Fully Automatic Knee Bone Detection and Segmentation on Three-Dimensional MRI

In the medical sector, three-dimensional (3D) images are commonly used like computed tomography (CT) and magnetic resonance imaging (MRI). The 3D MRI is a non-invasive method of studying the soft-tissue structures in a knee joint for osteoarthritis studies. It can greatly improve the accuracy of segmenting structures such as cartilage, bone marrow lesion, and meniscus by identifying the bone structure first. U-net is a convolutional neural network that was originally designed to segment the biological images with limited training data. The input of the original U-net is a single 2D image and the output is a binary 2D image. In this study, we modified the U-net model to identify the knee bone structures using 3D MRI, which is a sequence of 2D slices. A fully automatic model has been proposed to detect and segment knee bones. The proposed model was trained, tested, and validated using 99 knee MRI cases where each case consists of 160 2D slices for a single knee scan. To evaluate the model’s performance, the similarity, dice coefficient (DICE), and area error metrics were calculated. Separate models were trained using different knee bone components including tibia, femur, patella, as well as a combined model for segmenting all the knee bones. Using the whole MRI sequence (160 slices), the method was able to detect the beginning and ending bone slices first, and then segment the bone structures for all the slices in between. On the testing set, the detection model accomplished 98.79% accuracy and the segmentation model achieved DICE 96.94% and similarity 93.98%. The proposed method outperforms several state-of-the-art methods, i.e., it outperforms U-net by 3.68%, SegNet by 14.45%, and FCN-8 by 2.34%, in terms of DICE score using the same dataset.

Advantages and Functions of Clinical and Decision Support Systems

Clinical Decision Support Systems (CDSSs) signify the framework shift in the medical sector in the modern age. CDSSs are utilized in augmenting healthcare facilities in the process of making complex clinical decisions. Since the first application of CDSSs in the 80s, the framework has witnessed significant transformation. The frameworks are now administered through electronic healthcare records with complex capacities. Irrespective of these complex advancements, there are existing questions concerning the impacts of CDSSs on service providers, healthcare costs, and patients’ records. There are many published texts concerning the success stories of CDSSs, but significant setbacks have proved that CDSSs are not without any potential risks. In this research, we provide critical analysis on the application of CDSSs in clinical setting, integrating various forms, present application cases with proven effectiveness, potential harms and common pitfalls. We therefore conclude with evidence-centered recommendation for mitigating the issues of CDSSs maintainability, evaluation, implementation and designing.

THE IMPACT OF JOB ENGAGEMENT ON EMPLOYEE PERFORMANCE WITHIN THE MEDICAL SECTOR IN ZIMBABWE

The issue of job engagement has been central around the performance of employees as evidenced by the negotiations which have been aimed to serve as an impetus vehicle to seek attention for engagement. The process of engaging employees is vital for any organisation to succeed but it appears employees within the Zimbabwean medical sector feel neglected. The issue of job engagement has led to a standoff within the health sector. The study adopted the positivism research philosophy and the case study research design. The sample size was 140 respondents drawn from a population of 180 respondents and a structured questionnaire was adopted as the main research instrument. Findings revealed there is a positive relationship between Job characteristics and job engagement. Findings revealed also that there is a positive relationship between rewards & recognition and job engagement. Recommendations are that the medical sector should as a matter of urgency review its rewards systems to all of its employees to enhance job engagement and organisational performance.

Is AI augmenting or substituting humans? An eye-tracking study of visual attention toward health application

In this paper, the authors focus on Artificial Intelligence as a tangible technology that is designed to sense, comprehend, act, and learn. There are two manifestations of AI in the medical service: an algorithm that analyzes and interprets the test result and a virtual assistant that communicates the result to the patient. The aim of this paper is to consider how AI can substitute a doctor in measuring human health and how the interaction with virtual assistant impacts one’s visual attention processes. Theoretically, the article refers to the following research strands: Human-Computer Interaction, technology in services, implementation of AI in the medical sector, and behavioral economy. By conducting an eye-tracking experimental study, it is demonstrated that the perception of medical diagnosis does not differ across experimental groups (human vs. AI). However, it is observed that participants exposed to AI-based assistant focused more on button allowing to contact a real doctor.

Denoising of Images Using Deep Convolutional Autoencoders for Brain Tumor Classification

In the acquisition of images of the human body, medical imaging devices are crucial. The Magnetic Resonance Imaging (MRI) system detects tissue anomalies and tumours in the body of people. During the forming process, the MRI images are degraded by different kind of noises. It is difficult to remove certain noises, accompanied by the segmentation of images in order to classify anomalies. The most commonly explored areas of this period are automatic tumour detection systems using Magnetic Resonance Imaging. In the medical sector, timely and exact identification of frequencies is a problem. Automated systems are efficient that reduce human errors when tumour is detected. In recent years, many approaches have been proposed to do this, but there are still several drawbacks and a wide range of improvements on these methodologies are still needed. The image processing mechanism is widely used to improve early detection and treatment stages in the field of medical sciences. Sometimes the doctor can misdiagnose the image of MRI because of noise levels. To date, Deep Convolution Neural Networks (DCNN) have demonstrated excellent classification and segmentation efficiency. This paper proposes a technique for the image denoising using DCNN based Auto Encoders (DCNNAE) for achieving better accuracy rates in brain tumour prediction. In this paper we propose a deep convolution denoising auto encoder to remove noise from images and over fit the model problem by developing a deep convolution neural network for brain MRI image tumour prediction. The proposed model is compared with the existing methods and the results exhibits that the proposed model performance levels are better than the existing ones.

Predicting Retinal Diseases using Efficient Image Processing and Convolutional Neural Network (CNN)

Neural networks in image processing are becoming a more crucial and integral part of machine learning as computational technology and hardware systems are advanced. Deep learning is also getting attention from the medical sector as it is a prominent process for classifying diseases. There is a lot of research to predict retinal diseases using deep learning algorithms like Convolutional Neural Network (CNN). Still, there are not many researches for predicting diseases like CNV which stands for choroidal neovascularization, DME, which stands for Diabetic Macular Edema; and DRUSEN. In our research paper, the CNN (Convolutional Neural Networks) algorithm labeled the dataset of OCT retinal images into four types: CNV, DME, DRUSEN, and Natural Retina. We have also done several preprocessing on the images before passing these to the neural network. We have implemented different models for our algorithm where individual models have different hidden layers. At the end of our following research, we have found that our algorithm CNN generates 93% accuracy.

About the need to introduce a presumption of consent for organs transplantation and other human anatomical materials

The purpose of the article is to discuss the need to introduce a presumption of consent for the transplantation of organs and other human anatomical materials in Ukraine. Therefore, the object of the study is the presumption of consent for organ transplantation. The authors of the article have used methods of deduction, analysis and synthesis, comparative, and legal methods. The need to make amendments to the legislation of Ukraine regarding the introduction of the presumption of consent for the transplantation of organs and other human anatomical materials from a person and the feasibility of the practical implementation of these changes, namely, mean a major improvement and elimination of problems in the field of transplantation. It is concluded that at present one of the main problems governing the matter is the absence of presumption of consent for transplantation in Ukrainian legislation and, at the same time, the lack of significant funding of the medical sector, together with the low awareness of the rights of actors involved in organ transplant processes.

Are Americans less likely to reply to emails from Black people relative to White people?

In this article, we present the results from a large-scale field experiment designed to measure racial discrimination among the American public. We conducted an audit study on the general public—sending correspondence to 250,000 citizens randomly drawn from public voter registration lists. Our within-subjects experimental design tested the public’s responsiveness to electronically delivered requests to volunteer their time to help with completing a simple task—taking a survey. We randomized whether the request came from either an ostensibly Black or an ostensibly White sender. We provide evidence that in electronic interactions, on average, the public is less likely to respond to emails from people they believe to be Black (rather than White). Our results give us a snapshot of a subtle form of racial bias that is systemic in the United States. What we term everyday or “paper cut” discrimination is exhibited by all racial/ethnic subgroups—outside of Black people themselves—and is present in all geographic regions in the United States. We benchmark paper cut discrimination among the public to estimates of discrimination among various groups of social elites. We show that discrimination among the public occurs more frequently than discrimination observed among elected officials and discrimination in higher education and the medical sector but simultaneously, less frequently than discrimination in housing and employment contexts. Our results provide a window into the discrimination that Black people in the United States face in day-to-day interactions with their fellow citizens.

Unifying The Evaluation Criteria Of Many Objectives Optimization Using Fuzzy Delphi Method

Many objective optimizations (MaOO) algorithms that intends to solve problems with many objectives (MaOP) (i.e., the problem with more than three objectives) are widely used in various areas such as industrial manufacturing, transportation, sustainability, and even in the medical sector. Various approaches of MaOO algorithms are available and employed to handle different MaOP cases. In contrast, the performance of the MaOO algorithms assesses based on the balance between the convergence and diversity of the non-dominated solutions measured using different evaluation criteria of the quality performance indicators. Although many evaluation criteria are available, yet most of the evaluation and benchmarking of the MaOO with state-of-art algorithms perform using one or two performance indicators without clear evidence or justification of the efficiency of these indicators over others. Thus, unify a set of most suitable evaluation criteria of the MaOO is needed. This study proposed a distinct unifying model for the MaOO evaluation criteria using the fuzzy Delphi method. The study followed a systematic procedure to analyze 49 evaluation criteria, sub-criteria, and its performance indicators, a penal of 23 domain experts, participated in this study. Lastly, the most suitable criteria outcomes are formulated in the unifying model and evaluate by experts to verify the appropriateness and suitability of the model in assessing the MaOO algorithms fairly and effectively.

High-Performance UV-Vis Light Induces Radical Photopolymerization Using Novel 2-Aminobenzothiazole-Based Photosensitizers

The popularity of using the photopolymerization reactions in various areas of science and technique is constantly gaining importance. Light-induced photopolymerization is the basic process for the production of various polymeric materials. The key role in the polymerization reaction is the photoinitiator. The huge demand for radical and cationic initiators results from the dynamic development of the medical sector, and the optoelectronic, paints, coatings, varnishes and adhesives industries. For this reason, we dealt with the subject of designing new, highly-efficient radical photoinitiators. This paper describes novel photoinitiating systems operating in UV-Vis light for radical polymerization of acrylates. The proposed photoinitiators are composed of squaraine (SQ) as a light absorber and various diphenyliodonium (Iod) salts as co-initiators. The kinetic parameters of radical polymerization of trimethylolpropane triacrylate (TMPTA), such as the degree of double bonds conversion (C%), the rate of photopolymerization (Rp), as well as the photoinitiation index (Ip) were calculated. It was found that 2-aminobenzothiazole derivatives in the presence of iodonium salts effectively initiated the polymerization of TMPTA. The rates of polymerization were at about 2 × 10−2 s−1 and the degree of conversion of acrylate groups from 10% to 36% were observed. The values of the photoinitiating indexes for the most optimal initiator concentration, i.e., 5 × 10−3 M were in the range from 1 × 10−3 s−2 even to above 9 × 10−3 s−2. The photoinitiating efficiency of new radical initiators depends on the concentration and chemical structure of used photoinitiator. The role of squaraine-based photoinitiating systems as effective dyeing photoinitiators for radical polymerization is highlighted in this article.