Search results for: image guided therapy

Authors: Mathilde Blondon, Salla Aicha Dieng, Catherine Pfister

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In a paradoxical way, teenagers nowadays seem to use suicidal attempts to elaborate on their trauma abuses and regain some kind of control in their lives. As their behavior becomes life-threatening, the hospital offers a variety of expertise to address their need, with Child Protective Services also joining in, to a point when teenagers could have a feeling of losing control of their lives, which results in them making more suicidal attempts. Our goal here is to walk with these foster teenagers long enough to step therapy up first, then as their mental health is restored enough to step the therapy down in a way that is secure and will give them their life back. This would prevent them from making suicidal attempts to get a feeling of control over their life. We’ll present a clinical case of a 14-year-old girl named Sofia, who was suffering from parental deprivation, an identity disorder, and severe depression disorder. Our intervention took place in January 2024, after Sofia had undergone four hospitalizations, including a two-month period in a specialized clinic. In a stepping-up effort, a substantial setting has been built around Sofia. She was coming three days a week to therapeutic activities at the Child Psychiatry Day Hospital, she had one psychotherapy session a week at the Medical-Psychological Center, and she was meeting with the Adolescent Psychiatrist on a regular basis. However, her suicidal attempts frequency continued to increase to the point when she couldn’t stay more than four days outside the hospital unit without harming herself and being brought back to the Emergency Unit. We were all stuck in some kind of medical deadlock, writing to clinics that had no room for her while social workers were calling foster homes that wouldn’t even accept her either. At some point, a clinical decision was made by the psychiatrist to stop what appeared to be a global movement of traumatic repetition, which involved Sofia’s family, the medical team and the social workers as one. This decision to step therapy down created a surprise and put an end to the cycle. It provided a new path, a new solution where Sofia could securely settle without being unfaithful to her family. Her suicidal attempts stopped for four weeks. She had one relapse, then didn’t make another attempt so far. There is a fine line between too little and too much, a pathway with the right amount of care and support. We believe it is not a steady line but rather a path up and down the hill. It’s about building up this moment when medication and mental processes have improved the subject’s condition enough to allow the medical team to step therapy down and give more control back to the subject. These needed variations used to come from a change of hospital or medical team. Stepped care avoids any breaking of bonds and appears to be decisive in stopping teenagers’ suicidal attempts.

Keywords: child protection, adolescent psychiatry, teenager suicidal attempt, foster teenagers, parental deprivation, stepped care

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Authors: Daniel A. Walzer

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As the options for course delivery and development expand, plenty of misconceptions still exist concerning e-learning and online course delivery. Classroom instructors often discuss pedagogy, methodologies, and best practices regarding teaching from a singular, traditional in-class perspective. As more professors integrate online, blended, and hybrid courses into their dossier, a clearly defined rubric for gauging online course delivery is essential. The transition from a traditional learning structure towards an updated distance-based format requires careful planning, evaluation, and revision. This paper examines how new media stimulates reflective practice and guided inquiry to improve pedagogy, engage interdisciplinary collaboration, and supply rich qualitative data for future research projects in media arts disciplines.

Keywords: action research, inquiry, new media, reflection

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Authors: Charalabos Antonatos, Mariza Panoutsopoulou, Georgios K. Georgakilas, Evangelos Evangelou, Yiannis Vasilopoulos

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The extended tissue damage and severe clinical outcomes of autoimmune diseases, accompanied by the high annual costs to the overall health care system, highlight the need for an efficient therapy. Increasing knowledge over the pathophysiology of specific chronic inflammatory diseases, namely Psoriasis (PsO), Inflammatory Bowel Diseases (IBD) consisting of Crohn’s disease (CD) and Ulcerative colitis (UC), and Rheumatoid Arthritis (RA), has provided insights into the underlying mechanisms that lead to the maintenance of the inflammation, such as Tumor Necrosis Factor alpha (TNF-α). Hence, the anti-TNFα biological agents pose as an ideal therapeutic approach. Despite the efficacy of anti-TNFα agents, several clinical trials have shown that 20-40% of patients do not respond to treatment. Nowadays, high-throughput technologies have been recruited in order to elucidate the complex interactions in multifactorial phenotypes, with the most ubiquitous ones referring to transcriptome quantification analyses. In this context, a random effects meta-analysis of available gene expression cDNA microarray datasets was performed between responders and non-responders to anti-TNFα therapy in patients with IBD, PsO, and RA. Publicly available datasets were systematically searched from inception to 10th of November 2020 and selected for further analysis if they assessed the response to anti-TNFα therapy with clinical score indexes from inflamed biopsies. Specifically, 4 IBD (79 responders/72 non-responders), 3 PsO (40 responders/11 non-responders) and 2 RA (16 responders/6 non-responders) datasetswere selected. After the separate pre-processing of each dataset, 4 separate meta-analyses were conducted; three disease-specific and a single combined meta-analysis on the disease-specific results. The MetaVolcano R package (v.1.8.0) was utilized for a random-effects meta-analysis through theRestricted Maximum Likelihood (RELM) method. The top 1% of the most consistently perturbed genes in the included datasets was highlighted through the TopConfects approach while maintaining a 5% False Discovery Rate (FDR). Genes were considered as Differentialy Expressed (DEGs) as those with P ≤ 0.05, |log2(FC)| ≥ log2(1.25) and perturbed in at least 75% of the included datasets. Over-representation analysis was performed using Gene Ontology and Reactome Pathways for both up- and down-regulated genes in all 4 performed meta-analyses. Protein-Protein interaction networks were also incorporated in the subsequentanalyses with STRING v11.5 and Cytoscape v3.9. Disease-specific meta-analyses detected multiple distinct pro-inflammatory and immune-related down-regulated genes for each disease, such asNFKBIA, IL36, and IRAK1, respectively. Pathway analyses revealed unique and shared pathways between each disease, such as Neutrophil Degranulation and Signaling by Interleukins. The combined meta-analysis unveiled 436 DEGs, 86 out of which were up- and 350 down-regulated, confirming the aforementioned shared pathways and genes, as well as uncovering genes that participate in anti-inflammatory pathways, namely IL-10 signaling. The identification of key biological pathways and regulatory elements is imperative for the accurate prediction of the patient’s response to biological drugs. Meta-analysis of such gene expression data could aid the challenging approach to unravel the complex interactions implicated in the response to anti-TNFα therapy in patients with PsO, IBD, and RA, as well as distinguish gene clusters and pathways that are altered through this heterogeneous phenotype.

Keywords: anti-TNFα, autoimmune, meta-analysis, microarrays

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Authors: Mohamed M. Diab, Fahmy E. Mohamed, Alaa Balbaa

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Background: Myofascial pain syndrome a common type of non-articular musculoskeletal pain, is a condition associated with regional pain and muscle tenderness characterized by the presence of hypersensitive nodules. Objectives: the purpose of this study is to compare between the effects of manual progressive ischemic pressure versus the effect of post isometric facilitation in the treatment of Rhomboid latent myofascial trigger points. Methods: six patients had participated in this study. Patients divided into two groups. Group A treated by manual progressive ischemic pressure and traditional physical therapy program. Group B treated by post isometric facilitation and traditional physical therapy program. Treatment program was for 6 sessions over two week’s period. Result: Statistical analysis revealed that there is no significant difference in post treatment from pretreatment in pain severity (VAS) in myofascial trigger points with Rhomboid muscles) and Pain pressure threshold (PPT) for tenderness at both groups (A,B). Conclusion: ischemic pressure technique appear to be no more effective than post isometric facilitation in treatment of rhomboids latent myofacial trigger point.

Keywords: Rhmoiboid trigger point, myofacila trigger point, ischemic pressure, post isometric facilitation

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Authors: Magda Cepeda, Frances Hodgson, Ann Jopson

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There are different barriers people face when choosing to cycle for commuting purposes. This study examined the role of psycho-social factors predicting the intention to cycle to commute in Mexico City. An extended version of the theory of planned behaviour was developed and utilized with a simple random sample of 401 road users. We applied exploratory and confirmatory factor analysis and after identifying five factors, a structural equation model was estimated to find the relationships among the variables. The results indicated that cycling attributes, attitudes to cycling, social comparison and social image and prestige were the most important factors influencing intention to cycle. Although the results from this study are specific to Mexico City, they indicate areas of interest to transportation planners in other regions especially in those cities where intention to cycle its linked to its perceived image and there is political ambition to instigate positive cycling cultures. Moreover, this study contributes to the current literature developing applications of the Theory of Planned Behaviour.

Keywords: cycling, latent variable model, perception, theory of planned behaviour

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Authors: Vahid Bayrami Rad

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In recent years, the use of artificial intelligence techniques, tools, and methods in processing medical images and health-related applications has been highlighted and a lot of research has been done in this regard. For example, colonoscopy and diagnosis of colon lesions are some cases in which the process of diagnosis of lesions can be improved by using image processing and artificial intelligence algorithms, which help doctors a lot. Due to the lack of accurate measurements and the variety of injuries in colonoscopy images, the process of diagnosing the type of lesions is a little difficult even for expert doctors. Therefore, by using different software and image processing, doctors can be helped to increase the accuracy of their observations and ultimately improve their diagnosis. Also, by using automatic methods, the process of diagnosing the type of disease can be improved. Therefore, in this paper, a deep learning framework called KCBA is proposed to classify colonoscopy lesions which are composed of several methods such as K-means clustering, a bag of features and deep auto-encoder. Finally, according to the experimental results, the proposed method's performance in classifying colonoscopy images is depicted considering the accuracy criterion.

Keywords: colorectal cancer, colonoscopy, region of interest, narrow band imaging, texture analysis, bag of feature

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Authors: Nur Azlina Mohamed Mokmin, Mona Masood

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Guided by the theory of learning style, this study is based on the development of a multimedia learning application for students with mastery learning style. The learning material was developed by applying a graduated difficulty learning strategy. Algebraic fraction was chosen as the learning topic for this application. The effectiveness of this application in helping students learn is measured by giving a pre- and post-test. The result shows that students who learn using the learning material that matches their preferred learning style performs better than the students with a non-personalized learning material.

Keywords: algebraic fractions, graduated difficulty, mastery learning style, multimedia

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Authors: Jiangbo Shi, Zeyu Gao, Chen Li

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Nuclei segmentation is a fundamental task in digital pathology analysis and can be automated by deep learning-based methods. However, the development of such an automated method requires a large amount of data with precisely annotated masks which is hard to obtain. Training with weakly labeled data is a popular solution for reducing the workload of annotation. In this paper, we propose a novel meta-learning-based nuclei segmentation method which follows the label correction paradigm to leverage data with noisy masks. Specifically, we design a fully conventional meta-model that can correct noisy masks by using a small amount of clean meta-data. Then the corrected masks are used to supervise the training of the segmentation model. Meanwhile, a bi-level optimization method is adopted to alternately update the parameters of the main segmentation model and the meta-model. Extensive experimental results on two nuclear segmentation datasets show that our method achieves the state-of-the-art result. In particular, in some noise scenarios, it even exceeds the performance of training on supervised data.

Keywords: deep learning, histopathological image, meta-learning, nuclei segmentation, weak annotations

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Authors: Vu Hoang Thu Huong

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Background and Purpose: The effects of Extracorporeal Shockwave Therapy (ESWT) in the participants with knee osteoarthritis (KOA) were unclear on physical performance although its effects on pain had been investiagted. This study aims to explore the effects of ESWT on pain relief and physical performance on KOA. Methods: The studies with the randomized controlled design to investigate the effects of ESWT on KOA were systematically searched using inclusion and exclusion criteria through seven electronic databases including Pubmed etc. between 1990 and Dec 2022. To summarize those data, visual analog scale (VAS) or pain scores were determined for measure of pain intensity. Range of knee motion, or the scores of physical activities including Lequesne index (LI), Knee Injury and Osteoarthritis Outcome Score (KOOS), and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) were determined for measure of physical performances. The first evaluate after treatment period was define as the effect of post-treatment period or immediately effect; and the last evaluate was defined as the effect of following period or the end effect in our study. Data analysis was performed using RevMan 5.4.1 software. A significant level was set at p<0.05. Results: Eight studies (number of participant= 499) reporting the ESWT effects on mild-to-moderate severity (Grades I to III Kellgren–Lawrence) of KOA were qualified for meta-analysis. Compared with sham or placebo group, the ESWT group had a significant decrease of VAS rest score (0.90[0.12~1.67] as mean difference [95% confidence interval]) and pain score WOMAC (2.49[1.22~3.76]), and a significant improvement of physical performance with a decrease of the scores of WOMAC activities (8.18[3.97~12.39]), LI (3.47[1.68~5.26]), and KOOS (5.87[1.73~ 10.00]) in the post-treatment period. There were also a significant decrease of WOMAC pain score (2.83[2.12~3.53]) and a significant decrease of the scores of WOMAC activities (9.47[7.65~11.28]) and LI (4.12[2.34 to 5.89]) in the following period. Besides, compared with other treatment groups, ESWT also displayed the improvement in pain and physical performance, but it is not significant. Conclusions: The ESWT was effective and valuable method in pain relief as well as in improving physical activities in the participants with mild-to-moderate KOA. Clinical Relevance: There are the effects of ESWT on pain relief and the improvement of physical performance in the with KOA.

Keywords: knee osteoarthritis, extracorporeal shockwave therapy, pain relief, physical performance, shockwave

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Authors: Karan Sharma, Ajay Kumar

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Epileptic seizure is a type of disease due to which electrical charge in the brain flows abruptly resulting in abnormal activity by the subject. One percent of total world population gets epileptic seizure attacks.Due to abrupt flow of charge, EEG (Electroencephalogram) waveforms change. On the display appear a lot of spikes and sharp waves in the EEG signals. Detection of epileptic seizure by using conventional methods is time-consuming. Many methods have been evolved that detect it automatically. The initial part of this paper provides the review of techniques used to detect epileptic seizure automatically. The automatic detection is based on the feature extraction and classification patterns. For better accuracy decomposition of the signal is required before feature extraction. A number of parameters are calculated by the researchers using different techniques e.g. approximate entropy, sample entropy, Fuzzy approximate entropy, intrinsic mode function, cross-correlation etc. to discriminate between a normal signal & an epileptic seizure signal.The main objective of this review paper is to present the variations in the EEG signals at both stages (i) Interictal (recording between the epileptic seizure attacks). (ii) Ictal (recording during the epileptic seizure), using most appropriate methods of analysis to provide better healthcare diagnosis. This research paper then investigates the effects of a noninvasive healing therapy on the subjects by studying the EEG signals using latest signal processing techniques. The study has been conducted with Reiki as a healing technique, beneficial for restoring balance in cases of body mind alterations associated with an epileptic seizure. Reiki is practiced around the world and is recommended for different health services as a treatment approach. Reiki is an energy medicine, specifically a biofield therapy developed in Japan in the early 20th century. It is a system involving the laying on of hands, to stimulate the body’s natural energetic system. Earlier studies have shown an apparent connection between Reiki and the autonomous nervous system. The Reiki sessions are applied by an experienced therapist. EEG signals are measured at baseline, during session and post intervention to bring about effective epileptic seizure control or its elimination altogether.

Keywords: EEG signal, Reiki, time consuming, epileptic seizure

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Authors: Muhammad Shafi'u Adamu

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This study examined the effects of two Group Cognitive Behavioural Therapies (Cognitive Restructuring and Rational Emotive Behavioural Therapy) on Psychological Distress of awaiting-trial Inmates in Correctional Centres in North-West, Nigeria. The study had four specific objectives, four research questions, and four null hypotheses. The study used a quasi-experimental design that involved pre-test and post-test. The population comprised of all 7,962 awaiting-trial inmates in correctional centres in North-west, Nigeria. 131 awaiting trial inmates from three intact Correctional Centres were randomly selected using the census technique. The respondents were sampled and randomly put into 3 groups (CR, REBT and Control). Kessler Psychological Distress Scale (K10) was adapted for data collection in the study. The instrument was validated by experts and subjected to pilot study using Cronbach's Alpha with reliability co-efficient of 0.772. Each group received treatment for 8 consecutive weeks (60 minutes/week). Data collected from the field were subjected to descriptive statistics of mean, standard deviation and mean difference to answer the research questions. Inferential statistics of ANOVA and independent sample t-test were used to test the null hypotheses at P≤ 0.05 level of significance. Results in the study revealed that there was no significant difference among the pre-treatment mean scores of experimental and control groups. Statistical evidence also showed a significant difference among the mean sores of the three groups, and thus, results of the Post Hoc multiple-comparison test indicating the posttreatment reduction of psychological distress on the awaiting-trial inmates. Documented output also showed a significant difference between the post-treatment psychologically distressed mean scores of male and female awaiting-trial inmates, but there was no difference on those exposed to REBT. The research recommends that a standardized structured CBT counselling technique treatment should be designed for correctional centres across Nigeria, and CBT counselling techniques could be used in the treatment of PD in both correctional and clinical settings.

Keywords: awaiting-trial inmates, cognitive restructuring, correctional centres, group cognitive behavioural therapies, rational emotive behavioural therapy

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Authors: Rajendar Kandan, Mohammad Zakaria Alli, Hong Ong

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Cloud computing is a business model which provides an easier management of computing resources. Cloud users can request virtual machine and install additional softwares and configure them if needed. However, user can also request virtual appliance which provides a better solution to deploy application in much faster time, as it is ready-built image of operating system with necessary softwares installed and configured. Large numbers of virtual appliances are available in different image format. User can download available appliances from public marketplace and start using it. However, information published about the virtual appliance differs from each providers leading to the difficulty in choosing required virtual appliance as it is composed of specific OS with standard software version. However, even if user choses the appliance from respective providers, user doesn’t have any flexibility to choose their own set of softwares with required OS and application. In this paper, we propose a referenced architecture for dynamically customizing virtual appliance and provision them in an easier manner. We also add our experience in integrating our proposed architecture with public marketplace and Mi-Cloud, a cloud management software.

Keywords: cloud computing, marketplace, virtualization, virtual appliance

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Authors: Henni Sid Ahmed, Belbachir Mohamed Faouzi, Jean Caelen

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Behavior recognition has been studied for realizing drivers assisting system and automated navigation and is an important studied field in the intelligent Building. In this paper, a recognition method of behavior recognition separated from a real image was studied. Images were divided into several categories according to the actual weather, distance and angle of view etc. SIFT was firstly used to detect key points and describe them because the SIFT (Scale Invariant Feature Transform) features were invariant to image scale and rotation and were robust to changes in the viewpoint and illumination. My goal is to develop a robust and reliable system which is composed of two fixed cameras in every room of intelligent building which are connected to a computer for acquisition of video sequences, with a program using these video sequences as inputs, we use SIFT represented different images of video sequences, and SVM (support vector machine) Lights as a programming tool for classification of images in order to classify people’s behaviors in the intelligent building in order to give maximum comfort with optimized energy consumption.

Keywords: video analysis, people behavior, intelligent building, classification

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Authors: Adel Edwar Waheeb Louka

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deeplearning, image processing, machine learningSarapin, intraarticular, chronic knee pain, osteoarthritisFNS, trauma, hip, neck femur fracture, minimally invasive surgery

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Authors: Naghmeh Sadat Nabavi

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Women´s role in political participation, despite its limitations, is undoubtedly the most essential and effective part of Iran. In all political events throughout Iran's history, women have been pioneers, although they have been limited from getting political positions, even in the parliament. In recent years, movements and protests have been formed by Iranian women to respect natural human rights, especially for women. These movements are accompanied and sometimes guided by female celebrities, the most important of which are actresses. In 2017, this cooperation reached its highest level compared to the past, and the political participation of actresses in support of Hassan Rouhani in the presidential elections brought people who were hesitant to vote to the polls. This type of participation of actresses is seen in the recent protest of #Woman_Life_Freedom in 2022 and 2023 that still continues.

Keywords: political participation, presidential election, actresses, celebrities, social media, women, Iran

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Authors: Joanna Dzieńdziora

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Lobbying is an instrument of influence in various decision-making processes. It is also the underestimated issue as a research problem. The lack of research on the modern lobbyist competencies is the most crucial element. The paper presents attempts of finding answers to the following questions: Who should run the lobbying activity? What competencies should a lobbyist possess in order to implement lobbying activities effectively? Searching for answers for the mentioned above questions requires positioning the opportunity to change the image of lobbying in the area of competencies of entities that provide lobbying activities. The aim of the paper is presenting the lobbyist competencies profile in the framework of his professional role. The essence of lobbying activity and its significance in the modern economy as well as areas, the scope of lobbying activities, diagnosis of a modern lobbyist’s competences, lobbyist’s competencies profile that is focused on the professionalization of the lobbying activity, will have been presented in this paper. Indicated research tasks let emerge lobbyist’s competencies in the way that allows identifying and elaborating the lobbyist competencies profile. The profile lets improve lobbying activities. Its elaboration is based on the author’s research results analysis. Taking into consideration the shortages within the theory and research on the lobbying activity, the implementation of this research enables to fill the cognitive gap existing in the theory of management sciences.

Keywords: competencies, competencies profile, lobbying, lobbyist

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Authors: Pachawo Bisani, Goodall Nyirenda

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The Malawi National Antiretroviral Therapy (NART) Electronic Medical Record (EMR) system was designed and developed with guidance from the Ministry of Health through the Department of HIV and AIDS (DHA) with the aim of supporting the management of HIV patient data and reporting in high prevalence ART clinics. As of 2021, the system has been scaled up to over 206 facilities across the country. The system is integrated with the clinical decision support system (CDSS) to assist healthcare providers in making a decision about an individual patient at a particular point in time. Despite NART EMR undergoing several evaluations and assessments, little has been done to evaluate the clinical decision support system in the NART EMR system. Hence, the study aimed to evaluate the use of CDSS in the NART EMR system in Malawi. The study adopted a mixed-method approach, and data was collected through interviews, observations, and questionnaires. The study has revealed that the CDSS tools were integrated into the ART clinic workflow, making it easy for the user to use it. The study has also revealed challenges in system reliability and information accuracy. Despite the challenges, the study further revealed that the system is effective and efficient, and overall, users are satisfied with the system. The study recommends that the implementers focus more on the logic behind the clinical decision-support intervention in order to address some of the concerns and enhance the accuracy of the information supplied. The study further suggests consulting the system's actual users throughout implementation.

Keywords: clinical decision support system, electronic medical record system, usability, antiretroviral therapy

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Authors: Shweh Fern Loo, Jun Yin Ong, Than Zaw Oo

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Acute respiratory distress syndrome (ARDS) is a common serious condition of bilateral lung infiltrates that develops secondary to various underlying conditions such as diseases or injuries. ARDS with severe hypercapnia is associated with higher ICU mortality and morbidity. Venovenous Extracorporeal membrane oxygenation (VV-ECMO) support has been established to avert life-threatening hypoxemia and hypercapnic respiratory failure despite optimal conventional mechanical ventilation. However, VV-ECMO is relatively not advisable in particular groups of patients, especially in multi-organ failure, advanced age, hemorrhagic complications and irreversible central nervous system pathology. We presented a case of a 79-year-old Chinese lady without any pre-existing lung disease admitted to our hospital intensive care unit (ICU) after acute presentation of breathlessness and chest pain. After extensive workup, she was diagnosed with rapidly progressing acute interstitial pneumonia with ARDS and hypercapnia respiratory failure. The patient received lung protective strategies of mechanical ventilation and neuromuscular blockage therapy as per clinical guidelines. However, hypercapnia respiratory failure was refractory, and she was deemed not a good candidate for VV-ECMO support given her advanced age and high vasopressor requirements from shock. Alternative therapy with extracorporeal CO2 removal (ECCO2R) was considered and implemented. The patient received 12 days of ECCO2R paired with muscle paralysis, optimization of lung-protective mechanical ventilation and dialysis. Unfortunately, the patient still had refractory hypercapnic respiratory failure with dual vasopressor support despite prolonged therapy. Given failed and futile medical treatment, the family opted for withdrawal of care, a conservative approach, and comfort care, which led to her demise. The effectivity of extracorporeal CO2 removal may depend on disease burden, involvement and severity of the disease. There is insufficient data to make strong recommendations about its benefit-risk ratio for ECCO2R devices, and further studies and data would be required. Nonetheless, ECCO2R can be considered an alternative treatment for refractory hypercapnic respiratory failure patients who are unsuitable for initiating venovenous ECMO.

Keywords: extracorporeal CO2 removal (ECCO2R), acute respiratory distress syndrome (ARDS), acute interstitial pneumonia (AIP), hypercapnic respiratory failure

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Authors: Yiyuan David Hu, Alex Lindqwister, Samuel B. Klein, Karen Moodie, Norman A. Paradis

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Introduction: Pseudo-Pulseless Electrical Activity (p-PEA) is a lifeless form of profound cardiac shock characterized by measurable cardiac mechanical activity without clinically detectable pulses. Patients in pseudo-PEA carry different prognoses than those in true PEA and may require different therapies. End-tidal carbon dioxide (ET-CO2) has been studied in ventricular fibrillation and true PEA but in p-PEA. We utilized an hypoxic porcine model to characterize the performance of ET-CO2 in resuscitation from p-PEA. Hypothesis: Capnography correlates to the number of required interventions for resuscitation from p-PEA. Methods: Female swine (N = 14) under intravenous anesthesia were instrumented with aortic and right atrial micromanometer pressure. ECG and ET-CO2 were measured continuously. p-PEA was induced by ventilation with 6% oxygen in 94% nitrogen and was defined as a systolic aortic (Ao) pressure less than 40 mmHg. Pigs were grouped based on the interventions required to achieve ROSC: 100%O2, 100%O2 + CPR, 100%O2 + CPR + epinephrine. Results: End tidal CO2 reliably predicted O2 therapy vs CPR-based interventions needed for resuscitation (Figure 1). Pigs who would recover with 100%O2 only had a mean ET-CO2 slope of 0.039 ± 0.013 [ R2 = 0.68], those requiring oxygen + CPR had a slope of -0.15 ± 0.016 [R2 = 0.95], and those requiring oxygen + CPR + epinephrine had a slope of -0.12 ± 0.031 [R2 = 0.79]. Conclusions: In a porcine model of hypoxic p-PEA, measured ET-CO2 appears to be strongly correlated with the required interventions needed for ROSC. If confirmed clinically, these results indicate that ET-CO2 may be useful in guiding therapy in patients suffering p-PEA cardiac arrest.

Keywords: pseudo-PEA, resuscitation, capnography, hypoxic pseudo-PEA

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Authors: Murat Demir Aydin, Elanur Celebi

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Adhesively bonded joints are used as an alternative to traditional joining methods due to the important advantages they provide. The most important consideration in the use of adhesively bonded joints is that these joints have appropriate requirements for their use in terms of safety. In order to ensure control of this condition, damage analysis of the adhesively bonded joints should be performed by determining the mechanical properties of the adhesives. When the literature is investigated; it is generally seen that the mechanical properties of adhesives are determined by traditional measurement methods. In this study, to determine the mechanical properties of adhesives, the Digital Image Correlation (DIC) method, which can be an alternative to traditional measurement methods, has been used. The DIC method is a new optical measurement method which is used to determine the parameters of displacement and strain in an appropriate and correct way. In this study, tensile tests of Thick Adherent Shear Test (TAST) samples formed using DP410 liquid structural adhesive and steel materials and bulk tensile specimens formed using and DP410 liquid structural adhesive was performed. The displacement and strain values of the samples were determined by DIC method and the shear stress-strain curves of the adhesive for TAST specimens and the tensile strain curves of the bulk adhesive specimens were obtained. Various methods such as numerical methods are required as conventional measurement methods (strain gauge, mechanic extensometer, etc.) are not sufficient in determining the strain and displacement values of the very thin adhesive layer such as TAST samples. As a result, the DIC method removes these requirements and easily achieves displacement measurements with sufficient accuracy.

Keywords: structural adhesive, adhesively bonded joints, digital image correlation, thick adhered shear test (TAST)

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Authors: Tienfuan Kerh, Hsienchang Lu, Rob Saunders

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Shoreline erosion problems caused by global warming and sea level rising may result in losing of land areas, so it should be examined regularly to reduce possible negative impacts. Initially in this study, three sets of survey images obtained from the years of 1990, 2001, and 2010, respectively, are digitalized by using graphical software to establish the spatial coordinates of six major beaches around the island of Taiwan. Then, by overlaying the known multi-period images, the change of shoreline can be observed from their distribution of coordinates. In addition, the neural network approximation is used to develop a model for predicting shoreline variation in the years of 2015 and 2020. The comparison results show that there is no significant change of total sandy area for all beaches in the three different periods. However, the prediction results show that two beaches may exhibit an increasing of total sandy areas under a statistical 95% confidence interval. The proposed method adopted in this study may be applicable to other shorelines of interest around the world.

Keywords: digitalized shoreline coordinates, survey image overlaying, neural network approximation, total beach sandy areas

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Authors: Ole-Christian Galbo Engstrøm, Erik Schou Dreier, Birthe Møller Jespersen, Kim Steenstrup Pedersen

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Grain quality analysis is a multi-parameterized problem that includes a variety of qualitative and quantitative parameters such as grain type classification, damage type classification, and nutrient regression. Currently, these parameters require human inspection, a multitude of instruments employing a variety of sensor technologies, and predictive model types or destructive and slow chemical analysis. This paper investigates the feasibility of applying near-infrared hyperspectral imaging (NIR-HSI) to grain quality analysis. For this study two datasets of NIR hyperspectral images in the wavelength range of 900 nm - 1700 nm have been used. Both datasets contain images of sparsely and densely packed grain kernels. The first dataset contains ~87,000 image crops of bulk wheat samples from 63 harvests where protein value has been determined by the FOSS Infratec NOVA which is the golden industry standard for protein content estimation in bulk samples of cereal grain. The second dataset consists of ~28,000 image crops of bulk grain kernels from seven different wheat varieties and a single rye variety. In the first dataset, protein regression analysis is the problem to solve while variety classification analysis is the problem to solve in the second dataset. Deep convolutional neural networks (CNNs) have the potential to utilize spatio-spectral correlations within a hyperspectral image to simultaneously estimate the qualitative and quantitative parameters. CNNs can autonomously derive meaningful representations of the input data reducing the need for advanced preprocessing techniques required for classical chemometric model types such as artificial neural networks (ANNs) and partial least-squares regression (PLS-R). A comparison between different CNN architectures utilizing 2D and 3D convolution is conducted. These results are compared to the performance of ANNs and PLS-R. Additionally, a variety of preprocessing techniques from image analysis and chemometrics are tested. These include centering, scaling, standard normal variate (SNV), Savitzky-Golay (SG) filtering, and detrending. The results indicate that the combination of NIR-HSI and CNNs has the potential to be the foundation for an automatic system unifying qualitative and quantitative grain quality analysis within a single sensor technology and predictive model type.

Keywords: deep learning, grain analysis, hyperspectral imaging, preprocessing techniques

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Authors: Hadeer R. M. Tawfik, Rania A. K. Birry, Amani A. Saad

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Eyes are considered to be the most sensitive and important organ for human being. Thus, any eye disorder will affect the patient in all aspects of life. Cataract is one of those eye disorders that lead to blindness if not treated correctly and quickly. This paper demonstrates a model for automatic detection, classification, and grading of cataracts based on image processing techniques and artificial intelligence. The proposed system is developed to ease the cataract diagnosis process for both ophthalmologists and patients. The wavelet transform combined with 2D Log Gabor Wavelet transform was used as feature extraction techniques for a dataset of 120 eye images followed by a classification process that classified the image set into three classes; normal, early, and advanced stage. A comparison between the two used classifiers, the support vector machine SVM and the artificial neural network ANN were done for the same dataset of 120 eye images. It was concluded that SVM gave better results than ANN. SVM success rate result was 96.8% accuracy where ANN success rate result was 92.3% accuracy.

Keywords: cataract, classification, detection, feature extraction, grading, log-gabor, neural networks, support vector machines, wavelet

Procedia PDF Downloads 333

Authors: Sher Muhammad, Mirza Muhammad Waqar

Abstract:

It is necessary to monitor and identify mangroves types and spatial extent near coastal areas because it plays an important role in coastal ecosystem and environmental protection. This research aims at identifying and mapping mangroves types along Karachi coast ranging from 24.79 to 24.85 degree in latitude and 66.91 to 66.97 degree in longitude using hyperspectral remote sensing data and techniques. Image acquired during February, 2012 through Hyperion sensor have been used for this research. Image preprocessing includes geometric and radiometric correction followed by Minimum Noise Fraction (MNF) and Pixel Purity Index (PPI). The output of MNF and PPI has been analyzed by visualizing it in n-dimensions for end-member extraction. Well-distributed clusters on the n-dimensional scatter plot have been selected with the region of interest (ROI) tool as end members. These end members have been used as an input for classification techniques applied to identify and map mangroves species including Spectral Angle Mapper (SAM), Spectral Feature Fitting (SFF), and Spectral Information Diversion (SID). Only two types of mangroves namely Avicennia Marina (white mangroves) and Avicennia Germinans (black mangroves) have been observed throughout the study area.

Keywords: mangrove, hyperspectral, hyperion, SAM, SFF, SID

Procedia PDF Downloads 362

Authors: Nasser Y. Mahamoud, Pierre Dehombreux, Hassan E. Robleh

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Industrial companies may be confronted with questions about their choice of maintenance policy. This choice must be guided by several numbers of decision criteria or objectives related to their production or service activities but also to their level of development and their investment prospects. A decision-support methodology to choose a maintenance policy (corrective, systematic or conditional preventive, predictive, opportunistic or not) is proposed to facilitate this choice using the main categories of the most important decision criteria. The different steps of this methodology are illustrated using theoretical case: identification of the different maintenance alternatives, determining the structure of the most important categories of the decision criteria, assessing the different maintenance policies on to the criteria by using an ordinal preference relation, and finally ranking the different maintenance policies.

Keywords: maintenance policy, decision criteria, decision-making process, AHP

Procedia PDF Downloads 333

Authors: N. Dlamini, Mpumelelo G. Ndlela, Philisiwe Dlamini, Nicholus Kisyeri, Bhekizitha Sithole

Abstract:

Accessing health services during the COVID-19 pandemic have exacerbated scarcity to routine medication. To ensure continuous accessibility to services, Eswatini launched Community Health Commodities Distribution (CHCD). Eligible Antiretroviral Therapy(ART) stable clients (VL<1,000) and patients on Non-Communicable Disease (NCD) medications were attended at community pick up points (PUP) based on distance between clients’ residence and the public health facility. Services provided includes ART and Pre-Exposure prophylaxis (PrEP) refills and NCD drug refills). The number of community PUP was 14% higher than health facility visits. Among all medications and commodities distributed between April and October 2020 at the PUP, 64% were HIV-related (HIV rapid test, HIVST, VL test, PrEP meds), and 36% were NCD related. The rapid roll out of CHCD during COVID-19 pandemic reduced the risk of COVID-19 transmission to clients as travel to health facilities was eliminated. It Additionally increased access to commodities during COVID-19-driven lockdown, decongested health facilities, integrated model of care, and increase service coverage. It was also noted that CHCD added different curative and HIV related services based on client specific needs and availability of the commodities.

Keywords: community health commodities distribution, pick up points, antiretroviral therapy, pre-exposure prophylaxis

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Authors: Insaf Bellamine, Hamid Tairi

Abstract:

Detecting moving objects in sequences is an essential step for video analysis. This paper mainly contributes to the Color Space-Time Interest Points (CSTIP) extraction and detection. We propose a new method for detection of moving objects. Two main steps compose the proposed method. First, we suggest to apply the algorithm of the detection of Color Space-Time Interest Points (CSTIP) on both components of the Color Structure-Texture Image Decomposition which is based on a Partial Differential Equation (PDE): a color geometric structure component and a color texture component. A descriptor is associated to each of these points. In a second stage, we address the problem of grouping the points (CSTIP) into clusters. Experiments and comparison to other motion detection methods on challenging sequences show the performance of the proposed method and its utility for video analysis. Experimental results are obtained from very different types of videos, namely sport videos and animation movies.

Keywords: Color Space-Time Interest Points (CSTIP), Color Structure-Texture Image Decomposition, Motion Detection, clustering

Procedia PDF Downloads 378

Authors: Zachary Pan

Abstract:

Our general mission is to solve the problem of classifying images into different body part types and deciding if each of them is healthy or not. However, for now, we will determine healthiness for only one-sixth of the body parts, specifically the chest. We will detect pneumonia in X-ray scans of those chest images. With this type of AI, doctors can use it as a second opinion when they are taking CT or X-ray scans of their patients. Another ad-vantage of using this machine learning classifier is that it has no human weaknesses like fatigue. The overall ap-proach to this problem is to split the problem into two parts: first, classify the image, then determine if it is healthy. In order to classify the image into a specific body part class, the body parts dataset must be split into test and training sets. We can then use many models, like neural networks or logistic regression models, and fit them using the training set. Now, using the test set, we can obtain a realistic accuracy the models will have on images in the real world since these testing images have never been seen by the models before. In order to increase this testing accuracy, we can also apply many complex algorithms to the models, like multiplicative weight update. For the second part of the problem, to determine if the body part is healthy, we can have another dataset consisting of healthy and non-healthy images of the specific body part and once again split that into the test and training sets. We then use another neural network to train on those training set images and use the testing set to figure out its accuracy. We will do this process only for the chest images. A major conclusion reached is that convolutional neural networks are the most reliable and accurate at image classification. In classifying the images, the logistic regression model, the neural network, neural networks with multiplicative weight update, neural networks with the black box algorithm, and the convolutional neural network achieved 96.83 percent accuracy, 97.33 percent accuracy, 97.83 percent accuracy, 96.67 percent accuracy, and 98.83 percent accuracy, respectively. On the other hand, the overall accuracy of the model that de-termines if the images are healthy or not is around 78.37 percent accuracy.

Keywords: body part, healthcare, machine learning, neural networks

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Authors: Noor Ayuni Che Zakaria, Takashi Komeda, Cheng Yee Low, Kaoru Inoue, Fazah Akhtar Hanapiah

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Previous studies have shown that there are arguments regarding the reliability and validity of the Ashworth and Modified Ashworth Scale towards evaluating patients diagnosed with upper limb disorders. These evaluations depended on the raters’ experiences. This initiated us to develop an upper limb disorder part-task trainer that is able to simulate consistent upper limb disorders, such as spasticity and rigidity signs, based on the Modified Ashworth Scale to improve the variability occurring between raters and intra-raters themselves. By providing consistent signs, novice therapists would be able to increase training frequency and exposure towards various levels of signs. A total of 22 physiotherapists and occupational therapists participated in the study. The majority of the therapists agreed that with current therapy education, they still face problems with inter-raters and intra-raters variability (strongly agree 54%; n = 12/22, agree 27%; n = 6/22) in evaluating patients’ conditions. The therapists strongly agreed (72%; n = 16/22) that therapy trainees needed to increase their frequency of training; therefore believe that our initiative to develop an upper limb disorder training tool will help in improving the clinical education field (strongly agree and agree 63%; n = 14/22).

Keywords: upper limb disorder, clinical education tool, inter/intra-raters variability, spasticity, modified Ashworth scale

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Authors: A. Dionísio, L. Roseiro, J. Fonseca, P. Nicolau

Abstract:

Thermography is a non-radiating and contact-free technology which can be used to monitor skin temperature. The efficiency and safety of thermography technology make it a useful tool for detecting and locating thermal changes in skin surface, characterized by increases or decreases in temperature. This work intends to be a contribution for the use of thermography as a methodology for evaluation of skin temperature in the context of orofacial biomechanics. The study aims to identify the oscillations of skin temperature in the left and right hemiface regions of the masseter muscle, during and after thermal stimulus, and estimate the time required to restore the initial temperature after the application of the stimulus. Using a FLIR T430sc camera, a data acquisition protocol was followed with a group of eight volunteers, aged between 22 and 27 years. The tests were performed in a controlled environment with the volunteers in a comfortably static position. The thermal stimulus involves the use of an ice volume with controlled size and contact surface. The skin surface temperature was recorded in two distinct situations, namely without further stimulus and with the additions of a stimulus obtained by a chewing gum. The data obtained were treated using FLIR Research IR Max software. The time required to recover the initial temperature ranged from 20 to 52 minutes when no stimulus was added and varied between 8 and 26 minutes with the chewing gum stimulus. These results show that recovery is faster with the addition of the stimulus and may guide clinicians regarding the pre and post-operative times with ice therapy, in the presence or absence of mechanical stimulus that increases muscle functions (e.g. phonetics or mastication).

Keywords: thermography, orofacial biomechanics, skin temperature, ice therapy

Procedia PDF Downloads 255