Search results for: medical resonance (MR) images

Authors: Kristina Sakalauskiene, Renaldas Raisutis, Gintare Linkeviciute, Skaidra Valiukeviciene

Abstract:

Cutaneous melanoma is a melanocytic skin tumour, which has a very poor prognosis while is highly resistant to treatment and tends to metastasize. Thickness of melanoma is one of the most important biomarker for stage of disease, prognosis and surgery planning. In this study, we hypothesized that the automatic analysis of spectrophotometric images and high-frequency ultrasonic 2D data can improve differential diagnosis of cutaneous melanoma and provide additional information about tumour penetration depth. This paper presents the novel complex automatic system for non-invasive melanocytic skin tumour differential diagnosis and penetration depth evaluation. The system is composed of region of interest segmentation in spectrophotometric images and high-frequency ultrasound data, quantitative parameter evaluation, informative feature extraction and classification with linear regression classifier. The segmentation of melanocytic skin tumour region in ultrasound image is based on parametric integrated backscattering coefficient calculation. The segmentation of optical image is based on Otsu thresholding. In total 29 quantitative tissue characterization parameters were evaluated by using ultrasound data (11 acoustical, 4 shape and 15 textural parameters) and 55 quantitative features of dermatoscopic and spectrophotometric images (using total melanin, dermal melanin, blood and collagen SIAgraphs acquired using spectrophotometric imaging device SIAscope). In total 102 melanocytic skin lesions (including 43 cutaneous melanomas) were examined by using SIAscope and ultrasound system with 22 MHz center frequency single element transducer. The diagnosis and Breslow thickness (pT) of each MST were evaluated during routine histological examination after excision and used as a reference. The results of this study have shown that automatic analysis of spectrophotometric and high frequency ultrasound data can improve non-invasive classification accuracy of early-stage cutaneous melanoma and provide supplementary information about tumour penetration depth.

Keywords: cutaneous melanoma, differential diagnosis, high-frequency ultrasound, melanocytic skin tumours, spectrophotometric imaging

Procedia PDF Downloads 270

Authors: Manaranjan Pradhan, Shailaja Grover, U. Dinesh Kumar

Abstract:

Farming community in India, as well as other parts of the world, is one of the highly stressed communities due to reasons such as increasing input costs (cost of seeds, fertilizers, pesticide), droughts, reduced revenue leading to farmer suicides. Lack of integrated farm advisory system in India adds to the farmers problems. Farmers need right information during the early stages of crop’s lifecycle to prevent damage and loss in revenue. In this paper, we use deep learning techniques to develop an early warning system for detection of crop diseases using images taken by farmers using their smart phone. The research work leads to building a smart assistant using analytics and big data which could help the farmers with early diagnosis of the crop diseases and corrective actions. The classical approach for crop disease management has been to identify diseases at crop level. Recently, ImageNet Classification using the convolutional neural network (CNN) has been successfully used to identify diseases at individual plant level. Our model uses convolution filters, max pooling, dense layers and dropouts (to avoid overfitting). The models are built for binary classification (healthy or not healthy) and multi class classification (identifying which disease). Transfer learning is used to modify the weights of parameters learnt through ImageNet dataset and apply them on crop diseases, which reduces number of epochs to learn. One shot learning is used to learn from very few images, while data augmentation techniques are used to improve accuracy with images taken from farms by using techniques such as rotation, zoom, shift and blurred images. Models built using combination of these techniques are more robust for deploying in the real world. Our model is validated using tomato crop. In India, tomato is affected by 10 different diseases. Our model achieves an accuracy of more than 95% in correctly classifying the diseases. The main contribution of our research is to create a personal assistant for farmers for managing plant disease, although the model was validated using tomato crop, it can be easily extended to other crops. The advancement of technology in computing and availability of large data has made possible the success of deep learning applications in computer vision, natural language processing, image recognition, etc. With these robust models and huge smartphone penetration, feasibility of implementation of these models is high resulting in timely advise to the farmers and thus increasing the farmers' income and reducing the input costs.

Keywords: analytics in agriculture, CNN, crop disease detection, data augmentation, image recognition, one shot learning, transfer learning

Procedia PDF Downloads 119

Authors: Adel Belayadi, Ahmed Mougari, Boualem Bourahla

Abstract:

Molecular junctions are currently considered a promising style in the miniaturization of electronic devices. In this contribution, we provide a tight-binding model to investigate the quantum transport properties across-molecular junctions sandwiched between 2D-graphene nanoribbons in the zigzag direction. We investigate, in particular, the effect of embedded atoms such as Gold and Silicon across the molecular junction. The results exhibit a resonance behavior in terms of incident Fermi levels, depending on the molecular junction type. Additionally, the transport properties under a perpendicular magnetic field exhibit an oscillation for the transmittance versus the magnetic field strength.

Keywords: molecular junction, 2D-graphene nanoribbons, quantum transport properties, magnetic field

Procedia PDF Downloads 96

Authors: Chih-Chung Huang, Po-Hsun Peng

Abstract:

Ultrasound transient elastography has become a valuable tool for many clinical diagnoses, such as liver diseases and breast cancer. The pathological tissue can be distinguished by elastography due to its stiffness is different from surrounding normal tissues. An ultrafast frame rate of ultrasound imaging is needed for transient elastography modality. The elastography obtained in the ultrafast system suffers from a low quality for resolution, and affects the robustness of the transient elastography. In order to overcome these problems, a coherent plane wave compounding technique has been proposed for conventional ultrasound system which the operating frequency is around 3-15 MHz. The purpose of this study is to develop a novel beamforming technique for high frequency ultrasound coherent plane-wave compounding imaging and the simulated results will provide the standards for hardware developments. Plane-wave compounding imaging produces a series of low-resolution images, which fires whole elements of an array transducer in one shot with different inclination angles and receives the echoes by conventional beamforming, and compounds them coherently. Simulations of plane-wave compounding image and focused transmit image were performed using Field II. All images were produced by point spread functions (PSFs) and cyst phantoms with a 64-element linear array working at 35MHz center frequency, 55% bandwidth, and pitch of 0.05 mm. The F number is 1.55 in all the simulations. The simulated results of PSFs and cyst phantom which were obtained using single, 17, 43 angles plane wave transmission (angle of each plane wave is separated by 0.75 degree), and focused transmission. The resolution and contrast of image were improved with the number of angles of firing plane wave. The lateral resolutions for different methods were measured by -10 dB lateral beam width. Comparison of the plane-wave compounding image and focused transmit image, both images exhibited the same lateral resolution of 70 um as 37 angles were performed. The lateral resolution can reach 55 um as the plane-wave was compounded 47 angles. All the results show the potential of using high-frequency plane-wave compound imaging for realizing the elastic properties of the microstructure tissue, such as eye, skin and vessel walls in the future.

Keywords: plane wave imaging, high frequency ultrasound, elastography, beamforming

Procedia PDF Downloads 538

Authors: D. Madhushanka, Y. Liu, H. C. Fernando

Abstract:

Fires are one of the foremost factors of land surface disturbance in diverse ecosystems, causing soil erosion and land-cover changes and atmospheric effects affecting people's lives and properties. Generally, the severity of the fire is calculated as the Normalized Burn Ratio (NBR) index. This is performed manually by comparing two images obtained afterward. Then by using the bitemporal difference of the preprocessed satellite images, the dNBR is calculated. The burnt area is then classified as either unburnt (dNBR<0.1) or burnt (dNBR>= 0.1). Furthermore, Wildfire Severity Assessment (WSA) classifies burnt areas and unburnt areas using classification levels proposed by USGS and comprises seven classes. This procedure generates a burn severity report for the area chosen by the user manually. This study is carried out with the objective of producing an automated tool for the above-mentioned process, namely the World Wildfire Severity Assessment Tool (WWSAT). It is implemented in Google Earth Engine (GEE), which is a free cloud-computing platform for satellite data processing, with several data catalogs at different resolutions (notably Landsat, Sentinel-2, and MODIS) and planetary-scale analysis capabilities. Sentinel-2 MSI is chosen to obtain regular processes related to burnt area severity mapping using a medium spatial resolution sensor (15m). This tool uses machine learning classification techniques to identify burnt areas using NBR and to classify their severity over the user-selected extent and period automatically. Cloud coverage is one of the biggest concerns when fire severity mapping is performed. In WWSAT based on GEE, we present a fully automatic workflow to aggregate cloud-free Sentinel-2 images for both pre-fire and post-fire image compositing. The parallel processing capabilities and preloaded geospatial datasets of GEE facilitated the production of this tool. This tool consists of a Graphical User Interface (GUI) to make it user-friendly. The advantage of this tool is the ability to obtain burn area severity over a large extent and more extended temporal periods. Two case studies were carried out to demonstrate the performance of this tool. The Blue Mountain national park forest affected by the Australian fire season between 2019 and 2020 is used to describe the workflow of the WWSAT. This site detected more than 7809 km2, using Sentinel-2 data, giving an error below 6.5% when compared with the area detected on the field. Furthermore, 86.77% of the detected area was recognized as fully burnt out, of which high severity (17.29%), moderate-high severity (19.63%), moderate-low severity (22.35%), and low severity (27.51%). The Arapaho and Roosevelt National Forest Park, California, the USA, which is affected by the Cameron peak fire in 2020, is chosen for the second case study. It was found that around 983 km2 had burned out, of which high severity (2.73%), moderate-high severity (1.57%), moderate-low severity (1.18%), and low severity (5.45%). These spots also can be detected through the visual inspection made possible by cloud-free images generated by WWSAT. This tool is cost-effective in calculating the burnt area since satellite images are free and the cost of field surveys is avoided.

Keywords: burnt area, burnt severity, fires, google earth engine (GEE), sentinel-2

Procedia PDF Downloads 235

Authors: Mrinmoy Dhara, Vivek K. Sengar, Shovan L. Chattoraj, Soumiya Bhattacharjee

Abstract:

Remote sensing techniques have emerged as an asset for various geological studies. Satellite images obtained by different sensors contain plenty of information related to the terrain. Digital image processing further helps in customized ways for the prospecting of minerals. In this study, an attempt has been made to map the hydrothermally altered zones using multispectral and hyperspectral datasets of South East Rajasthan. Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) and Hyperion (Level1R) dataset have been processed to generate different Band Ratio Composites (BRCs). For this study, ASTER derived BRCs were generated to delineate the alteration zones, gossans, abundant clays and host rocks. ASTER and Hyperion images were further processed to extract mineral end members and classified mineral maps have been produced using Spectral Angle Mapper (SAM) method. Results were validated with the geological map of the area which shows positive agreement with the image processing outputs. Thus, this study concludes that the band ratios and image processing in combination play significant role in demarcation of alteration zones which may provide pathfinders for mineral prospecting studies.

Keywords: ASTER, hyperion, band ratios, alteration zones, SAM

Procedia PDF Downloads 279

Authors: Sue Hershberger Yoder, Jon Yoder

Abstract:

“The appearance does not hide the essence, it reveals it; it is the essence.”—Jean-Paul Sartre, Being and Nothingness Three decades ago, transarchitect Marcos Novak developed an early form of algorithmic animation he called “liquid architecture.” In that project, digitally floating forms morphed seamlessly in cyberspace without claiming to evolve or improve. Change itself was seen as inevitable. And although some imagistic moments certainly stood out, none was hierarchically privileged over another. That project challenged longstanding assumptions about creativity and artistic genius by posing infinite parametric possibilities as inviting alternatives to traditional notions of stability, originality, and evolution. Through ephemeral processes of printing, milling, and projecting, the exhibition “Liquid Illumination” destabilizes the solid foundations of fashion and architecture. The installation is neither worn nor built in the conventional sense, but—like the sensual art forms of fashion and architecture—it is still radically embodied through the logics and techniques of design. Appearances are everything. Surface pattern and color are no longer understood as minor afterthoughts or vapid carriers of dubious content. Here, they become essential but ever-changing aspects of precisely fabricated images. Fourteen silk “colorways” (a term from the fashion industry) are framed selections from ongoing experiments with intricate pattern and complex color configurations. Whether these images are printed on fabric, milled in foam, or illuminated through projection, they explore and celebrate the untapped potentials of the surficial and superficial. Some components of individual prints appear to float in front of others through stereoscopic superimpositions; some figures appear to melt into others due to subtle changes in hue without corresponding changes in value; and some layers appear to vibrate via moiré effects that emerge from unexpected pattern and color combinations. The liturgical atmosphere of Liquid Illumination is intended to acknowledge that, like the simultaneously sacred and superficial qualities of rose windows and illuminated manuscripts, artistic and religious ideologies are also always malleable. The intellectual provocation of this paper pushes the boundaries of current thinking concerning viable applications for fashion print designs and architectural images—challenging traditional boundaries between fine art and design. The opportunistic installation of digital printing, CNC milling, and video projection mapping in a gallery that is normally reserved for fine art exhibitions raises important questions about cultural/commercial display, mass customization, digital reproduction, and the increasing prominence of surface effects (color, texture, pattern, reflection, saturation, etc.) across a range of artistic practices and design disciplines.

Keywords: fashion, print design, architecture, projection mapping, image, fabrication

Procedia PDF Downloads 88

Authors: Monica Racha, Siva Chandrasekaran, Alex Stojcevski

Abstract:

The engineering programs focus on promoting students' personal and professional development by ensuring that students acquire technical and professional competencies during four-year studies. The traditional engineering laboratory provides an opportunity for students to "practice by doing," and laboratory facilities aid them in obtaining insight and understanding of their discipline. Due to rapid technological advancements and the current COVID-19 outbreak, the traditional labs were transforming into virtual learning environments. Aim: To better understand the limitations of the physical laboratory, this research study aims to use a Machine Learning (ML) algorithm that interfaces with the Augmented Reality HoloLens and predicts the image behavior to classify and detect the electronic components. The automated electronic components error classification and detection automatically detect and classify the position of all components on a breadboard by using the ML algorithm. This research will assist first-year undergraduate engineering students in conducting laboratory practices without any supervision. With the help of HoloLens, and ML algorithm, students will reduce component placement error on a breadboard and increase the efficiency of simple laboratory practices virtually. Method: The images of breadboards, resistors, capacitors, transistors, and other electrical components will be collected using HoloLens 2 and stored in a database. The collected image dataset will then be used for training a machine learning model. The raw images will be cleaned, processed, and labeled to facilitate further analysis of components error classification and detection. For instance, when students conduct laboratory experiments, the HoloLens captures images of students placing different components on a breadboard. The images are forwarded to the server for detection in the background. A hybrid Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) algorithm will be used to train the dataset for object recognition and classification. The convolution layer extracts image features, which are then classified using Support Vector Machine (SVM). By adequately labeling the training data and classifying, the model will predict, categorize, and assess students in placing components correctly. As a result, the data acquired through HoloLens includes images of students assembling electronic components. It constantly checks to see if students appropriately position components in the breadboard and connect the components to function. When students misplace any components, the HoloLens predicts the error before the user places the components in the incorrect proportion and fosters students to correct their mistakes. This hybrid Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) algorithm automating electronic component error classification and detection approach eliminates component connection problems and minimizes the risk of component damage. Conclusion: These augmented reality smart glasses powered by machine learning provide a wide range of benefits to supervisors, professionals, and students. It helps customize the learning experience, which is particularly beneficial in large classes with limited time. It determines the accuracy with which machine learning algorithms can forecast whether students are making the correct decisions and completing their laboratory tasks.

Keywords: augmented reality, machine learning, object recognition, virtual laboratories

Procedia PDF Downloads 134

Authors: Tsega Asresa, Getahun Tigistu, Melaku Bayih

Abstract:

Computer vision is a subfield of artificial intelligence that allows computers and systems to extract meaning from digital images and video. It is used in a wide range of fields of study, including self-driving cars, video surveillance, medical diagnosis, manufacturing, law, agriculture, quality control, health care, facial recognition, and military applications. Aromatic medicinal plants are botanical raw materials used in cosmetics, medicines, health foods, essential oils, decoration, cleaning, and other natural health products for therapeutic and Aromatic culinary purposes. These plants and their products not only serve as a valuable source of income for farmers and entrepreneurs but also going to export for valuable foreign currency exchange. In Ethiopia, there is a lack of technologies for the classification and identification of Aromatic medicinal plant parts and disease type cured by aromatic medicinal plants. Farmers, industry personnel, academicians, and pharmacists find it difficult to identify plant parts and disease types cured by plants before ingredient extraction in the laboratory. Manual plant identification is a time-consuming, labor-intensive, and lengthy process. To alleviate these challenges, few studies have been conducted in the area to address these issues. One way to overcome these problems is to develop a deep learning model for efficient identification of Aromatic medicinal plant parts with their corresponding disease type. The objective of the proposed study is to identify the aromatic medicinal plant parts and their disease type classification using computer vision technology. Therefore, this research initiated a model for the classification of aromatic medicinal plant parts and their disease type by exploring computer vision technology. Morphological characteristics are still the most important tools for the identification of plants. Leaves are the most widely used parts of plants besides roots, flowers, fruits, and latex. For this study, the researcher used RGB leaf images with a size of 128x128 x3. In this study, the researchers trained five cutting-edge models: convolutional neural network, Inception V3, Residual Neural Network, Mobile Network, and Visual Geometry Group. Those models were chosen after a comprehensive review of the best-performing models. The 80/20 percentage split is used to evaluate the model, and classification metrics are used to compare models. The pre-trained Inception V3 model outperforms well, with training and validation accuracy of 99.8% and 98.7%, respectively.

Keywords: aromatic medicinal plant, computer vision, convolutional neural network, deep learning, plant classification, residual neural network

Procedia PDF Downloads 186

Authors: David Jurado, Carlos Ávila

Abstract:

Detection of early signs of breast cancer development is crucial to quickly diagnose the disease and to define adequate treatment to increase the survival probability of the patient. Computer Aided Detection systems (CADs), along with modern data techniques such as Machine Learning (ML) and Neural Networks (NN), have shown an overall improvement in digital mammography cancer diagnosis, reducing the false positive and false negative rates becoming important tools for the diagnostic evaluations performed by specialized radiologists. However, ML and NN-based algorithms rely on datasets that might bring issues to the segmentation tasks. In the present work, an automatic segmentation and detection algorithm is described. This algorithm uses image processing techniques along with the Hough transform to automatically identify microcalcifications that are highly correlated with breast cancer development in the early stages. Along with image processing, automatic segmentation of high-contrast objects is done using edge extraction and circle Hough transform. This provides the geometrical features needed for an automatic mask design which extracts statistical features of the regions of interest. The results shown in this study prove the potential of this tool for further diagnostics and classification of mammographic images due to the low sensitivity to noisy images and low contrast mammographies.

Keywords: breast cancer, segmentation, X-ray imaging, hough transform, image analysis

Procedia PDF Downloads 83

Authors: Pragya Paneru, Prativa Paneru

Abstract:

This paper explores the attitude of high school Nepalese students toward scientists using a famous method named as Draw-A-Scientist-Test (DAST). A total of 145 students from Grade 11 and Grade 12 took part in this research and drew images of scientists. The findings indicated gender imbalance with male dominance in the images of scientists. The result also showed some usual stereotypes relating to hair, equipment, objects, use of eyeglasses, and lab coat in the drawings of scientists. Moreover, the influence of some mainstream western male scientists was widely seen in the drawings implying the exposure of limited male scientists to the students. In contrast to this, no real-life female scientists were mentioned by the participants demonstrating limited exposure of female scientists contributing to the gendered attitude toward the scientists. However, some of the findings also challenged the previous findings and depicted scientists with local features, positive expression, and working outdoors. Moreover, participants’ awareness that scientists could be anyone with an inquisitive mind was indicated by the variations in the characters in their drawings. The drawings indicated that scientists could be someone like a mother, themselves, a fashion icon, Buddha, or a crazy-looking person. This study recommends the inclusion of participants’ interviews, and exploration of their textbooks’ depiction of scientists to uncover additional details regarding their understanding of scientists. Also, a critical discussion of the stereotypical attitudes about scientists in class could help challenge the stereotypical assumptions of scientists.

Keywords: scientists, drawings, stereotypes, gender, high school students

Procedia PDF Downloads 80

Authors: Roza Dzierzak, Waldemar Wojcik, Piotr Kacejko

Abstract:

Analysis of the texture of medical images consists of determining the parameters and characteristics of the examined tissue. The main goal is to assign the analyzed area to one of two basic groups: as a healthy tissue or a tissue with pathological changes. The CT images of the thoracic lumbar spine from 15 healthy patients and 15 with confirmed osteoporosis were used for the analysis. As a result, 120 samples with dimensions of 50x50 pixels were obtained. The set of features has been obtained based on the histogram, gradient, run-length matrix, co-occurrence matrix, autoregressive model, and Haar wavelet. As a result of the image analysis, 290 descriptors of textural features were obtained. The dimension of the space of features was reduced by the use of three selection methods: Fisher coefficient (FC), mutual information (MI), minimization of the classification error probability and average correlation coefficients between the chosen features minimization of classification error probability (POE) and average correlation coefficients (ACC). Each of them returned ten features occupying the initial place in the ranking devised according to its own coefficient. As a result of the Fisher coefficient and mutual information selections, the same features arranged in a different order were obtained. In both rankings, the 50% percentile (Perc.50%) was found in the first place. The next selected features come from the co-occurrence matrix. The sets of features selected in the selection process were evaluated using six classification tree methods. These were: decision stump (DS), Hoeffding tree (HT), logistic model trees (LMT), random forest (RF), random tree (RT) and reduced error pruning tree (REPT). In order to assess the accuracy of classifiers, the following parameters were used: overall classification accuracy (ACC), true positive rate (TPR, classification sensitivity), true negative rate (TNR, classification specificity), positive predictive value (PPV) and negative predictive value (NPV). Taking into account the classification results, it should be stated that the best results were obtained for the Hoeffding tree and logistic model trees classifiers, using the set of features selected by the POE + ACC method. In the case of the Hoeffding tree classifier, the highest values of three parameters were obtained: ACC = 90%, TPR = 93.3% and PPV = 93.3%. Additionally, the values of the other two parameters, i.e., TNR = 86.7% and NPV = 86.6% were close to the maximum values obtained for the LMT classifier. In the case of logistic model trees classifier, the same ACC value was obtained ACC=90% and the highest values for TNR=88.3% and NPV= 88.3%. The values of the other two parameters remained at a level close to the highest TPR = 91.7% and PPV = 91.6%. The results obtained in the experiment show that the use of classification trees is an effective method of classification of texture features. This allows identifying the conditions of the spongy tissue for healthy cases and those with the porosis.

Keywords: classification, feature selection, texture analysis, tree algorithms

Procedia PDF Downloads 178

Authors: Frazer Kirk, Rohen Skiba, Pankaj Saxena

Abstract:

The natural history of the QAV is poorly understood due to the exceeding rarity of the condition. Incidence rates vary between 0.00028-1%. Classically patients present with Aortic Regurgitation (AR) between 40-60 years of age experiencing palpitations, chest pain, or heart failure. (1, 2) Echocardiography is the mainstay of diagnosis for this condition; however, given the rarity of this condition, it can easily be overlooked, as demonstrated here. The case report that follows serves as a reminder of the condition to reduce the innate cognitive bias to overlook the diagnosis due to the availability heuristic. Intraoperative photography, echocardiographic and magnetic resonance imaging from this case for reference to demonstrate that while the diagnosis of Aortic regurgitation was recognized early, the valve morphology was underappreciated.

Keywords: quadricuspid aortic valve, cardiac surgery, echocardiography, congenital

Procedia PDF Downloads 162

Authors: Haykal. Kay-Anne

Abstract:

The health and well-being of students is a priority for the Faculty of Medicine at the University of Ottawa. The demands of medical studies are extreme, and many studies confirm that the prevalence of psychological distress is very high among medical students and that it is higher than that of the general population of the same age. The main goal is to identify risk factors for mental health among medical students at the University of Ottawa. The secondary objectives are to determine the variation of these risk factors according to demographic variables, as well as to determine if there is a change in the mental health of students during the 1st and 3rd years of their study. Medical students have a mandatory first and third-year wellness check meeting. This assessment includes a questionnaire on demographic information, mental health, and risk factors such as physical health, sleep, social support, financial stress, education and career, stress and drug use and/or alcohol. Student responses were converted to numerical values and analyzed statistically. The results show that 61% of the variation in the mean of the mental health score is explained by the following risk factors (R2 = 0.61, F (9.396) = 67.197, p < 0.01): lack of sleep and fatigue (β = 0.281, p < 0.001), lack of social support (β = 0.217, p <0.001), poor study or career development (β = 0.195, p < 0.001) and an increase stress and drug and alcohol use (β = -0.239, p < 0.001). No demographic variable has a significant effect on the presence of risk factors. In addition, fixed-effects regression demonstrated significantly lower mental health (p < 0.1) among first-year students (M = 0.587, SD = 0.072) than among third-year students (M = 0.719, SD = 0.071). This preliminary study indicates the need to continue data collection and analysis to increase the significance of the study results. As risk factors are present at the beginning of medical studies, it is important to offer resources to students very early in their medical studies and to have close monitoring and supervision.

Keywords: assessment of mental health, medical students, risk factors for mental health, wellness assessment

Procedia PDF Downloads 123

Authors: Yili Chen, Xiaokun Liang, Zhicheng Zhang, Yaoqin Xie

Abstract:

In an effort to restore visual perception in retinal diseases, an electronic retinal prosthesis with thousands of electrodes has been developed. The image processing strategies of retinal prosthesis system converts the original images from the camera to the stimulus pattern which can be interpreted by the brain. Practically, the original images are with more high resolution (256x256) than that of the stimulus pattern (such as 25x25), which causes a technical image processing challenge to do large-capacity image information reduction. In this paper, we focus on developing an efficient image processing stimulus pattern extraction algorithm by using a single cue saliency map for extracting salient objects in the image with an optimal trimming threshold. Experimental results showed that the proposed stimulus pattern extraction algorithm performs quite well for different scenes in terms of the stimulus pattern. In the algorithm performance experiment, our proposed SCSPE algorithm have almost five times of the score compared with Boyle’s algorithm. Through experiment s we suggested that when there are salient objects in the scene (such as the blind meet people or talking with people), the trimming threshold should be set around 0.4max, in other situations, the trimming threshold values can be set between 0.2max-0.4max to give the satisfied stimulus pattern.

Keywords: retinal prosthesis, image processing, region of interest, saliency map, trimming threshold selection

Procedia PDF Downloads 246

Authors: Marrone Silverio Melo Dantas Pedro Henrique Dreyer, Gabriel Fonseca Reis de Souza, Daniel Bezerra, Ricardo Souza, Silvia Lins, Judith Kelner, Djamel Fawzi Hadj Sadok

Abstract:

The creation of a dataset is time-consuming and often discourages researchers from pursuing their goals. To overcome this problem, we present and discuss two solutions adopted for the automation of this process. Both optimize valuable user time and resources and support video object segmentation with object tracking and 3D projection. In our scenario, we acquire images from a moving robotic arm and, for each approach, generate distinct annotated datasets. We evaluated the precision of the annotations by comparing these with a manually annotated dataset, as well as the efficiency in the context of detection and classification problems. For detection support, we used YOLO and obtained for the projection dataset an F1-Score, accuracy, and mAP values of 0.846, 0.924, and 0.875, respectively. Concerning the tracking dataset, we achieved an F1-Score of 0.861, an accuracy of 0.932, whereas mAP reached 0.894. In order to evaluate the quality of the annotated images used for classification problems, we employed deep learning architectures. We adopted metrics accuracy and F1-Score, for VGG, DenseNet, MobileNet, Inception, and ResNet. The VGG architecture outperformed the others for both projection and tracking datasets. It reached an accuracy and F1-score of 0.997 and 0.993, respectively. Similarly, for the tracking dataset, it achieved an accuracy of 0.991 and an F1-Score of 0.981.

Keywords: RJ45, automatic annotation, object tracking, 3D projection

Procedia PDF Downloads 167

Authors: Kawthar F. Albasri, Abdullah M. AlHudaithi, Dana B. AlTurairi, Abdullaziz S. AlQuraini, Adoub Y. AlDerazi, Reem A. Hubail, Haitham A. Jahrami

Abstract:

Several studies have found that medical students have a significant prevalence of anxiety. The purpose of this review paper is to carefully evaluate the current research on anxiety among medical students in the MENA region and, as a result, estimate the prevalence of these disturbances. Multiple databases, including the CINAHL (Cumulative Index to Nursing and Allied Health Literature), Cochrane Library, Embase, MEDLINE (Medical Literature Analysis and Retrieval System Online), PubMed, PsycINFO (Psychological Information Database), Scopus, Web of Science, UpToDate, ClinicalTrials.gov, WHO Global Health Library, EbscoHost, ProQuest, JAMA Network, and ScienceDirect, were searched. The retrieved article reference lists were rigorously searched and rated for quality. A random effects meta-analysis was performed to compute estimates. The current meta-analysis revealed an alarming estimated pooled prevalence of anxiety (K = 46, N = 27023) of 52.5% [95%CI: 43.3%–61.6%]. A total of 62.0% [95% CI 42.9%; 78.0%] of the students (K = 18, N = 16466) suffered from anxiety during the COVID-19 pandemic, while 52.5% [95% CI 43.3%; 61.6%] had anxiety before COVID-19. Based on the GAD-7 measure, a total of 55.7% [95%CI 30.5%; 78.3%] of the students (K = 10, N = 5830) had anxiety, and a total of 54.7% of the students (K = 18, N = 12154) [95%CI 42.8%; 66.0%] had anxiety using the DASS-21 or 42 measure. Anxiety is a common issue among medical students, making it a genuine problem. Further research should be conducted post-COVD 19, with a focus on anxiety prevention and intervention initiatives for medical students.

Keywords: anxiety, medical students, MENA, meta-analysis, prevalence

Procedia PDF Downloads 73

Authors: Tanakrit Doltanakarn

Abstract:

Introduction: These days, cannabis is being accepted in many countries due to the fact that cannabis could be use in medical. The medical cannabis is used to treat and reduce the pain many diseases. For example, neuropathic pain, Parkinson, autism disorders, cancer pain reduce the adverse effect of chemotherapy, diabetes, and migraine. Active ingredients in cannabis that modulate patients' perceptions of their conditions include Δ9‐tetrahydrocannabinol (THC), cannabidiol (CBD), flavonoids, and terpenes. However, there is an adverse effect of cannabis, cardiovascular effects, psychosis, schizophrenia, mood disorder, and cognitive alternation. These effects are from the THC and CBD ingredients in the cannabis. The metabolize processes of delta-9 THC to 11-OH-delta 9 -THC (inactive form), THC were cause of adverse effects. Interestingly, the distributions of CYP2C9 gene (CYP2C9*2 and CYP2C9*3, poor metabolizer) that might affect incidences of adverse effects in patients who treated with medical cannabis. Objective: The aim of this study we want to investigate the association between genetic polymorphism of CYP2C9 frequency and Thai patients who treated with medical cannabis. Materials and Methods:We recruited sixty-five unrelated Thai patients from the College of Pharmacy, Rangsit University. DNA were extracted using Genomic DNA Mini Kit. Genotyping of CYP2C9*2 (430C>T, rs1799853) and CYP2C9*3 (1075A>C, rs1057910) were genotyped by the TaqMan Real-time PCR assay. Results: Among these 31 medicals cannabis-induced ADRs patients, they were diagnosed with 22 (33.85%) tachycardia and 3 (4.62%) arrhythmia. There were 34 (52.31%) medical cannabis-tolerant controls who were included in this study.40 (61.53%) Thai patients were female, and 25 (38.46%) were male, with median age of 57 (range 27 – 87) years. In this study, we found none of the medical cannabis-induced ADRs carried CYP2C9*2 variant along with medical cannabis-tolerant control group. CYP2C9*3 variant (intermediate metabolizer, IM) was found just only one of thirty-one (3.23%) in the medical cannabis-induced ADRs and two of thirty-fourth (5.88%) in the tolerant controls. Conclusions: Thus, the distribution of CYP2C9 alleles offer a comprehensive view of pharmacogenomics marker in Thai population that could be used as a reference for worldwide to investigate the pharmacogenomics application.

Keywords: medical cannabis, adverse effect, CYP2C9, thai patients

Procedia PDF Downloads 101

Authors: Kuo-Kai Lin, Po-Lun Chang

Abstract:

Background: The Emergency Care Research Institute released a list for the top 10 medical technology hazards in 2017, with the following hazard topping the list: ‘infusion errors can be deadly if simple safety steps are overlooked.’ In addition, hospitals use various assessment items to evaluate the safety of their medical equipment, confirming the importance of medical equipment safety. In recent years, the topic of patient safety has garnered increasing attention. Accordingly, various agencies have established patient safety-related committees to coordinate, collect, and analyze information regarding abnormal events associated with medical practice. Activities to promote and improve employee training have been introduced to diminish the recurrence of medical malpractice. Objective: To allow nursing personnel to acquire the skills needed to operate common medical equipment and update and review such skills whenever necessary to elevate medical care quality and reduce patient injuries caused by medical equipment operation errors. Method: In this study, a quasi-experimental design was adopted and nurses from a regional teaching hospital were selected as the study sample. Online videos instructing the operation method of common medical equipment were made and quick response codes were designed for the nursing personnel to quickly access the videos when necessary. Senior nursing supervisors and equipment experts were invited to formulate a ‘Scale-based Questionnaire for Assessing Nursing Personnel’s Operational Knowledge of Common Medical Equipment’ to evaluate the nursing personnel’s literacy regarding the operation of the medical equipment. From March to October 2017, an employee training on medical equipment operation and a practice course (simulation course) were implemented, after which the effectiveness of the training and practice course were assessed. Results: Prior to and after the training and practice course, the 66 participating nurses scored 58 and 87 on ‘operational knowledge of common medical equipment,’ respectively (showing a significant statistical difference; t = -9.407, p < .001); 53.5 and 86.3 on ‘operational knowledge of 12-lead electrocardiography’ (z = -2.087, p < .01), respectively; 40 and 79.5 on ‘operational knowledge of cardiac defibrillators’ (z = -3.849, p < .001), respectively; 90 and 98 on ‘operational knowledge of Abbott pumps’ (z = -1.841, p = 0.066), respectively; and 8.7 and 13.7 on ‘perceived competence’ (showing a significant statistical difference; t = -2.77, p < .05). In the participating hospital, medical equipment operation errors were observed in both 2016 and 2017. However, since the implementation of the intervention, medical equipment operation errors have not yet been observed up to October 2017, which can be regarded as the secondary outcome of this study. Conclusion: In this study, innovative teaching strategies were adopted to effectively enhance the professional literacy and skills of nursing personnel in operating medical equipment. The training and practice course also elevated the nursing personnel’s related literacy and perceived competence of operating medical equipment. The nursing personnel was thus able to accurately operate the medical equipment and avoid operational errors that might jeopardize patient safety.

Keywords: medical equipment, digital teaching plan, simulation-based teaching plan, operational knowledge, patient safety

Procedia PDF Downloads 138

Authors: Sabah Mohammed, Jinan Fiaidhi, Darien Sawyer

Abstract:

Describing clinical cases according to a clinical charting standard that enforces interoperability and enables connected care services can save lives in the event of a medical emergency or provide efficient and effective interventions for the benefit of the patients through the integration of bedside and bench side clinical research. This article presented a multi-tenant extension to the problem-oriented medical record that we have prototyped previously upon using the GraphQL Application Programming Interface to represent the notion of a problem list. Our implemented extension enables physicians and patients to collaboratively describe the patient case via using multi chatbots to collaboratively describe the patient case using the SOAP charting standard. Our extension also connects the described SOAP patient case with the HL7 FHIR (Health Interoperability Resources) medical record for connecting the patient case to the bench data.

Keywords: problem-oriented medical record, graphQL, chatbots, SOAP

Procedia PDF Downloads 91

Authors: Mannan Mridha, Usama Gazay, Kosovare V. Aslani, Hugo Linder, Alice Ravizza, Carmelo de Maria

Abstract:

In most developing countries, although the vast majority of the people are living in the rural areas, the qualified medical doctors are not available there. Health care workers and paramedics, called village doctors, informal healthcare providers, are largely responsible for the rural medical care. Mishaps due to wrong diagnosis and inappropriate medication have been causing serious suffering that is preventable. While innovators have created many devices, the vast majority of these technologies do not find applications to address the needs and conditions in low-resource settings. The primary motive is to address the acute lack of affordable medical technologies for the poor people in low-resource settings. A low cost smart medical device that is portable, battery operated and can be used at any point of care has been developed to detect breathing rate, electrocardiogram (ECG) and arterial pulse rate to improve diagnosis and monitoring of patients and thus improve care and safety. This simple and easy to use smart medical device can be used, managed and maintained effectively and safely by any health worker with some training. In order to empower the health workers and village doctors, our device is being further developed to integrate with ICT tools like smart phones and connect to the medical experts wherever available, to manage the serious health problems.

Keywords: e-health for low resources settings, health awareness education, improve patient care and safety, smart and affordable medical device

Procedia PDF Downloads 193

Authors: Xiang Shijie, Zhou Dong, Tian Dan

Abstract:

This paper focuses on the application of real-time semantic segmentation technology in complex road condition recognition, aiming to address the critical issue of how to improve segmentation accuracy while ensuring real-time performance. Semantic segmentation technology has broad application prospects in fields such as autonomous vehicle navigation and remote sensing image recognition. However, current real-time semantic segmentation networks face significant technical challenges and optimization gaps in balancing speed and accuracy. To tackle this problem, this paper conducts an in-depth study and proposes an innovative Guided Image Reconstruction Module. By resampling high-resolution images into a set of low-resolution images, this module effectively reduces computational complexity, allowing the network to more efficiently extract features within limited resources, thereby improving the performance of real-time segmentation tasks. In addition, a dual-branch network structure is designed in this paper to fully leverage the advantages of different feature layers. A novel Hybrid Attention Mechanism is also introduced, which can dynamically capture multi-scale contextual information and effectively enhance the focus on important features, thus improving the segmentation accuracy of the network in complex road condition. Compared with traditional methods, the proposed model achieves a better balance between accuracy and real-time performance and demonstrates competitive results in road condition segmentation tasks, showcasing its superiority. Experimental results show that this method not only significantly improves segmentation accuracy while maintaining real-time performance, but also remains stable across diverse and complex road conditions, making it highly applicable in practical scenarios. By incorporating the Guided Image Reconstruction Module, dual-branch structure, and Hybrid Attention Mechanism, this paper presents a novel approach to real-time semantic segmentation tasks, which is expected to further advance the development of this field.

Keywords: hybrid attention mechanism, image reconstruction, real-time, road status recognition

Procedia PDF Downloads 23

Authors: Randy M. Fitratullah, Afriwardi, Nurhayati

Abstract:

Overweight-obesity caused by exercise. The objective of this research is to analyze the relation between exercise with the incidence of overweight-obesity of medical students of medical faculty of Andalas Univesity batch 2013. This is an analytical observational research with case-control method. This research conducted in FK Unand on September-October 2015. The population of this research is medical students batch 2013. 26 samples (13 samples were case, 13 samples were control) were taken by purposive sampling technique and analysed using statistical univariate and bivariate analysis. Exercise questionnaire was used as research instruments. Based on the interview with questionnaire, anaerobic exercise was majority in case group and aerobic exercise was majority in control group. The case and control group have a rare category in exercise. Less category was majority in exercise duration of case and enough category was majority in control group. Bivariate analysis is using chi-square test with cell combining to 2x2 table, obtained p-value=0.097 in sort of exercise, p-value=1,000 in the frequency of exercise, and p-value=0,112 in duration of exercise, which means statistically unsignificant. There is no relation between exercise with the incidence of overweight-obesity of medical students of FK Unand batch 2013. For medical students suffers overweight-obesity is suggested for increase the frequency of exercise.

Keywords: overweight-obesity, exercise, aerobic, anaerobic, frequency, duration

Procedia PDF Downloads 262

Authors: Mst Shapna Akter, Hossain Shahriar

Abstract:

Neural network approaches are machine learning methods used in many domains, such as healthcare and cyber security. Neural networks are mostly known for dealing with image datasets. While training with the images, several fundamental mathematical operations are carried out in the Neural Network. The operation includes a number of algebraic and mathematical functions, including derivative, convolution, and matrix inversion and transposition. Such operations require higher processing power than is typically needed for computer usage. Central Processing Unit (CPU) is not appropriate for a large image size of the dataset as it is built with serial processing. While Graphics Processing Unit (GPU) has parallel processing capabilities and, therefore, has higher speed. This paper uses advanced Neural Network techniques such as VGG16, Resnet50, Densenet, Inceptionv3, Xception, Mobilenet, XGBOOST-VGG16, and our proposed models to compare CPU and GPU resources. A system for classifying autism disease using face images of an autistic and non-autistic child was used to compare performance during testing. We used evaluation matrices such as Accuracy, F1 score, Precision, Recall, and Execution time. It has been observed that GPU runs faster than the CPU in all tests performed. Moreover, the performance of the Neural Network models in terms of accuracy increases on GPU compared to CPU.

Keywords: autism disease, neural network, CPU, GPU, transfer learning

Procedia PDF Downloads 118

Authors: Duygu Çimen, Nilay Bereli, Adil Denizli

Abstract:

In this study is to synthesis magnetic nanoparticles for purify protein C. For this aim, N-Methacryloyl-(L)-histidine methyl ester (MAH) containing 2-hydroxyethyl methacrylate (HEMA) based magnetic nanoparticles were synthesized by using micro-emulsion polymerization technique for templating protein C via metal chelation. The obtained nanoparticles were characterized with Fourier transform infrared spectroscopy (FTIR), transmission electron microscopy (TEM), zeta-size analysis and electron spin resonance (ESR) spectroscopy. After that, they were used for protein C purification from aqueous solution to evaluate/optimize the adsorption condition. Hereby, the effecting factors such as concentration, pH, ionic strength, temperature, and reusability were evaluated. As the last step, protein C was determined with sodium dodecyl sulfate-polyacrylamide gel electrophoresis.

Keywords: immobilized metal affinity chromatography (IMAC), magnetic nanoparticle, protein C, hydroxyethyl methacrylate (HEMA)

Procedia PDF Downloads 425

Authors: Niharika Keot, Manabendra Sarma

Abstract:

A thorough investigation of Ln3+ complexes with more than one inner-sphere water molecule is crucial for designing high relaxivity contrast agents (CAs) used in magnetic resonance imaging (MRI). This study accomplished a comparative stability analysis of two hexadentate (H3cbda and H3dpaa) and two heptadentate (H4peada and H3tpaa) ligands with Ln3+ ions. The higher stability of the hexadentate H3cbda and heptadentate H4peada ligands has been confirmed by the binding affinity and Gibbs free energy analysis in aqueous solution. In addition, energy decomposition analysis (EDA) reveals the higher binding affinity of the peada4− ligand than the cbda3− ligand towards Ln3+ ions due to the higher charge density of the peada4− ligand. Moreover, a mechanistic overview of water exchange kinetics has been carried out based on the strength of the metal–water bond. The strength of the metal–water bond follows the trend Gd–O47 (w) > Gd–O39 (w) > Gd–O36 (w) in the case of the tris-aquated [Gd(cbda)(H2O)3] and Gd–O43 (w) > Gd–O40 (w) for the bis-aquated [Gd(peada)(H2O)2]− complex, which was confirmed by bond length, electron density (ρ), and electron localization function (ELF) at the corresponding bond critical points. Our analysis also predicts that the activation energy barrier decreases with the decrease in bond strength; hence kex increases. The 17O and 1H hyperfine coupling constant values of all the coordinated water molecules were different, calculated by using the second-order Douglas–Kroll–Hess (DKH2) approach. Furthermore, the ionic nature of the bonding in the metal–ligand (M–L) bond was confirmed by the Quantum Theory of Atoms-In-Molecules (QTAIM) and ELF along with energy decomposition analysis (EDA). We hope that the results can be used as a basis for the design of highly efficient Gd(III)-based high relaxivity MRI contrast agents for medical applications.

Keywords: MRI contrast agents, lanthanide chemistry, thermodynamic stability, water exchange kinetics

Procedia PDF Downloads 83

Authors: Tamara Storozhenko

Abstract:

In the paper, we want to highlight the influence of the advertising field on gender and vice versa. We will show what it was like before and the way it has changed until nowadays. We will also analyze when and how advertisements are used to create gender stereotypes and at which moment gender became a shaping advertisement. In this paper, we work not only with pure advertisements (e.g., videos and printed materials) but also with films that contain ads. Special attention is placed on the separation of goods for the ‘male ones’ and ‘female ones’, specifically if they can be used independently of gender and sex (food items and some kinds of personal supplies). Also, in this paper, we represent the history of several advertising campaigns, including the following reaction of the society that demonstrated that some of the gender stereotypes were finding resonance while some of them were not heard. Moreover, advertisements could be used as a tool for creating new ones or developing stereotypes that had already existed, and it wasn’t always successful. In the final part of the paper, we would like to analyze the current situation in this area and show how the change of understanding gender made advertisement change.

Keywords: advertisement, gender studies, psycholinguistics, sociolinguistics

Procedia PDF Downloads 155

Authors: Su Chul Han, Seungwoo Park

Abstract:

As the increasing incidence of cancer, the technology and modality of radiotherapy have advanced and the importance of preclinical model is increasing in the cancer research. Furthermore, the small animal dosimetry is an essential part of the evaluation of the relationship between the absorbed dose in preclinical small animal and biological effect in preclinical study. In this study, we carried out realistic modeling of the preclinical small animal phantom possible to verify irradiated dose using commercial software. The small animal phantom was modeling from 4D Digital Mouse whole body phantom. To manipulate Moby phantom in commercial software (Mimics, Materialise, Leuven, Belgium), we converted Moby phantom to DICOM image file of CT by Matlab and two- dimensional of CT images were converted to the three-dimensional image and it is possible to segment and crop CT image in Sagittal, Coronal and axial view). The CT images of small animals were modeling following process. Based on the profile line value, the thresholding was carried out to make a mask that was connection of all the regions of the equal threshold range. Using thresholding method, we segmented into three part (bone, body (tissue). lung), to separate neighboring pixels between lung and body (tissue), we used region growing function of Mimics software. We acquired 3D object by 3D calculation in the segmented images. The generated 3D object was smoothing by remeshing operation and smoothing operation factor was 0.4, iteration value was 5. The edge mode was selected to perform triangle reduction. The parameters were that tolerance (0.1mm), edge angle (15 degrees) and the number of iteration (5). The image processing 3D object file was converted to an STL file to output with 3D printer. We modified 3D small animal file using 3- Matic research (Materialise, Leuven, Belgium) to make space for radiation dosimetry chips. We acquired 3D object of realistic small animal phantom. The width of small animal phantom was 2.631 cm, thickness was 2.361 cm, and length was 10.817. Mimics software supported efficiency about 3D object generation and usability of conversion to STL file for user. The development of small preclinical animal phantom would increase reliability of verification of absorbed dose in small animal for preclinical study.

Keywords: mimics, preclinical small animal, segmentation, 3D printer

Procedia PDF Downloads 366

Authors: Rajish Shil, Amal Alduhoori, Vipin Thomachan, Jamal Teir, Radhakrishnan Renganathan

Abstract:

Background: Antiphospholipid antibody syndrome (APS) has a broad spectrum of thrombotic and non-thrombotic clinical manifestations. We present a case of APS presenting with seizure, stroke, and atrial mass. Case Description: A 38-year-old male presented with headache of 10 days duration and tonic-clonic seizure. The neurological examination was normal. Magnetic resonance imaging of brain showed small acute right cerebellar infarct. Magnetic resonance angiography of brain and neck showed a focal narrowing in the origin of the internal carotid artery bilaterally. Electroencephalogram was normal. He was started on aspirin, atorvastatin, and carbamazepine. Transthoracic and trans-esophageal echocardiography showed a pedunculated and lobular atrial mass, measuring 1 X 1.5 cm, which was freely mobile across mitral valve opening across the left ventricular inflow. Autoimmune screening showed positive Antiphospholipid antibodies in high titer (Cardiolipin IgG > 120 units/ml, B2 glycoprotein IgG 90 units/mL). Anti-nuclear antibody was negative. Erythrocyte sedimentation rate and C-reactive protein levels were normal. Platelet count was low (111 x 109/L). The patient underwent successful surgical removal of the mass, which looked like a thrombotic clot, and Histopathological analysis confirmed it as a fibrinous clot, with no evidence of tumor cells. The patient was started on full anticoagulation treatment and was followed up regularly in the clinic, where our patient did not have any further complications from the disease. Discussion: Our patient was diagnosed to have APS based on the features of high positive anticardiolipin antibody IgG and B2 glycoprotein IgG levels, Stroke, thrombocytopenia, and abnormal echo findings. Thrombotic vegetation can mimic an atrial myxoma on echo. Conclusion: APS can present with neurological and cardiac manifestations, and therefore a high index of suspicion is necessary for a diagnosis of the disease as it can affect both short and long term treatment plans and prognosis. Therefore, in patients presenting with neurological symptoms like seizures, weakness and radiological diagnosis of stroke in a young patient, where atrial masses could be thought to be the cause of stroke, they should be screened for any concomitant findings of thrombocytopenia and/or activated partial thromboplastin time prolongation, which should raise the suspicion of vasculitis, specifically APS to be the primary cause of the clinical presentation.

Keywords: antiphospholipid syndrome, seizures, atrial mass, stroke

Procedia PDF Downloads 113

Authors: Grace Omowunmi Soyebi

Abstract:

A computerized medical record system is a collection of medical information about a person that is stored on a computer. One principal problem of most hospitals in rural areas is using the file management system for keeping records. A lot of time is wasted when a patient visits the hospital, probably in an emergency, and the nurse or attendant has to search through voluminous files before the patient's file can be retrieved, this may cause an unexpected to happen to the patient. This Data Mining application is to be designed using a Structured System Analysis and design method which will help in a well-articulated analysis of the existing file management system, feasibility study, and proper documentation of the Design and Implementation of a Computerized medical record system. This Computerized system will replace the file management system and help to quickly retrieve a patient's record with increased data security, access clinical records for decision-making, and reduce the time range at which a patient gets attended to.

Keywords: programming, computing, data, innovation

Procedia PDF Downloads 119