An overview of 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 that SZ affects the temporal and anterior lobes of hippocampus regions of the brain. Also, increased volume of cerebrospinal fluid (CSF) and decreased volume of white and gray matter can be observed due to this disease. 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 accurately diagnose SZ. This paper presents a comprehensive overview of studies conducted on the automated diagnosis of SZ using MRI modalities. First, an AI-based computer aided-diagnosis system (CADS) for SZ diagnosis and its relevant sections are presented. Then, this section introduces the most important conventional machine learning (ML) and deep learning (DL) techniques in the diagnosis of diagnosing SZ. A comprehensive comparison is also made between ML and DL studies in the discussion section. In the following, the most important challenges in diagnosing SZ are addressed. Future works in diagnosing SZ using AI techniques and MRI modalities are recommended in another section. Results, conclusion, and research findings are also presented at the end.Ministerio de Ciencia e Innovación (España)/ FEDER under the RTI2018-098913-B100 projectConsejería de Economía, Innovación, Ciencia y Empleo (Junta de Andalucía) and FEDER under CV20-45250 and A-TIC-080-UGR18 project

Assessment of practical skill in measuring blood pressure in undergraduate nursing students

Background: Blood pressure is an important vital sign that reflects systemic and focal changes. Planning of treating most of the disease is based on the values obtained from this measurement. Regarding this, Knowledge about Blood pressure measurement procedure is important. Objective: The aim of this study was to determine the practical skill in measuring of blood pressure in undergraduate nursing students. Methods: This is an analytic-descriptive study of 350 first years to fourth year undergraduate nursing students who had studied in educational year 2012-2013 of Tehran University of Medical Sciences and samplized by census methods. Data collection was done through investigator-made questionnaire. The data was analyzed by using SPSS software (V: 21), using descriptive and analytical Statistics; at the significant level P<0.05. Findings: Findings showed the overall mean score of the students's knowledge in Blood pressure measurement was13/77±2/86. First year students's level of knowledge in Blood pressure measurement was higher than the other years, however the differences wasn's t statistically significant (P=0/93). Conclusion: These findings indicate that knowledge of participants was inadequate to perform blood pressure measurement in a standardized manner; therefore it is vital to have frequent, regular and up-to-date training for nurses in order to follow the new rules for measuring blood pressure. Key Words: Blood pressure, skill, measurement technique, nursing student

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

Comparing the Effect of Static and PNF Stretching on Hip Joint Flexibility of Un-training Female Students

Since many of people in their functional activities mostly place their knee joint in flexed position, the hamstring muscles tend to be shortened. On the other hand, shortness of these muscles affect the knee joint directly and the Hip Joint Flexibility indirectly. The purpose of this study is to compare the effect of static and PNF stretching on hip joint flexibility of un-training female students. Twenty-four 18&ndash;30 years old un-training female students without any history of pathology in hip, knee or back were selected. They were divided into three groups with 8 women in each group (static stretch, PNF stretch and control). The two stretch groups received stretching program three days every week for six weeks, while the control group did not. The result shows that range of hip joint flexibility of both groups of static and PNF stretching increased (P &lt; 0.05). However, there was no significant difference between these two groups (P &gt; 0.05). The while it remained unchanged in control subjects (P &gt; 0.05). Employing both methods (static and PNF stretching) increase the hip joint flexibility. The findings of this study suggest that optimal flexibility is achieved with a combination of these two methods. Thus, we recommend the combined training method to athletes who require very high flexibility

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 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

Comparison of concept mapping and conventional teaching methods on creativity of nursing students

Abstract Introduction: Creativity is an essential part of nursing care. Thus developing creativity skills in nursing education is a priority. The aim of this study was to compare the effect of instruction by concept-mapping and update the fundamental of nursing by teaching creativity skills to nursing students. Methods: This quasi-experimental study was carried out on 70 nursing students, who were divided randomly into two equal experimental and control groups, in the Clinical Skills Lab of Tehran Nursing and Midwifery School. Educational content was presented in the form of concept-mapping in the experimental group and common method in the control group. Data collection included a demographic information and Abedi Creativity questionnaire; that filled at the beginning and at the end of four weeks course period. Data were analyzed using SPSS software (V. 21), using descriptive and analytical statistics at the significant level P<0.05. Results: Before the intervention, mean total creativity’s score was 126.1±7.4 in the concept mapping group and 128.2±5.2 in the common group and the difference was not significant (P=0.07). However, after the intervention, a significant difference was found between the intervention and control group (157.8±7.3 vs. 138.1±5.1, P=0.01). Conclusion: After the intervention, the creativity skills in nursing students was improved in both group, but the Influence of education in concept-mapping group was greater than the common method. Further research with more time and for other lessons can be useful for evidence-based decision-making