we describe a case of cardiomyopathy of this type occurring in a 13-year-old male with a history of mental retardation and sudden cardiac death (SCD) of second-degree relatives. The patient was referred for an evaluation of cardiac status. An echocardiography analysis demonstrated a spherical left ventricle (LV) appearance with mild mitral regurgitation. Cardiac magnetic resonance imaging (MRI) confirmed a spherical and truncated LV appearance. The right ventricle was found to have elongated and wrapped around the LV, and diverticulum was also seen in the cardiac MRI.

CONCLUSION

To the best of our knowledge, this is to present the first case of LV apical hypoplasia combined with LV diverticulum and a family history of SCD. As more cases featuring this cardiomyopathy type are recognized, it will be easier to elucidate the natural history and management of such cardiac anomalies.

Temporal Super Resolution Enhancement of Echocardiographic Images Based on Sparse Representation

A challenging issue for echocardiographic image interpretation is the accurate analysis of small transient motions of myocardium and valves during real-time visualization. A higher frame rate video may reduce this difficulty, and temporal super resolution (TSR) is useful for illustrating the fast-moving structures. In this paper, we introduce a novel framework that optimizes TSR enhancement of echocardiographic images by utilizing temporal information and sparse representation. The goal of this method is to increase the frame rate of echocardiographic videos, and therefore enable more accurate analyses of moving structures. For the proposed method, we first derived temporal information by extracting intensity variation time curves (IVTCs) assessed for each pixel. We then designed both low-resolution and high-resolution overcomplete dictionaries based on prior knowledge of the temporal signals and a set of prespecified known functions. The IVTCs can then be described as linear combinations of a few prototype atoms in the low-resolution dictionary. We used the Bayesian compressive sensing (BCS) sparse recovery algorithm to find the sparse coefficients of the signals. We extracted the sparse coefficients and the corresponding active atoms in the low-resolution dictionary to construct new sparse coefficients corresponding to the high-resolution dictionary. Using the estimated atoms and the high-resolution dictionary, a new IVTC with more samples was constructed. Finally, by placing the new IVTC signals in the original IVTC positions, we were able to reconstruct the original echocardiography video with more frames. The proposed method does not require training of low-resolution and high-resolution dictionaries, nor does it require motion estimation; it does not blur fast-moving objects, and does not have blocking artifacts.

Automatic computation of left ventricular volume changes over a cardiac cycle from echocardiography images by nonlinear dimensionality reduction

Curve of left ventricular (LV) volume changes throughout the cardiac cycle is a fundamental parameter for clinical evaluation of various cardiovascular diseases. Currently, this evaluation is often performed manually which is tedious and time consuming and suffers from significant interobserver and intraobserver variability. This paper introduces a new automatic method, based on nonlinear dimensionality reduction (NLDR) for extracting the curve of the LV volume changes over a cardiac cycle from two-dimensional (2-D) echocardiography images. Isometric feature mapping (Isomap) is one of the most popular NLDR algorithms. In this study, a modified version of Isomap algorithm, where image to image distance metric is computed using nonrigid registration, is applied on 2-D echocardiography images of one cycle of heart. Using this approach, the nonlinear information of these images is embedded in a 2-D manifold and each image is characterized by a symbol on the constructed manifold. This new representation visualizes the relationship between these images based on LV volume changes and allows extracting the curve of the LV volume changes automatically. Our method in comparison to the traditional segmentation algorithms does not need any LV myocardial segmentation and tracking, particularly difficult in the echocardiography images. Moreover, a large data set under various diseases for training is not required. The results obtained by our method are quantitatively evaluated to those obtained manually by the highly experienced echocardiographer on ten healthy volunteers and six patients which depict the usefulness of the presented method.

Study of the effects of age and body mass index on the carotid wall vibration: Extraction methodology and analysis

This study aims to non-invasively extract the vibrations of the carotid wall and evaluate the changes in the carotid artery wall caused by age and obesity. Such evaluation can increase the possibility of detecting wall stiffness and atherosclerosis in its early stage. In this study, a novel method that uses a phase-tracking method based on the continuous wavelet transform calculates the carotid wall motion from the ultrasound radio frequency signals. To extract the high-frequency components of the wall motion, wall vibration, the empirical mode decomposition was then used. The posterior wall (intima-media) motion and vibration were extracted for 54 healthy volunteers (mean age: 33.87 ± 14.73 years), including 13 overweight subjects (body mass index > 25) and 14 female participants using their radio frequency signals. The results showed that the dominant frequency of the wall vibration correlates with age ( r = −0.5887, p < 0.001) and body mass index ( r = −0.4838, p < 0.001). The quantitative analysis further demonstrated that the dominant frequency of the vibration in the radial direction of the carotid wall decreases by age and is lower in overweight subjects. Besides, the peak-to-peak amplitude of the wall vibration showed significant correlations with age ( r = −0.5456, p < 0.001) and body mass index ( r = −0.5821, p < 0.001). The peak-to-peak amplitude also decreases by age and is lower in overweight subjects. However, there were no significant correlations between these features of the wall vibrations and systolic/diastolic blood pressure and sex. Our proposed measures were certified using the calculated arterial stiffness indices. The average power spectrum of the elderly subjects’ wall motion in the frequency range of the wall vibration (>100 Hz) is decreased more in comparison with the young subjects. Our results revealed that the proposed method may be useful for detecting the stiffness and distortion in the carotid wall that occur prior to wall thickening caused by age as an early-stage atherosclerotic sign.

Diagnosing Coronary Artery Disease via Data Mining Algorithms by Considering Laboratory and Echocardiography Features

BACKGROUND

Coronary artery disease (CAD) is the result of the accumulation of athermanous plaques within the walls of coronary arteries, which supply the myocardium with oxygen and nutrients. CAD leads to heart attacks or strokes and is, thus, one of the most important causes of death worldwide. Angiography, an imaging modality for blood vessels, is currently the most accurate method of diagnosing artery stenosis. However, the disadvantages of this method such as complications, costs, and possible side effects have prompted researchers to investigate alternative solutions.

OBJECTIVES

The current study aimed to use data analysis, a non-invasive and less costly method, and various data mining algorithms to predict the stenosis of arteries. Among many people who refer to hospitals due to chest pain, a great number of them are normal and as such do not need angiography. The objective of this study was to predict patients who are most probably normal using features with the highest correlations with CAD with a view to obviate angiography costs and complications. Not a substitute for angiography, this method would select high-risk cases that definitely need angiography.

PATIENTS AND METHODS

Different features were measured and collected from potential patients in order to construct a dataset, which was later utilized for model extraction. Most of the proposed methods in the literature have not considered the stenosis of each artery separately, whereas the present study employed laboratory and echocardiographic data to diagnose the stenosis of each artery separately. The data were gathered from 303 random visitors to Rajaie Cardiovascular, Medical and Research Center. Electrocardiographic (ECG) data were studied in our previous works. The goal of this study was, therefore, to seek the accuracy of echocardiographic and laboratory features to predict CAD patients that require angiography.

RESULTS

Bagging and C4.5 classification algorithms were drawn upon to analyse the data, the former reaching accuracy rates of 79.54%, 61.46%, and 68.96% for the diagnosis of the stenoses of the left anterior descending (LAD), left circumflex (LCX), and right coronary artery (RCA), respectively. The accuracy to predict the LAD stenosis was attained via feature selection. In the current study, features effective in the stenosis of arteries were further determined, and some rules for the evaluation of triglyceride, hemoglobin, hypertension, dyslipidemia, diabetes mellitus, and ejection fraction were extracted.

CONCLUSIONS

The current study presents the highest accuracy value to diagnose the LAD stenosis in the literature.

Doppler-derived strain and strain rate imaging assessment of right ventricular systolic function in adults late after tetralogy of Fallot repair: an observational study

OBJECTIVE

Tetralogy of Fallot (TOF) is the most common form of cyanotic congenital heart disease. Today, we are faced with an increasing number of patients with residual pulmonary regurgitation (PR) late after TOF repair. The right ventricular (RV) volumes and function are among the most important factors influencing clinical decision-making. Cardiac magnetic resonance (CMR) is the gold standard method for the quantitative assessment of the RV function; it is, however, expensive for routine clinical follow-up and sometimes is contraindicated. We sought to evaluate the RV systolic function via CMR and compare it with Doppler-derived strain(S) and strain rate (SR) imaging in patients with repaired TOF.

METHODS

In an observational cross-sectional study, 70 patients (22 women, mean age=22±4.9 years) late after TOF repair with severe PR were evaluated. Peak systolic strain and SR in the basal, mid, and apical segments of RV free wall (RVFW) were measured and compared with the RV function measured in the short-axis cine MR. Associations between RVEF and S/SR, investigated by ordinal logistic regression models.

RESULTS

Significant association was observed between RV function and mean S of all the three segments of the RVFW segments [OR (CI95%): 1.17 (1.05-1.31)]. Association between RV function and mean SR of all the three segments of the RVFW segments was borderline significant [OR (CI95%): 1.7 (0.97-2.93)].

CONCLUSION

There was a significant correlation between the Doppler-derived mean strain of RVFW and the RV function measured by CMR in adults late after TOF repair. These quantitative methods improved the assessment of the RV function and served as an additional method to follow up patients with contraindications to CMR.

A data mining approach for diagnosis of coronary artery disease

Cardiovascular diseases are very common and are one of the main reasons of death. Being among the major types of these diseases, correct and in-time diagnosis of coronary artery disease (CAD) is very important. Angiography is the most accurate CAD diagnosis method; however, it has many side effects and is costly. Existing studies have used several features in collecting data from patients, while applying different data mining algorithms to achieve methods with high accuracy and less side effects and costs. In this paper, a dataset called Z-Alizadeh Sani with 303 patients and 54 features, is introduced which utilizes several effective features. Also, a feature creation method is proposed to enrich the dataset. Then Information Gain and confidence were used to determine the effectiveness of features on CAD. Typical Chest Pain, Region RWMA2, and age were the most effective ones besides the created features by means of Information Gain. Moreover Q Wave and ST Elevation had the highest confidence. Using data mining methods and the feature creation algorithm, 94.08% accuracy is achieved, which is higher than the known approaches in the literature.

Effect of exercise on blood flow through the aortic valve: a combined clinical and numerical study

The aim of this study was to measure the cardiac output and stroke volume for a healthy subject by coupling an echocardiogram Doppler (echo-Doppler) method with a fluid-structure interaction (FSI) simulation at rest and during exercise. Blood flow through aortic valve was measured by Doppler flow echocardiography. Aortic valve geometry was calculated by echocardiographic imaging. An FSI simulation was performed, using an arbitrary Lagrangian-Eulerian mesh. Boundary conditions were defined by pressure loads on ventricular and aortic sides. Pressure loads applied brachial pressures with (stage 1) and without (stage 2) differences between brachial, central and left ventricular pressures. FSI results for cardiac output were 15.4% lower than Doppler results for stage 1 (r = 0.999). This difference increased to 22.3% for stage 2. FSI results for stroke volume were undervalued by 15.3% when compared to Doppler results at stage 1 and 26.2% at stage 2 (r = 0.94). The predicted mean backflow of blood was 4.6%. Our results show that numerical methods can be combined with clinical measurements to provide good estimates of patient-specific cardiac output and stroke volume at different heart rates.

Diagnosis of coronary arteries stenosis using data mining

Cardiovascular diseases are one of the most common diseases that cause a large number of deaths each year. Coronary Artery Disease (CAD) is the most common type of these diseases worldwide and is the main reason of heart attacks. Thus early diagnosis of CAD is very essential and is an important field of medical studies. Many methods are used to diagnose CAD so far. These methods reduce cost and deaths. But a few studies examined stenosis of each vessel separately. Determination of stenosed coronary artery when significant ECG abnormality exists is not a difficult task. Moreover, ECG abnormality is not common among CAD patients. The aim of this study is to find a way for specifying the lesioned vessel when there is not enough ECG changes and only based on risk factors, physical examination and Para clinic data. Therefore, a new data set was used which has no missing value and includes new and effective features like Function Class, Dyspnoea, Q Wave, ST Elevation, ST Depression and Tinversion. These data was collected from 303 random visitor of Tehran's Shaheed Rajaei Cardiovascular, Medical and Research Centre, in 2011 fall and 2012 winter. They processed with C4.5, Naïve Bayes, and k-nearest neighbour (KNN) algorithms and their accuracy were measured by tenfold cross validation. In the best method the accuracy of diagnosis of stenosis of each vessel reached to 74.20 ± 5.51% for Left Anterior Descending (LAD), 63.76 ± 9.73% for Left Circumflex and 68.33 ± 6.90% for Right Coronary Artery. The effective features of stenosis of each vessel were found too.

Modified optical flow technique for cardiac motions analysis in echocardiography images

The quantitative analysis of cardiac motions in echocardiography images is a noteworthy issue in processing of these images. Cardiac motions can be estimated by optical flow (OF) computation in different regions of image which is based on the assumption that the intensity of a moving pattern remains constant in consecutive frames. However, in echocardiographic sequences, this assumption may be violated because of unique specifications of ultrasound. There are some methodsapplying the brightness variation effect in OF. Almost all of them have presented a mathematical brightness variation model globally in the images. Nevertheless, there is not a brightness variation model for echocardiographic images in these methods. Therefore, we are looking for a method to apply brightness variations locally in different regions of the image. In this study, we proposed a method to modify ausual OF technique by considering intensity variation. To evaluate this method, we implement two other OF-based methods, one usual OF method and a modified OF method applying brightness variation as a multiplier and an offset (generalized dynamic imaging model [GDIM]) and compare them with ours. These algorithms and ours were implemented on real 2D echocardiograms. Our method resulted in more accurate estimations than two others. At last, we compared our method with expert'spoint of view and observed that three distance metrics between them was appropriately smaller than other methods. The Haussdorff distance between the estimated curve defined by the proposed method and the expert defined curve is 4.81 pixels less than this distance for Lucas-Kanade and 2.28 pixels less than GDIM.

Exerting Cost-Sensitive and Feature Creation Algorithms for Coronary Artery Disease Diagnosis

One of the main causes of death the world over is the family of cardiovascular diseases, of which coronary artery disease (CAD) is a major type. Angiography is the principal diagnostic modality for the stenosis of heart arteries; however, it leads to high complications and costs. The present study conducted data-mining algorithms on the Z-Alizadeh Sani dataset, so as to investigate rule based and feature based classifiers and their comparison, and the reason for the effectiveness of a preprocessing algorithm on a dataset. Misclassification of diseased patients has more side effects than that of healthy ones. To this end, this paper employs 10-fold cross-validation on cost-sensitive algorithms along with base classifiers of Naïve Bayes, Sequential Minimal Optimization (SMO), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and C4.5 and the results show that the SMO algorithm yielded very high sensitivity (97.22%) and accuracy (92.09%) rates.

A New Method for Pseudo-increasing Frame Rates of Echocardiography Images Using Manifold Learning

Increasing frame rate is a challenging issue for better interpretation of medical images and diagnosis based on tracking the small transient motions of myocardium and valves in real time visualization. In this paper, manifold learning algorithm is applied to extract the nonlinear embedded information about echocardiography images from the consecutive images in two dimensional manifold spaces. In this method, we presume that the dimensionality of echocardiography images obtained from a patient is artificially high and the images can be described as functions of only a few underlying parameters such as periodic motion due to heartbeat. By this approach, each image is projected as a point on the reconstructed manifold; hence, the relationship between images in the new domain can be obtained according to periodicity of the heart cycle. To have a better tracking of the echocardiography, images during the fast motions of heart we have rearranged the similar frames of consecutive heart cycles in a sequence. This provides a full view slow motion of heart movement through increasing the frame rate to three times the traditional ultrasound systems.

Assessment of heavy metals in chicken feeds available in Sokoto, Nigeria

In the present work six metals (Cu, Pb, Zn, Cd, Mn and Ni) were analyzed for, using atomic absorption spectrophotometry in three main feed brands commonly used in Sokoto (2 commercial feed and 1 locally compounded chicken feed). Initially, the samples were digested with concentrated nitric acid and perchloric acid at about 3700C to 4500C heat in a digestion block. The concentration in ìg/ml of the six metals analyzed for in the feed samples ranged between 0.04 and 1.21 for Cu, 0.01 and 0.55 for Pb, 1.43 and 11.65 for Zn, 0.10 and 0.12 Cd, 0.94 and 3.12 for Mn and 0.004 and 0.25 for Ni. In most of the analyzed samples, the concentration of Cu, Zn, Mn and Ni was found to be lower than the nutritional requirement of broiler chicken at a level which could be harmful for the poultry. Also the study showed the presence of heavy metals (Pb and Cd) in all the feed samples analyzed, but none exceeded permissible levels as set by European Union and National Research Council.Keywords: Atomic absorption spectrophotometry, Chicken feeds, Contamination, Heavy metals, Sokoto, Toxicity