22 research outputs found
Robust adaptive synchronization of a class of uncertain chaotic systems with unknown time-delay
In this paper, a robust adaptive control strategy is proposed to synchronize a class of uncertain chaotic systems with unknown time delays. Using Lyapunov theory and Lipschitz conditions in chaotic systems, the necessary adaptation rules for estimating uncertain parameters and unknown time delays are determined. Based on the proposed adaptation rules, an adaptive controller is recommended for the robust synchronization of the aforementioned uncertain systems that prove the robust stability of the proposed control mechanism utilizing the Lyapunov theorem. Finally, to evaluate the proposed robust and adaptive control mechanism, the synchronization of two Jerk chaotic systems with finite non-linear uncertainty and external disturbances as well as unknown fixed and variable time delays are simulated. The simulation results confirm the ability of the proposed control mechanism in robust synchronization of the uncertain chaotic systems as well as to estimate uncertain and unknown parameters
An Overview on Artificial Intelligence Techniques for Diagnosis of Schizophrenia Based on Magnetic Resonance Imaging Modalities: Methods, Challenges, and Future Works
Schizophrenia (SZ) is a mental disorder that typically emerges in late
adolescence or early adulthood. It reduces the life expectancy of patients by
15 years. Abnormal behavior, perception of emotions, social relationships, and
reality perception are among its most significant symptoms. Past studies have
revealed the temporal and anterior lobes of hippocampus regions of brain get
affected by SZ. Also, increased volume of cerebrospinal fluid (CSF) and
decreased volume of white and gray matter can be observed due to this disease.
The magnetic resonance imaging (MRI) is the popular neuroimaging technique used
to explore structural/functional brain abnormalities in SZ disorder owing to
its high spatial resolution. Various artificial intelligence (AI) techniques
have been employed with advanced image/signal processing methods to obtain
accurate diagnosis of SZ. This paper presents a comprehensive overview of
studies conducted on automated diagnosis of SZ using MRI modalities. Main
findings, various challenges, and future works in developing the automated SZ
detection are described in this paper
Automatic Autism Spectrum Disorder Detection Using Artificial Intelligence Methods with MRI Neuroimaging: A Review
Autism spectrum disorder (ASD) is a brain condition characterized by diverse
signs and symptoms that appear in early childhood. ASD is also associated with
communication deficits and repetitive behavior in affected individuals. Various
ASD detection methods have been developed, including neuroimaging modalities
and psychological tests. Among these methods, magnetic resonance imaging (MRI)
imaging modalities are of paramount importance to physicians. Clinicians rely
on MRI modalities to diagnose ASD accurately. The MRI modalities are
non-invasive methods that include functional (fMRI) and structural (sMRI)
neuroimaging methods. However, the process of diagnosing ASD with fMRI and sMRI
for specialists is often laborious and time-consuming; therefore, several
computer-aided design systems (CADS) based on artificial intelligence (AI) have
been developed to assist the specialist physicians. Conventional machine
learning (ML) and deep learning (DL) are the most popular schemes of AI used
for diagnosing ASD. This study aims to review the automated detection of ASD
using AI. We review several CADS that have been developed using ML techniques
for the automated diagnosis of ASD using MRI modalities. There has been very
limited work on the use of DL techniques to develop automated diagnostic models
for ASD. A summary of the studies developed using DL is provided in the
appendix. Then, the challenges encountered during the automated diagnosis of
ASD using MRI and AI techniques are described in detail. Additionally, a
graphical comparison of studies using ML and DL to diagnose ASD automatically
is discussed. We conclude by suggesting future approaches to detecting ASDs
using AI techniques and MRI neuroimaging
Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review
In recent years, cardiovascular diseases (CVDs) have become one of the
leading causes of mortality globally. CVDs appear with minor symptoms and
progressively get worse. The majority of people experience symptoms such as
exhaustion, shortness of breath, ankle swelling, fluid retention, and other
symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia,
cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina
are the most common CVDs. Clinical methods such as blood tests,
electrocardiography (ECG) signals, and medical imaging are the most effective
methods used for the detection of CVDs. Among the diagnostic methods, cardiac
magnetic resonance imaging (CMR) is increasingly used to diagnose, monitor the
disease, plan treatment and predict CVDs. Coupled with all the advantages of
CMR data, CVDs diagnosis is challenging for physicians due to many slices of
data, low contrast, etc. To address these issues, deep learning (DL) techniques
have been employed to the diagnosis of CVDs using CMR data, and much research
is currently being conducted in this field. This review provides an overview of
the studies performed in CVDs detection using CMR images and DL techniques. The
introduction section examined CVDs types, diagnostic methods, and the most
important medical imaging techniques. In the following, investigations to
detect CVDs using CMR images and the most significant DL methods are presented.
Another section discussed the challenges in diagnosing CVDs from CMR data.
Next, the discussion section discusses the results of this review, and future
work in CVDs diagnosis from CMR images and DL techniques are outlined. The most
important findings of this study are presented in the conclusion section
Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review
Coronavirus, or COVID-19, is a hazardous disease that has endangered the
health of many people around the world by directly affecting the lungs.
COVID-19 is a medium-sized, coated virus with a single-stranded RNA. This virus
has one of the largest RNA genomes and is approximately 120 nm. The X-Ray and
computed tomography (CT) imaging modalities are widely used to obtain a fast
and accurate medical diagnosis. Identifying COVID-19 from these medical images
is extremely challenging as it is time-consuming, demanding, and prone to human
errors. Hence, artificial intelligence (AI) methodologies can be used to obtain
consistent high performance. Among the AI methodologies, deep learning (DL)
networks have gained much popularity compared to traditional machine learning
(ML) methods. Unlike ML techniques, all stages of feature extraction, feature
selection, and classification are accomplished automatically in DL models. In
this paper, a complete survey of studies on the application of DL techniques
for COVID-19 diagnostic and automated segmentation of lungs is discussed,
concentrating on works that used X-Ray and CT images. Additionally, a review of
papers on the forecasting of coronavirus prevalence in different parts of the
world with DL techniques is presented. Lastly, the challenges faced in the
automated detection of COVID-19 using DL techniques and directions for future
research are discussed
Population-based incidence of Type 2 diabetes and its associated risk factors: results from a six-year cohort study in Iran
<p>Abstract</p> <p>Background</p> <p>The Middle East is estimated to have the largest increase in prevalence of diabetes by 2030; yet there is lack of published data on the incidence of Type 2 diabetes in this region. This study aimed to estimate Type 2 diabetes incidence and its associated risk factors in an Iranian urban population.</p> <p>Methods</p> <p>Among 3307 non-diabetics ≥ 20 years (mean age 42 ± 13 years, 42% males), glucose tolerance test was performed at baseline in 1999–2001 and at two consecutive phases in 2001–2005 and 2005–2008. Diabetes and glucose tolerance status were defined according to the ADA 1997 criteria. Logistic regression was used to determine the independent variables associated with incident diabetes and their odds ratios (OR).</p> <p>Results</p> <p>After median follow-up of 6 years, 237 new cases of diabetes were ascertained corresponding to an age and sex standardized cumulative incidence of 6.4% (95%CI: 5.6–7.2) and incidence rate of 10.6 (9.2–12.1) per 1000 person years. Besides classical diabetes risk factors, female sex and low education level significantly increased risk of diabetes in age adjusted models. In full model, the independent predictors were age [OR, 95%CI: 1.2 (1.1–1.3)], family history of diabetes [1.8 (1.3–2.5)], body mass index ≥ 30 kg/m<sup>2 </sup>[2.3 (1.5–3.6)], abdominal obesity [1.9 (1.4–2.6)], high triglyceride [1.4 (1.1–1.9)], Isolated impaired fasting glucose (IFG) [7.4 (3.6–15.0)], Isolated impaired glucose tolerance (IGT) [5.9 (4.2–8.4)] and combined IFG and IGT [42.2 (23.8–74.9)].</p> <p>Conclusion</p> <p>More than 1% of the Iranian urban population older than 20 years develops Type 2 diabetes each year. Combination of IFG and IGT was the strongest predictor of incident diabetes among the modifiable risk factors.</p
The global burden of adolescent and young adult cancer in 2019 : a systematic analysis for the Global Burden of Disease Study 2019
Background In estimating the global burden of cancer, adolescents and young adults with cancer are often overlooked, despite being a distinct subgroup with unique epidemiology, clinical care needs, and societal impact. Comprehensive estimates of the global cancer burden in adolescents and young adults (aged 15-39 years) are lacking. To address this gap, we analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, with a focus on the outcome of disability-adjusted life-years (DALYs), to inform global cancer control measures in adolescents and young adults. Methods Using the GBD 2019 methodology, international mortality data were collected from vital registration systems, verbal autopsies, and population-based cancer registry inputs modelled with mortality-to-incidence ratios (MIRs). Incidence was computed with mortality estimates and corresponding MIRs. Prevalence estimates were calculated using modelled survival and multiplied by disability weights to obtain years lived with disability (YLDs). Years of life lost (YLLs) were calculated as age-specific cancer deaths multiplied by the standard life expectancy at the age of death. The main outcome was DALYs (the sum of YLLs and YLDs). Estimates were presented globally and by Socio-demographic Index (SDI) quintiles (countries ranked and divided into five equal SDI groups), and all estimates were presented with corresponding 95% uncertainty intervals (UIs). For this analysis, we used the age range of 15-39 years to define adolescents and young adults. Findings There were 1.19 million (95% UI 1.11-1.28) incident cancer cases and 396 000 (370 000-425 000) deaths due to cancer among people aged 15-39 years worldwide in 2019. The highest age-standardised incidence rates occurred in high SDI (59.6 [54.5-65.7] per 100 000 person-years) and high-middle SDI countries (53.2 [48.8-57.9] per 100 000 person-years), while the highest age-standardised mortality rates were in low-middle SDI (14.2 [12.9-15.6] per 100 000 person-years) and middle SDI (13.6 [12.6-14.8] per 100 000 person-years) countries. In 2019, adolescent and young adult cancers contributed 23.5 million (21.9-25.2) DALYs to the global burden of disease, of which 2.7% (1.9-3.6) came from YLDs and 97.3% (96.4-98.1) from YLLs. Cancer was the fourth leading cause of death and tenth leading cause of DALYs in adolescents and young adults globally. Interpretation Adolescent and young adult cancers contributed substantially to the overall adolescent and young adult disease burden globally in 2019. These results provide new insights into the distribution and magnitude of the adolescent and young adult cancer burden around the world. With notable differences observed across SDI settings, these estimates can inform global and country-level cancer control efforts. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd.Peer reviewe