Publications by authors named "Abbas Khosravi"

58 Publications

Factors associated with mortality in hospitalized cardiovascular disease patients infected with COVID-19.

Immun Inflamm Dis 2022 Jan 20. Epub 2022 Jan 20.

Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, Victoria, Australia.

Introduction: To reduce mortality in hospitalized patients with COVID-19 and cardiovascular disease (CVD), it is necessary to understand the relationship between patient's symptoms, risk factors, and comorbidities with their mortality rate. To the best of our knowledge, this paper is the first which take into account the determinants like risk factors, symptoms, and comorbidities leading to mortality in CVD patients who are hospitalized with COVID-19.

Methods: This study was conducted on 660 hospitalized patients with CVD and COVID-19 recruited between January 2020 and January 2021 in Iran. All patients were diagnosed with the previous history of CVD like angina, myocardial infarction, heart failure, cardiomyopathy, abnormal heart rhythms, and congenital heart disease before they were hospitalized for COVID-19. We collected data on patient's signs and symptoms, clinical and paraclinical examinations, and any underlying comorbidities. t test was used to determine the significant difference between the two deceased and alive groups. In addition, the relation between pairs of symptoms and pairs of comorbidities has been determined via correlation computation.

Results: Our findings suggest that signs and symptoms such as fever, cough, myalgia, chest pain, chills, abdominal pain, nausea, vomiting, diarrhea, and anorexia had no impact on patients' mortality. There was a significant correlation between COVID-19 cardiovascular patients' mortality rate and symptoms such as headache, loss of consciousness (LOC), oxygen saturation less than 93%, and need for mechanical ventilation.

Conclusions: Our results might help physicians identify early symptoms, comorbidities, and risk factors related to mortality in CVD patients hospitalized for COVID-19.
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http://dx.doi.org/10.1002/iid3.561DOI Listing
January 2022

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

J Cardiovasc Thorac Res 2021 14;13(4):364-366. Epub 2021 Apr 14.

Institute for Physical Activity and Nutrition, Deakin University, Melbourne, Australia.

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%.
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http://dx.doi.org/10.34172/jcvtr.2021.22DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8749363PMC
April 2021

Objective evaluation of deep uncertainty predictions for COVID-19 detection.

Sci Rep 2022 01 17;12(1):815. Epub 2022 Jan 17.

Institute of Physical Activity and Nutrition, Deakin University, Melbourne, VIC, Australia.

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.
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http://dx.doi.org/10.1038/s41598-022-05052-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8763911PMC
January 2022

Application of artificial intelligence in wearable devices: Opportunities and challenges.

Comput Methods Programs Biomed 2022 Jan 17;213:106541. Epub 2021 Nov 17.

Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan.

Background And Objectives: Wearable technologies have added completely new and fast emerging tools to the popular field of personal gadgets. Aside from being fashionable and equipped with advanced hardware technologies such as communication modules and networking, wearable devices have the potential to fuel artificial intelligence (AI) methods with a wide range of valuable data.

Methods: Various AI techniques such as supervised, unsupervised, semi-supervised and reinforcement learning (RL) have already been used to carry out various tasks. This paper reviews the recent applications of wearables that have leveraged AI to achieve their objectives.

Results: Particular example applications of supervised and unsupervised learning for medical diagnosis are reviewed. Moreover, examples combining the internet of things, wearables, and RL are reviewed. Application examples of wearables will be also presented for specific domains such as medical, industrial, and sport. Medical applications include fitness, movement disorder, mental health, etc. Industrial applications include employee performance improvement with the aid of wearables. Sport applications are all about providing better user experience during workout sessions or professional gameplays.

Conclusion: The most important challenges regarding design and development of wearable devices and the computation burden of using AI methods are presented. Finally, future challenges and opportunities for wearable devices are presented.
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http://dx.doi.org/10.1016/j.cmpb.2021.106541DOI Listing
January 2022

A novel deep neuroevolution-based image classification method to diagnose coronavirus disease (COVID-19).

Comput Biol Med 2021 12 1;139:104994. Epub 2021 Nov 1.

Institute for Intelligent Systems Research and Innovation, (IISRI), Deakin University, Geelong, Australia.

COVID-19 has had a detrimental impact on normal activities, public safety, and the global financial system. To identify the presence of this disease within communities and to commence the management of infected patients early, positive cases should be diagnosed as quickly as possible. New results from X-ray imaging indicate that images provide key information about COVID-19. Advanced deep-learning (DL) models can be applied to X-ray radiological images to accurately diagnose this disease and to mitigate the effects of a shortage of skilled medical personnel in rural areas. However, the performance of DL models strongly depends on the methodology used to design their architectures. Therefore, deep neuroevolution (DNE) techniques are introduced to automatically design DL architectures accurately. In this paper, a new paradigm is proposed for the automated diagnosis of COVID-19 from chest X-ray images using a novel two-stage improved DNE Algorithm. The proposed DNE framework is evaluated on a real-world dataset and the results demonstrate that it provides the highest classification performance in terms of different evaluation metrics.
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http://dx.doi.org/10.1016/j.compbiomed.2021.104994DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8558149PMC
December 2021

Deep learning for neuroimaging-based diagnosis and rehabilitation of Autism Spectrum Disorder: A review.

Comput Biol Med 2021 Dec 29;139:104949. Epub 2021 Oct 29.

Deakin University, IMPACT - the Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon Health, Geelong, Australia; Orygen, The National Centre of Excellence in Youth Mental Health, Centre for Youth Mental Health, Florey Institute for Neuroscience and Mental Health and the Department of Psychiatry, The University of Melbourne, Melbourne, Australia.

Accurate diagnosis of Autism Spectrum Disorder (ASD) followed by effective rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) techniques can aid physicians to apply automatic diagnosis and rehabilitation procedures. AI techniques comprise traditional machine learning (ML) approaches and deep learning (DL) techniques. Conventional ML methods employ various feature extraction and classification techniques, but in DL, the process of feature extraction and classification is accomplished intelligently and integrally. DL methods for diagnosis of ASD have been focused on neuroimaging-based approaches. Neuroimaging techniques are non-invasive disease markers potentially useful for ASD diagnosis. Structural and functional neuroimaging techniques provide physicians substantial information about the structure (anatomy and structural connectivity) and function (activity and functional connectivity) of the brain. Due to the intricate structure and function of the brain, proposing optimum procedures for ASD diagnosis with neuroimaging data without exploiting powerful AI techniques like DL may be challenging. In this paper, studies conducted with the aid of DL networks to distinguish ASD are investigated. Rehabilitation tools provided for supporting ASD patients utilizing DL networks are also assessed. Finally, we will present important challenges in the automated detection and rehabilitation of ASD and propose some future works.
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http://dx.doi.org/10.1016/j.compbiomed.2021.104949DOI Listing
December 2021

A case-control study on factor V Leiden: an independent, gender-dependent risk factor for venous thromboembolism.

Thromb J 2021 Oct 19;19(1):74. Epub 2021 Oct 19.

Transfusion Research center, High Institute for Research and Education in Transfusion, Tehran, Iran.

Background: Activated protein C resistance (APCR) due to factor V Leiden (FVL) mutation (R506Q) is a major risk factor in patients with venous thromboembolism (VTE). The present study investigated the clinical manifestations and the risk of venous thromboembolism regarding multiple clinical, laboratory, and demographic properties in FVL patients.

Material And Methods: A retrospective cross-sectional analysis was conducted on a total of 288 FVL patients with VTE according to APCR. In addition, 288 VET control samples, without FVL mutation, were also randomly selected. Demographic information, clinical manifestations, family and treatment history were recorded, and specific tests including t-test, chi-square and uni- and multi-variable regression tests applied.

Results: APCR was found to be 2.3 times significantly more likely in men (OR: 2.1, p < 0.05) than women. The risk of deep vein thrombosis (DVT) and pulmonary embolism (PE) in APCR patients was 4.5 and 3.2 times more than the control group, respectively (p < 0.05). However, APCR could not be an independent risk factor for arterial thrombosis (AT) and pregnancy complications. Moreover, patients were evaluated for thrombophilia panel tests and showed significantly lower protein C and S than the control group and patients without DVT (p < 0.0001).

Conclusion: FVL mutation and APCR abnormality are noticeable risk factors for VTE. Screening strategies for FVL mutation in patients undergoing surgery, oral contraceptive medication, and pregnancy cannot be recommended, but a phenotypic test for activated protein C resistance should be endorsed in patients with VTE.
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http://dx.doi.org/10.1186/s12959-021-00328-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8527672PMC
October 2021

MCUa: Multi-Level Context and Uncertainty Aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification.

IEEE Trans Biomed Eng 2022 Feb 20;69(2):818-829. Epub 2022 Jan 20.

Breast histology image classification is a crucial step in the early diagnosis of breast cancer. In breast pathological diagnosis, Convolutional Neural Networks (CNNs) have demonstrated great success using digitized histology slides. However, tissue classification is still challenging due to the high visual variability of the large-sized digitized samples and the lack of contextual information. In this paper, we propose a novel CNN, called Multi-level Context and Uncertainty aware (MCUa) dynamic deep learning ensemble model. MCUa model consists of several multi-level context-aware models to learn the spatial dependency between image patches in a layer-wise fashion. It exploits the high sensitivity to the multi-level contextual information using an uncertainty quantification component to accomplish a novel dynamic ensemble model. MCUa model has achieved a high accuracy of 98.11% on a breast cancer histology image dataset. Experimental results show the superior effectiveness of the proposed solution compared to the state-of-the-art histology classification models.
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http://dx.doi.org/10.1109/TBME.2021.3107446DOI Listing
February 2022

Uncertainty-Aware Management of Smart Grids Using Cloud-Based LSTM-Prediction Interval.

IEEE Trans Cybern 2021 Aug 3;PP. Epub 2021 Aug 3.

This article introduces an uncertainty-aware cloud-fog-based framework for power management of smart grids using a multiagent-based system. The power management is a social welfare optimization problem. A multiagent-based algorithm is suggested to solve this problem, in which agents are defined as volunteering consumers and dispatchable generators. In the proposed method, every consumer can voluntarily put a price on its power demand at each interval of operation to benefit from the equal opportunity of contributing to the power management process provided for all generation and consumption units. In addition, the uncertainty analysis using a deep learning method is also applied in a distributive way with the local calculation of prediction intervals for sources with stochastic nature in the system, such as loads, small wind turbines (WTs), and rooftop photovoltaics (PVs). Using the predicted ranges of load demand and stochastic generation outputs, a range for power consumption/generation is also provided for each agent called ``preparation range'' to demonstrate the predicted boundary, where the accepted power consumption/generation of an agent might occur, considering the uncertain sources. Besides, fog computing is deployed as a critical infrastructure for fast calculation and providing local storage for reasonable pricing. Cloud services are also proposed for virtual applications as efficient databases and computation units. The performance of the proposed framework is examined on two smart grid test systems and compared with other well-known methods. The results prove the capability of the proposed method to obtain the optimal outcomes in a short time for any scale of grid.
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http://dx.doi.org/10.1109/TCYB.2021.3089634DOI Listing
August 2021

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

Sci Rep 2021 07 28;11(1):15343. Epub 2021 Jul 28.

Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, VIC, 3220, Australia.

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.
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http://dx.doi.org/10.1038/s41598-021-93543-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8319175PMC
July 2021

An oppositional-Cauchy based GSK evolutionary algorithm with a novel deep ensemble reinforcement learning strategy for COVID-19 diagnosis.

Appl Soft Comput 2021 Nov 10;111:107675. Epub 2021 Jul 10.

Institute for Intelligent Systems Research and Innovation, (IISRI), Deakin University, Geelong, Australia.

A novel coronavirus (COVID-19) has globally attracted attention as a severe respiratory condition. The epidemic has been first tracked in Wuhan, China, and has progressively been expanded in the entire world. The growing expansion of COVID-19 around the globe has made X-ray images crucial for accelerated diagnostics. Therefore, an effective computerized system must be established as a matter of urgency, to facilitate health care professionals in recognizing X-ray images from COVID-19 patients. In this work, we design a novel artificial intelligent-based automated X-ray image analysis framework based on an ensemble of deep optimized convolutional neural networks (CNNs) in order to distinguish coronavirus patients from non-patients. By developing a modified version of gaining-sharing knowledge (GSK) optimization algorithm using the Opposition-based learning (OBL) and Cauchy mutation operators, the architectures of the deployed deep CNNs are optimized automatically without performing the general trial and error procedures. After obtaining the optimized CNNs, it is also very critical to identify how to decrease the number of ensemble deep CNN classifiers to ensure the classification effectiveness. To this end, a selective ensemble approach is proposed for COVID-19 X-ray based image classification using a deep Q network that combines reinforcement learning (RL) with the optimized CNNs. This approach increases the model performance in particular and therefore decreases the ensemble size of classifiers. The experimental results show that the proposed deep RL optimized ensemble approach has an excellent performance over two popular X-ray image based COVID-19 datasets. Our proposed advanced algorithm can accurately identify the COVID-19 patients from the normal individuals with a significant accuracy of 0.991441, precision of 0.993568, recall (sensitivity) of 0.981445, F-measure of 0.989666 and AUC of 0.990337 for Kaggle dataset as well as an excellent accuracy of 0.987742, precision of 0.984334, recall (sensitivity) of 0.989123, F-measure of 0.984939 and AUC of 0.988466 for Mendely dataset.
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http://dx.doi.org/10.1016/j.asoc.2021.107675DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8272021PMC
November 2021

Increased incidence of rhino-orbital mucormycosis in an educational therapeutic hospital during the COVID-19 pandemic in western Iran: An observational study.

Mycoses 2021 Nov 31;64(11):1366-1377. Epub 2021 Jul 31.

Department of Infectious Disease, School of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran.

Background: COVID-19 patients, especially the patients requiring hospitalisation, have a high risk of several complications such as opportunistic bacterial and fungal infections. Mucormycosis is a rare and opportunistic fungal infection that mainly affects diabetic and immunocompromised patients. An increase has been observed in the number of rhino-orbital mucormycosis in patients with COVID-19 admitted to Imam Khomeini Hospital, Kermanshah, Iran, since October 2020. This is a report of the frequency, risk factors, clinical manifestations, treatment and prognosis of COVID-19 associated with mucormycosis infection.

Methods: The medical records of COVID-19 patients with rhino-orbital mucormycosis who were diagnosed in an educational therapeutic hospital in Kermanshah, west of Iran were surveyed. Several parameters were analysed including demographic, clinical, therapeutic and laboratory characteristics.

Results: Twelve patients with COVID-19-associated rhino-orbital mucormycosis were identified from 12 October to 18 November 2020. All cases reported as proven mucormycosis had a history of hospitalisation due to COVID-19. Comorbidities mainly included diabetes mellitus (83.33%) and hypertension (58.33%). Seventy-five per cent of patients received corticosteroids for COVID- 19 treatment. The sites of involvement were rhino-sino-orbital (83%) and rhino-sino (17%). Amphotericin B/liposomal amphotericin B alone or in combination with surgical debridement or orbital exenteration was used as the first-line therapy. The overall mortality rate was 66.7% (8/12).

Conclusions: We found a high incidence of mucormycosis among COVID-19 patients. Diabetes mellitus and corticosteroid use were the dominant predisposing factor of mucormycosis. Mucormycosis is a life-threatening and opportunistic infection; therefore, physicians should know the signs and symptoms of the disease so that a timely diagnosis and therapy can be performed.
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http://dx.doi.org/10.1111/myc.13351DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8447086PMC
November 2021

Cardiovascular diseases risk prediction in patients with diabetes: Posthoc analysis from a matched case-control study in Bangladesh.

J Diabetes Metab Disord 2021 Jun 15;20(1):417-425. Epub 2021 Feb 15.

Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, VIC Australia.

Purpose: This study aimed to investigate the estimated 10-year predicted risk of developing cardiovascular diseases (CVD) among participants with and without diabetes in Bangladesh.

Methods: We performed posthoc analysis from a matched case-control study conducted among 1262 participants. A total of 631 participants with diabetes (case) were recruited from a tertiary hospital, and 631 age, sex and residence matched participants (control) were recruited from the community in Dhaka, Bangladesh. Socioeconomic anthropometric, clinical and CVD risk factor data were collected from the participants. The 10-year estimated CVD risk was calculated using the Framingham Risk Score, which has reasonable validity in South Asians.

Results: The mean (SD) age of the participants were 51 (10) years. Total 52.3% of cases and 17.2% of controls were at high risk for CVD. The 10-year risk of CVD increased by age and was higher among males in both groups. Among the control group, high CVD risk was more prevalent among higher education and income groups. More than 85% of the tobacco smokers and 70% of chewing tobacco users in the case group were at high risk of CVD. Prevalence of high CVD risk among non-smokers cases was 8.6%. About 35% of hypertensive participants in the control group were at high risk of CVD.

Conclusion: Bangladeshi patients with diabetes showed a significant burden of CVD risk at a relatively younger age. Strategies for reducing tobacco use and improving BP control in people with diabetes is needed for lowering future CVD risks.
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http://dx.doi.org/10.1007/s40200-021-00761-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8212228PMC
June 2021

Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods.

Results Phys 2021 Aug 26;27:104495. Epub 2021 Jun 26.

John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary.

The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread worldwide, leading to an ongoing pandemic, imposed restrictions and costs to many countries. Predicting the number of new cases and deaths during this period can be a useful step in predicting the costs and facilities required in the future. The purpose of this study is to predict new cases and deaths rate one, three and seven-day ahead during the next 100 days. The motivation for predicting every n days (instead of just every day) is the investigation of the possibility of computational cost reduction and still achieving reasonable performance. Such a scenario may be encountered in real-time forecasting of time series. Six different deep learning methods are examined on the data adopted from the WHO website. Three methods are LSTM, Convolutional LSTM, and GRU. The bidirectional extension is then considered for each method to forecast the rate of new cases and new deaths in Australia and Iran countries. This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate time series. To the best of our knowledge, this is the first time that Bi-GRU and Bi-Conv-LSTM models are used for prediction on COVID-19 new cases and new deaths time series. The evaluation of the methods is presented in the form of graphs and Friedman statistical test. The results show that the bidirectional models have lower errors than other models. A several error evaluation metrics are presented to compare all models, and finally, the superiority of bidirectional methods is determined. This research could be useful for organisations working against COVID-19 and determining their long-term plans.
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http://dx.doi.org/10.1016/j.rinp.2021.104495DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8233414PMC
August 2021

Epileptic Seizures Detection Using Deep Learning Techniques: A Review.

Int J Environ Res Public Health 2021 05 27;18(11). Epub 2021 May 27.

Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore.

A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL). Before the rise of DL, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in DL, the extraction of features and classification are entirely automated. The advent of these techniques in many areas of medicine, such as in the diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of works focused on automated epileptic seizure detection using DL techniques and neuroimaging modalities is presented. Various methods proposed to diagnose epileptic seizures automatically using EEG and MRI modalities are described. In addition, rehabilitation systems developed for epileptic seizures using DL have been analyzed, and a summary is provided. The rehabilitation tools include cloud computing techniques and hardware required for implementation of DL algorithms. The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed. The advantages and limitations in employing DL-based techniques for epileptic seizures diagnosis are presented. Finally, the most promising DL models proposed and possible future works on automated epileptic seizure detection are delineated.
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http://dx.doi.org/10.3390/ijerph18115780DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8199071PMC
May 2021

Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning.

Comput Biol Med 2021 08 28;135:104418. Epub 2021 Apr 28.

Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia.

Accurate automated medical image recognition, including classification and segmentation, is one of the most challenging tasks in medical image analysis. Recently, deep learning methods have achieved remarkable success in medical image classification and segmentation, clearly becoming the state-of-the-art methods. However, most of these methods are unable to provide uncertainty quantification (UQ) for their output, often being overconfident, which can lead to disastrous consequences. Bayesian Deep Learning (BDL) methods can be used to quantify uncertainty of traditional deep learning methods, and thus address this issue. We apply three uncertainty quantification methods to deal with uncertainty during skin cancer image classification. They are as follows: Monte Carlo (MC) dropout, Ensemble MC (EMC) dropout and Deep Ensemble (DE). To further resolve the remaining uncertainty after applying the MC, EMC and DE methods, we describe a novel hybrid dynamic BDL model, taking into account uncertainty, based on the Three-Way Decision (TWD) theory. The proposed dynamic model enables us to use different UQ methods and different deep neural networks in distinct classification phases. So, the elements of each phase can be adjusted according to the dataset under consideration. In this study, two best UQ methods (i.e., DE and EMC) are applied in two classification phases (the first and second phases) to analyze two well-known skin cancer datasets, preventing one from making overconfident decisions when it comes to diagnosing the disease. The accuracy and the F1-score of our final solution are, respectively, 88.95% and 89.00% for the first dataset, and 90.96% and 91.00% for the second dataset. Our results suggest that the proposed TWDBDL model can be used effectively at different stages of medical image analysis.
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http://dx.doi.org/10.1016/j.compbiomed.2021.104418DOI Listing
August 2021

Clinico-Hematological and cytogenetic spectrum of adult myelodysplastic syndrome: The first retrospective cross-sectional study in Iranian patients.

Mol Cytogenet 2021 May 8;14(1):24. Epub 2021 May 8.

Department of Hematology and Blood Banking, School of Allied Medicine, Iran University of Medical Sciences, Tehran, Iran.

Background: Myelodysplastic syndrome (MDS), a heterogeneous group of hematopoietic malignancy, has been shown to present different cytogenetic abnormalities, risk factors, and clinico-hematological features in different populations and geographic areas. Herein, we determined the cytogenetic spectrum and clinico-hematological features of Iranian MDS patients for the first time.

Methods: This prospective cross-sectional study was conducted on 103 patients with MDS in Ahvaz, southwest of Iran, from 2014 to 2018. Clinical presentations, complete blood counts (CBC), and bone marrow (BM) biopsy samples were assessed. Perls' staining was used to evaluate BM iron storage. The cytogenetic evaluation was performed using the conventional G banding method on the BM.

Results: Patients' median age was 62.3 (ranged from 50-76), and the majority were male (72.8%). The most common clinical symptom at the time of admission was fatigue (n = 33) followed by pallor (n = 27). The most common subgroup was MDS-Multi Lineage Dysplasia (MDS-MLD) (n = 38, 36.8%), followed by MDS-Single Lineage Dysplasia (MDS-SLD) (n = 28, 18.4%). A normal karyotype was observed in 59 patients (57.3%), while 44 patients (42.7%) had cytogenetic abnormalities. Trisomy 8 (+ 8) was the most common cytogenetic abnormality (n = 14) followed by del 17p (n = 9) and monosomy 7 (- 7) (n = 7). Twelve patients (11.65%) were transformed to AML.

Conclusion: Our data betokened that among our MDS patients, Trisomy 8 is the predominant cytogenetic abnormality, and MDS-MLD and MDS-SLD are the most common of subtypes. Noteworthy, the male: female ratio was slightly higher in Iran than in previous reports from other parts of the world. Our study is the first report of the clinical, hematological, and cytogenetic spectrum of MDS patients in Iran.
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http://dx.doi.org/10.1186/s13039-021-00548-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8106119PMC
May 2021

Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images.

Biomed Signal Process Control 2021 Jul 8;68:102622. Epub 2021 Apr 8.

Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.

The coronavirus (COVID-19) is currently the most common contagious disease which is prevalent all over the world. The main challenge of this disease is the primary diagnosis to prevent secondary infections and its spread from one person to another. Therefore, it is essential to use an automatic diagnosis system along with clinical procedures for the rapid diagnosis of COVID-19 to prevent its spread. Artificial intelligence techniques using computed tomography (CT) images of the lungs and chest radiography have the potential to obtain high diagnostic performance for Covid-19 diagnosis. In this study, a fusion of convolutional neural network (CNN), support vector machine (SVM), and Sobel filter is proposed to detect COVID-19 using X-ray images. A new X-ray image dataset was collected and subjected to high pass filter using a Sobel filter to obtain the edges of the images. Then these images are fed to CNN deep learning model followed by SVM classifier with ten-fold cross validation strategy. This method is designed so that it can learn with not many data. Our results show that the proposed CNN-SVM with Sobel filter (CNN-SVM + Sobel) achieved the highest classification accuracy, sensitivity and specificity of 99.02%, 100% and 95.23%, respectively in automated detection of COVID-19. It showed that using Sobel filter can improve the performance of CNN. Unlike most of the other researches, this method does not use a pre-trained network. We have also validated our developed model using public databases and obtained the highest performance. Hence, our developed model is ready for clinical application.
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http://dx.doi.org/10.1016/j.bspc.2021.102622DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8026268PMC
July 2021

Combination of Angiotensin (1-7) Agonists and Convalescent Plasma as a New Strategy to Overcome Angiotensin Converting Enzyme 2 (ACE2) Inhibition for the Treatment of COVID-19.

Front Med (Lausanne) 2021 18;8:620990. Epub 2021 Mar 18.

PRASE and Biology Department, Faculty of Sciences - I, Lebanese University, Beirut, Lebanon.

Coronavirus disease-2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently the most concerning health problem worldwide. SARS-CoV-2 infects cells by binding to angiotensin-converting enzyme 2 (ACE2). It is believed that the differential response to SARS-CoV-2 is correlated with the differential expression of ACE2. Several reports proposed the use of ACE2 pharmacological inhibitors and ACE2 antibodies to block viral entry. However, ACE2 inhibition is associated with lung and cardiovascular pathology and would probably increase the pathogenesis of COVID-19. Therefore, utilizing ACE2 soluble analogs to block viral entry while rescuing ACE2 activity has been proposed. Despite their protective effects, such analogs can form a circulating reservoir of the virus, thus accelerating its spread in the body. Levels of ACE2 are reduced following viral infection, possibly due to increased viral entry and lysis of ACE2 positive cells. Downregulation of ACE2/Ang (1-7) axis is associated with Ang II upregulation. Of note, while Ang (1-7) exerts protective effects on the lung and cardiovasculature, Ang II elicits pro-inflammatory and pro-fibrotic detrimental effects by binding to the angiotensin type 1 receptor (AT1R). Indeed, AT1R blockers (ARBs) can alleviate the harmful effects associated with Ang II upregulation while increasing ACE2 expression and thus the risk of viral infection. Therefore, Ang (1-7) agonists seem to be a better treatment option. Another approach is the transfusion of convalescent plasma from recovered patients with deteriorated symptoms. Indeed, this appears to be promising due to the neutralizing capacity of anti-COVID-19 antibodies. In light of these considerations, we encourage the adoption of Ang (1-7) agonists and convalescent plasma conjugated therapy for the treatment of COVID-19 patients. This therapeutic regimen is expected to be a safer choice since it possesses the proven ability to neutralize the virus while ensuring lung and cardiovascular protection through modulation of the inflammatory response.
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http://dx.doi.org/10.3389/fmed.2021.620990DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8012486PMC
March 2021

Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991-2020).

Ann Oper Res 2021 Mar 21:1-42. Epub 2021 Mar 21.

Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore.

Understanding the data and reaching accurate conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have been widely used for this purpose in various fields. One critically important yet less explored aspect is capturing and analyzing uncertainties in the data and model. Proper quantification of uncertainty helps to provide valuable information to obtain accurate diagnosis. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, various novel deep learning techniques have been proposed to deal with such uncertainties and improve the performance in decision making.
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http://dx.doi.org/10.1007/s10479-021-04006-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7982279PMC
March 2021

The need for a prediction model assessment framework.

Lancet Glob Health 2021 04 10;9(4):e404. Epub 2021 Feb 10.

Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, VIC 3216, Australia.

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http://dx.doi.org/10.1016/S2214-109X(21)00022-XDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7906664PMC
April 2021

An Uncertainty-Aware Transfer Learning-Based Framework for COVID-19 Diagnosis.

IEEE Trans Neural Netw Learn Syst 2021 04 2;32(4):1408-1417. Epub 2021 Apr 2.

The early and reliable detection of COVID-19 infected patients is essential to prevent and limit its outbreak. The PCR tests for COVID-19 detection are not available in many countries, and also, there are genuine concerns about their reliability and performance. Motivated by these shortcomings, this article proposes a deep uncertainty-aware transfer learning framework for COVID-19 detection using medical images. Four popular convolutional neural networks (CNNs), including VGG16, ResNet50, DenseNet121, and InceptionResNetV2, are first applied to extract deep features from chest X-ray and computed tomography (CT) images. Extracted features are then processed by different machine learning and statistical modeling techniques to identify COVID-19 cases. We also calculate and report the epistemic uncertainty of classification results to identify regions where the trained models are not confident about their decisions (out of distribution problem). Comprehensive simulation results for X-ray and CT image data sets indicate that linear support vector machine and neural network models achieve the best results as measured by accuracy, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC). Also, it is found that predictive uncertainty estimates are much higher for CT images compared to X-ray images.
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http://dx.doi.org/10.1109/TNNLS.2021.3054306DOI Listing
April 2021

The prognostic importance of BCR-ABL transcripts in Chronic Myeloid Leukemia: A systematic review and meta-analysis.

Leuk Res 2021 02 19;101:106512. Epub 2021 Jan 19.

Department of Hematology and Blood Banking, School of Allied Medicine, Iran University of Medical Sciences, Tehran, Iran. Electronic address:

Background: Chronic Myeloid Leukemia (CML) is characterized by the overproduction of BCR-ABL, a tyrosine kinase with constitutive activity, in which the majority of CML patients have e13a2 or e14a2 transcripts. Reckoned the possible associations between the hematologic and molecular features of the disease, a profound understanding of different aspects of this neoplasm would be provided.

Method: The authors implemented a systematic literature search, utilizing the terms published articles or internationally accepted abstracts from PubMed, Embase, Medline, Cochrane library before January 2019. Weighted mean proportion and 95 % confidence intervals (CIs) of CML prevalence calculated using a fixed-effects and a random-effects model. Statistical heterogeneity was evaluated using the I2 statistic.

Results: 34 studies for a total of 54,034 Patients were selected and included in the review. Results revealed that compared to e13a2 group, the overall estimated prevalence is much higher in the e14a2 (39 % and 54 %, respectively). Besides, the overall estimated prevalence ratio of male to female was higher in the e13a2 group in comparison to e14a2 (1.08 and 0.856 respectively). The overall estimated prevalence of dual transcription of e13a2/e14a2 was 1.11 %, and male/female overall estimated prevalence ratio was 1.18.

Conclusion: This meta-analysis of CML patients demonstrated the e14a2 as the more common transcript type. Usually, the e14a2 transcript is prevalent in females, whereas e13a2 and dual transcription of e13a2/e14a2 are more common in men. These data explicate that the differences in proportion are not by chance. This is crucial, as the transcript type is a variable suspected to be of prognostic importance for the treatment-related response, the outcome of treatment, and the rate of treatment-free remission.
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http://dx.doi.org/10.1016/j.leukres.2021.106512DOI Listing
February 2021

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

J Med Virol 2021 04 17;93(4):2307-2320. Epub 2020 Dec 17.

Institute for Physical Activity and Nutrition, Faculty of Health, Deakin University, Melbourne, Victoria, Australia.

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.
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http://dx.doi.org/10.1002/jmv.26699DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7753243PMC
April 2021

Coronary artery disease detection using artificial intelligence techniques: A survey of trends, geographical differences and diagnostic features 1991-2020.

Comput Biol Med 2021 01 28;128:104095. Epub 2020 Oct 28.

Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan.

While coronary angiography is the gold standard diagnostic tool for coronary artery disease (CAD), but it is associated with procedural risk, it is an invasive technique requiring arterial puncture, and it subjects the patient to radiation and iodinated contrast exposure. Artificial intelligence (AI) can provide a pretest probability of disease that can be used to triage patients for angiography. This review comprehensively investigates published papers in the domain of CAD detection using different AI techniques from 1991 to 2020, in order to discern broad trends and geographical differences. Moreover, key decision factors affecting CAD diagnosis are identified for different parts of the world by aggregating the results from different studies. In this study, all datasets that have been used for the studies for CAD detection, their properties, and achieved performances using various AI techniques, are presented, compared, and analyzed. In particular, the effectiveness of machine learning (ML) and deep learning (DL) techniques to diagnose and predict CAD are reviewed. From PubMed, Scopus, Ovid MEDLINE, and Google Scholar search, 500 papers were selected to be investigated. Among these selected papers, 256 papers met our criteria and hence were included in this study. Our findings demonstrate that AI-based techniques have been increasingly applied for the detection of CAD since 2008. AI-based techniques that utilized electrocardiography (ECG), demographic characteristics, symptoms, physical examination findings, and heart rate signals, reported high accuracy for the detection of CAD. In these papers, the authors ranked the features based on their assessed clinical importance with ML techniques. The results demonstrate that the attribution of the relative importance of ML features for CAD diagnosis is different among countries. More recently, DL methods have yielded high CAD detection performance using ECG signals, which drives its burgeoning adoption.
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http://dx.doi.org/10.1016/j.compbiomed.2020.104095DOI Listing
January 2021

Objective measurement of tinnitus using functional near-infrared spectroscopy and machine learning.

PLoS One 2020 18;15(11):e0241695. Epub 2020 Nov 18.

The Bionics Institute, East Melbourne, Victoria, Australia.

Chronic tinnitus is a debilitating condition which affects 10-20% of adults and can severely impact their quality of life. Currently there is no objective measure of tinnitus that can be used clinically. Clinical assessment of the condition uses subjective feedback from individuals which is not always reliable. We investigated the sensitivity of functional near-infrared spectroscopy (fNIRS) to differentiate individuals with and without tinnitus and to identify fNIRS features associated with subjective ratings of tinnitus severity. We recorded fNIRS signals in the resting state and in response to auditory or visual stimuli from 25 individuals with chronic tinnitus and 21 controls matched for age and hearing loss. Severity of tinnitus was rated using the Tinnitus Handicap Inventory and subjective ratings of tinnitus loudness and annoyance were measured on a visual analogue scale. Following statistical group comparisons, machine learning methods including feature extraction and classification were applied to the fNIRS features to classify patients with tinnitus and controls and differentiate tinnitus at different severity levels. Resting state measures of connectivity between temporal regions and frontal and occipital regions were significantly higher in patients with tinnitus compared to controls. In the tinnitus group, temporal-occipital connectivity showed a significant increase with subject ratings of loudness. Also in this group, both visual and auditory evoked responses were significantly reduced in the visual and auditory regions of interest respectively. Naïve Bayes classifiers were able to classify patients with tinnitus from controls with an accuracy of 78.3%. An accuracy of 87.32% was achieved using Neural Networks to differentiate patients with slight/ mild versus moderate/ severe tinnitus. Our findings show the feasibility of using fNIRS and machine learning to develop an objective measure of tinnitus. Such a measure would greatly benefit clinicians and patients by providing a tool to objectively assess new treatments and patients' treatment progress.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0241695PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7673524PMC
January 2021

Electromagnetic radiation: a new charming actor in hematopoiesis?

Expert Rev Hematol 2021 01 7;14(1):47-58. Epub 2021 Jan 7.

Transfusion Research Center, High Institute for Research and Education in Transfusion Medicine , Tehran, Iran.

Introduction: Electromagnetic waves play indispensable roles in life. Many studies addressed the outcomes of Electromagnetic field (EMF) on various biological functions such as cell proliferation, gene expression, epigenetic alterations, genotoxic, and carcinogenic effects, and its therapeutic applications in medicine. The impact of EMF on bone marrow (BM) is of high importance; however, EMF effects on BM hematopoiesis are not well understood.

Areas Covered: Publications in English were searched in ISI Web of Knowledge and Google Scholar with no restriction on publication date. A literature review has been conducted on the consequences of EMF exposure on BM non-hematopoietic stem cells, mesenchymal stem cells, and the application of these waves in regenerative medicine. Human blood cells such as lymphocytes, red blood cells and their precursors are altered qualitatively and quantitatively following electromagnetic radiation. Therefore, studying the impact of EMF on related signaling pathways in hematopoiesis and hematopoietic stem cell (HSC) differentiation could give a better insight into its efficacy on hematopoiesis and its potential therapeutic usage.

Expert Opinion: In this review, authors evaluated the possible biologic consequences of EMF on the hematopoiesis process in addition to its probable application in the treatment of hematologic disorders.
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http://dx.doi.org/10.1080/17474086.2020.1826301DOI Listing
January 2021

Fertility and pregnancy in Iranian thalassemia patients: An update on transfusion complications.

Transfus Med 2020 Oct 21;30(5):352-360. Epub 2020 Aug 21.

Blood Transfusion Research Center, High Institute for Research and Education in Transfusion Medicine, Tehran, Iran.

Background: Despite the significant advances in thalassemia pathobiology and efficacy of chelation regimens, complications of transfusion therapy have attenuated the reproductive health of thalassemia patients. Depending on clinical profiles, we aimed to assess the fertility status and stresses among thalassemia patients who desired to have children.

Material And Methods: A total of 213 couples in reproductive ages were enrolled in this study in Tehran. Patients' demographic, clinical, fertility and spouse's health status were documented. We evaluated the pituitary-gonadal axis, serum ferritin, liver enzymes, and alloimmunization before planning a pregnancy and reported them as a function of spontaneous conception and transfusion dependency.

Results: Data showed that 131 patients (62%) had 228 spontaneous pregnancies leading to 198 (86.6%) successful pregnancies. A significant difference was observed in spontaneous pregnancy with respect to fertility complications and transfusion dependency. In addition, the clinical conditions of spouses in patients with any spontaneous pregnancy were more thalassemia carriers (P < .05). Moreover, serum ferritin levels had a significant negative correlation with the levels of Testosterone, Estradiol, luteinizing hormone, and follicle-stimulating hormone. Furthermore, a significant positive correlation was reported with the level of liver enzymes. Finally, alanine transaminase and aspartate transaminase had a significant negative correlation with pituitary hormones.

Conclusion: We suggest that organised instruction in addition to good iron chelation, especially during the puberty period, would reduce the oxidative damage and related complications in thalassemia patients. Moreover, infertility seems to be attributed to iron deposition in various endocrine organs, pituitary, reproductive system and the liver, contributing to hormonal metabolism.
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http://dx.doi.org/10.1111/tme.12707DOI Listing
October 2020

Automated Detection of Presymptomatic Conditions in Spinocerebellar Ataxia Type 2 Using Monte Carlo Dropout and Deep Neural Network Techniques with Electrooculogram Signals.

Sensors (Basel) 2020 May 27;20(11). Epub 2020 May 27.

Department of Electronic Technology, Universidad de Málaga, 29071 Málaga, Spain.

Application of deep learning (DL) to the field of healthcare is aiding clinicians to make an accurate diagnosis. DL provides reliable results for image processing and sensor interpretation problems most of the time. However, model uncertainty should also be thoroughly quantified. This paper therefore addresses the employment of Monte Carlo dropout within the DL structure to automatically discriminate presymptomatic signs of spinocerebellar ataxia type 2 in saccadic samples obtained from electrooculograms. The current work goes beyond the common incorporation of this special type of dropout into deep neural networks and uses the uncertainty derived from the validation samples to construct a decision tree at the register level of the patients. The decision tree built from the uncertainty estimates obtained a classification accuracy of 81.18% in automatically discriminating control, presymptomatic and sick classes. This paper proposes a novel method to address both uncertainty quantification and explainability to develop reliable healthcare support systems.
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http://dx.doi.org/10.3390/s20113032DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7309035PMC
May 2020

A Survey of Computational Intelligence Techniques for Wind Power Uncertainty Quantification in Smart Grids.

IEEE Trans Neural Netw Learn Syst 2020 Nov 30;31(11):4582-4599. Epub 2020 Oct 30.

The high penetration level of renewable energy is thought to be one of the basic characteristics of future smart grids. Wind power, as one of the most increasing renewable energy, has brought a large number of uncertainties into the power systems. These uncertainties would require system operators to change their traditional ways of decision-making. This article provides a comprehensive survey of computational intelligence techniques for wind power uncertainty quantification in smart grids. First, prediction intervals (PIs) are introduced as a means to quantify the uncertainties in wind power forecasts. Various PI evaluation indices, including the latest trends in comprehensive evaluation techniques, are compared. Furthermore, computational intelligence-based PI construction methods are summarized and classified into traditional methods (parametric) and direct PI construction methods (nonparametric). In the second part of this article, methods of incorporating wind power forecast uncertainties into power system decision-making processes are investigated. Three techniques, namely, stochastic models, fuzzy logic models, and robust optimization, and different power system applications using these techniques are reviewed. Finally, future research directions, such as spatiotemporal and hierarchical forecasting, deep learning-based methods, and integration of predictive uncertainty estimates into the decision-making process, are discussed. This survey can benefit the readers by providing a complete technical summary of wind power uncertainty quantification and decision-making in smart grids.
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http://dx.doi.org/10.1109/TNNLS.2019.2956195DOI Listing
November 2020
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