Santiago Aso Lete, Carlos Cavero, Andriana Magdalinou, John Mantas, Lydia Montandon.Introduction Big data is massive amounts of information that can work wonders. In the healthcare industry, various sources for big data include hospital records, medical records of patients, results of medical examinations, and devices that are a part of internet of things. Aim The research aim of this study is to investigate the perceptions of the Health Professionals about the Big Data Technology in Healthcare. Methods An empirical study was conducted among 151 health professionals (doctors and nurses) to assess their knowledge about the Big Data Technology and their perceptions about using this technology in healthcare. A questionnaire was developed in order to measure the aforementioned dimensions. Results The survey's population was formed by 151 doctors and nurses who are working at private and public hospitals in Greece. The majority of the population have never heard about Big Data. As a result, most of them were not aware of the format of Big Data. Conclusion Based on the study findings, it can be assumed that the majority of the responders did not have knowledge about the Big Data Technology. It is also important that most of them had never been informed about Big Data. It can be assumed that the Healthcare Sector in Greece is not familiar with Big Data Technology yet. selleck products Finally,the current study reveals a rather positive attitude toward the usage of Big Data in the Helathcare domain, although there are some doubts about the implementation of the aforementioned technology in the Greek national healthcare system. © 2020 John Minou, John Mantas, Flora Malamateniou, Daphne Kaitelidou.Introduction The segmentation method has a number of approaches, one of which is clustering. The clustering method is widely used for segmenting retinal blood vessels, especially the k-mean algorithm and fuzzy c-means (FCM). Unfortunately, so far there have been no studies comparing the two methods for blood vessel segmentation. Many studies do not explain the reason for choosing the method. Aim This study aims to analyze the performance of the algorithms of k-means and FCM for retinal blood vessel segmentation. Methods This research method is divided into three stages, namely preprocessing, segmentation, and performance analysis. Preprocessing uses the green channel method, Contrast-limited adaptive histogram equalization (CLAHE) and median filter. Segmentation is divided into three processes, namely clustering, thresholding and determining the region of interest (ROI). In the thresholding process, the determination of the threshold value uses two methods, namely the mean and the median. The third stage performs performance analysis using the performance parameters of the area under the curve (AUC) and statistical tests. Results The statistical test results comparing FCM with k-means based on AUC values resulted in p-values less then 0.05 with a confidence level of 95%. Conclusion Retinal vascular segmentation with the FCM method is significantly better than k-means. © 2020 Wiharto Wiharto, Esti Suryani.Introduction The number of newly diagnosed skin cancers per year is greater than the sum of the four most common cancers breast, prostate, lung, and colon. The implementation of primary and secondary prevention measures, over the last 2 to 3 decades, has made a major contribution to successful treatment. Aim Evaluate the accuracy and reliability of teledermoscopic versus clinical diagnosis for skin cancers when diagnostic algorithms are used, and when GPs and surgical specialties are involved in the clinical procedure. Methods Digital dermoscope (TS-DD, by Teleskin company) was used for the acquisition of teledermoscopic photographs and specialized teledermoscopic software was used for clinical examination and teledermoscopic consultation. The teledermoscopic procedure itself was performed in two steps. The first step was a clinical examination using the ABCDE rule with digital dermoscopic photography of the suspected lesion. The second step was a 2-step dermoscopic evaluation using the second step ABCD algors may equally be involved in prevention. © 2020 Jadran Bandic, Selimir Kovacevic, Reuf Karabeg, Marijana Lazarov Bandic, Aleksandar Lazarov, Dejan Opric.Introduction Machine Learning (ML) is a rapidly growing subfield of Artificial Intelligence (AI). It is used for different purposes in our daily life such as face recognition, speech recognition, text translation in different languages, weather prediction, and business prediction. In parallel, ML also plays an important role in the medical domain such as in medical imaging. ML has various algorithms that need to be trained with large volumes of data to produce a well-trained model for prediction. Aim The aim of this study is to highlight the most suitable Data Augmentation (DA) technique(s) for medical imaging based on their results. Methods DA refers to different approaches that are used to increase the size of datasets. In this study, eight DA approaches were used on publicly available low-grade glioma tumor datasets obtained from the Tumor Cancer Imaging Archive (TCIA) repository. The dataset included 1961 MRI brain scan images of low-grade glioma patients. You Only Look Once (YOLO) version 3 model was trained on the original dataset and the augmented datasets separately. A neural network training/testing ecosystem named as supervisely with Tesla K80 GPU was used for YOLO v3 model training on all datasets. Results The results showed that the DA techniques rotate at 180o and rotate at 90o performed the best as data enhancement techniques for medical imaging. Conclusion Rotation techniques are found significant to enhance the low volume of medical imaging datasets. © 2020 Muhammad Farhan Safdar, Shayma Saad Al Kobaisi, Fatima Tuz Zahra.Introduction Refractive surgery procedures, transepithelial photorefractive keratectomy (T-PRK) and femtosecond laser in situ keratomileusis (Fs-LASIK) are regarded as safe and efficacious methods for correcting myopia and myopic astigmatism. These two methods do not have much differences in results when treating spherical myopia, while some differences does exist in treatment of myopic astigmatism. Vector analysis presents powerful tool to show the real differences between these two methods regarding higher order ocular aberrations and central corneal thickness of treated eyes. Aim The aim of the study is to investigate changes in higher order ocular aberrations (HOAs) and central corneal thickness (CCT) following treatment of myopia and myopic astigmatism above -5.00DS and up to -2.00DC after either T-PRK or Fs-LASIK. Methods Patients (30 eyes per group) underwent T-PRK (group I) or Fs-LASIK (group II) procedure using Schwind Amaris 750S laser. HOAs (3mm&5mm pupil) and CCT were measured objectively at pre-, 1,3 & 6 months postop in each case.