Proposal for Medical Data Transmission in Healthcare Systems

Background: Information systems used in hospitals are slow and consume a lot of system memory, facilitating crashes, impacting patients seeking consultation face long waiting periods by a medical specialist; Still considering that exchange patient data and medical consultations in system interconnected between hospitals, for scheduling of consultations may become even more latent.Methods: Aiming to solve such problems, the present study implements modeling with discrete-event technology applied to a healthcare system, modulating the signal transmitted with the DQPSK format, through the simulation environment, the Simulink of the MATLAB software, improving the transmission of data, through a pre-coding process of bits adopting discrete events in the signal before modulation.Results: This study aims to increase the information capacity for healthcare systems, bringing a new approach for signal transmission, undertaken in the discrete domain employing the discrete entities in the bit generation process, this use being the differential applied on the bit itself, in the physical layer, showing better computational performance regarding memory utilization related to compression of information, showing an improvement of 101.52%.Conclusion: The proposal developed has the properties of improving the capacity of hospital services and can increase the performance of the communication between all medical devices, this positive impact is the result that the data stream will consume fewer communication resources.

Biomedical Cyber-Physical Systems in the Light of Database as a Service (DBaaS) Paradigm

Background: A database (DB) to store indexed information about drug delivery, test, and their temporal behavior is paramount in new Biomedical Cyber-Physical Systems (BCPSs). The term Database as a Service (DBaaS) means that a corporation delivers the hardware, software, and other infrastructure required by companies to operate their databases according to their demands instead of keeping an internal data warehouse. Methods: BCPSs attributes are presented and discussed. One needs to retrieve detailed knowledge reliably to make adequate healthcare treatment decisions. Furthermore, these DBs store, organize, manipulate, and retrieve the necessary data from an ocean of Big Data (BD) associated processes. There are Search Query Language (SQL), and NoSQL DBs. Results: This work investigates how to retrieve biomedical-related knowledge reliably to make adequate healthcare treatment decisions. Furthermore, Biomedical DBaaSs store, organize, manipulate, and retrieve the necessary data from an ocean of Big Data (BD) associated processes. Conclusion: A NoSQL DB allows more flexibility with changes while the BCPSs are running, which allows for queries and data handling according to the context and situation. A DBaaS must be adaptive and permit the DB management within an extensive variety of distinctive sources, modalities, dimensionalities, and data handling according to conventional ways.

SDR-Based High-Definition Video Transmission for Biomedical Engineering

Background: Software-Defined Radio (SDR) frameworks from cellular telephone base stations, e.g., Multiservice Distributed Access System (MDAS) and small cells, employ extensively integrated RF agile transceivers. The Internet of Medical Things (IoMT) is the collection of medical devices and applications that connect to healthcare IT systems through online computer networks. Medical devices equipped with Wi-Fi allow M2M communication, which is the backbone of IoMT and associated devices linked to cloud platforms containing stored data to be analyzed. Examples of IoMT include remote patient monitoring of people with chronic or long-term conditions, tracking patient medication orders and the location of patients admitted to hospitals, and patients' wearables to send info to caregivers. Infusion pumps connected to dashboards and hospital beds rigged with sensors measuring patients' vital signs are medical devices that can be converted to or deployed as IoMT technology. Methods: This work proposes an SDR architecture to allow wireless High-Definition (HD) video broadcast for biomedical applications. This text examines a Wideband Wireless Video (WWV) signal chain implementation using the transceivers, the data transmitted volume, the matching occupied RF bandwidth, the communication distance, the transmitter’s power, and the implementation of the PHY layer as Orthogonal Frequency Division Multiplexing (OFDM) with test results to evade RF interference. Results: As the IoMT grows, the amount of possible IoMT uses increases. Many mobile devices employ Near Field Communication (NFC) Radio Frequency Identification (RFID) tags allowing them to share data with IT systems. RFID tags in medical equipment and supplies allow hospital staff can remain aware of the quantities they have in stock. The practice of using IoMT devices to observe patients in their homes remotely is also known as telemedicine. This kind of treatment spares patients from traveling to healthcare facilities whenever they have a medical question or change in their condition. Conclusion: An SDR-based HD biomedical video transmission is proposed, with its benefits and disadvantages for biomedical WWV are discussed. The security of IoMT sensitive data is a developing concern for healthcare providers.

DICOM’s Standardization in Histo-Pathology

Background: The Digital Imaging and Communications in Medicine (DICOM) standard helps to represent, store, and to exchange healthcare images associated with its data. DICOM develops over time and is continuously adapted to match the rigors of new clinical demands and technologies. An uphill battle in this regard is to conciliate new software programs with legacy systems. Methods: This work discusses the essential aspects of the standard and assesses its capabilities and limitations in a multisite, multivendor healthcare system aiming at Whole Slicing Image (WSI) procedures. Selected relevant DICOM attributes help to develop and organize WSI applications that extract and handle image data, integrated patient records, and metadata. DICOM must also interface with proprietary file formats, clinical metadata and from different laboratory information systems. Standard DICOM validation tools to measure encoding, storing, querying and retrieval of medical data can verify the generated DICOM files over the web. Results: This work investigates the current regulations and recommendations for the use of DICOM with WSI data. They rely mostly on the EU guidelines that help envision future needs and extensions based on new examination modalities like concurrent use of WSI with in-vitro imaging and 3D WSI. Conclusion: A DICOM file format and communication protocol for pathology has been defined. However, adoption by vendors and in the field is pending. DICOM allows efficient access and prompt availability of WSI data as well as associated metadata. By leveraging a wealth of existing infrastructure solutions, the use of DICOM facilitates enterprise integration and data exchange for digital pathology. In the future, the DICOM standard will have to address several issues due to the way samples are gathered and encompassing new imaging technologies.

Hematology and Digital Image Processing: Watershed Transform-Based Methodology for Blood Cell Counting Using the WT-MO Algorithm

Background: Most diseases can be detected by routine examination, even if they are in the initial phase. Currently, one of the most requested medical laboratory tests is that which allows detecting from bacterial infections until leukemias. However, for less favored populations, this examination can be seen as having a high cost. Methods: Thus, this study introduces an algorithm of segmentation of images capable of detecting and counting red blood cells and leukocytes present in digital images of blood smear. The methodology was named by WT-MO, once it relies on the concepts of Watershed Transform and Morphological Operations. The experiments were conducted in the MATLAB software simulation environment, where 25 images were used in order to evaluate the accuracy, processing time, and execution time of the WT-MO algorithm. Results: The results show that the WT-MO methodology presents high accuracy, reaching 96% and 92% in the red blood cell and leukocyte counts, respectively; reliability and low processing time, reaching an average processing time and execution time, achieving from 0.74 to 2.17 seconds. Therefore, the WT-MO algorithm can be seen as the first step in making laboratory tests more accessible to populations in underdeveloped and developing countries. Conclusion: The WT-MO methodology helps not only disadvantaged populations gain access to low-cost, high-reliability tests but also has excellent potential for use in laboratories in developed countries.

Self-Organizing Algorithm for Massive Tractography Datasets Clustering with Outliers Elimination Based on Multiple Species Flocking Model

Background: The study of White Matter (WM) connectivity is of general interest in neuroscience, which is achieved by the analysis and clustering of the streamlines composed of the tractography dataset. The WM streamlines clustering is a challenge, because of the complexity and the vast size of the WM tractography dataset and its composition by various streamlines, in additionally to the presence of outliers. Methods: Several WM clustering methods have been proposed in the literature to overcome these constraints. However, these methods stay statics. Once the clustering streamline is performed, it stays in this one. In this paper, we propose a new framework of distributed multiagent, improving, and adapting a bio-inspired model called Multiple Species Flocking (MSF) for WM streamlines clustering and automatic outlier elimination. Results: The basic MSF rules are modified and adapted to perform streamlines clustering in higher dimensions. Specifically, each streamline is associated with a mobile agent and move onto a virtual space to form a group following the defined rules. Only the agents assigned to similar streamlines form a flock, whereas the agents assigned to dissimilar streamlines are sidelined and considered as outliers. Conclusion: Swarm intelligence features of the approach, such as adaptivity, parallelism, dynamism, and decentralization, make our algorithm scalable to large datasets, very fast and accurate, which are confirmed by experimental results on synthetic and real datasets.

Content-Based Image Retrieval (CBIR) in Big Histological Image Databases

Background: Automatic analysis of Histopathological Images (HIs) demands image processing and Computational Intelligence (CI) techniques. Both Computer-Aided Diagnosis (CAD) and Content-Based Image-Retrieval (CBIR) systems assist diagnosis, disease discovery, and biological decision-making. Classical tests comprise screening examinations and biopsy. Histopathology slides offer more ample diagnosis data. However, manual examination of microscopic images is labor-intensive and time-consuming and may depend on a subjective assessment by the pathologist, which can be a challenge. Methods: This work discusses a CBIR framework to extract and handle histological data, histological metadata, integrated patient records, specimen metadata, attributes, and similar stored files. This work presents a scalable image-retrieval framework for intelligent HI analysis with real-time retrieval. The potential applications of this framework include image-guided diagnosis, decision support, healthcare education, and efficient biological data management. Results: The considerable amount of biological-related data prompted the development and deployment of large-scale databases and data-driven techniques to bridge the semantic gap between images and diagnostic information. The new cloud computing technologies and the concept of cyber-physical systems have improved the CBIR architectures considerably. The proposed scalable architecture relies on CI and validates performance on several HIs acquired from microscopic tissues. Extensive assessments show improvements in terms of disease classification and retrieval tests. Conclusion: This research effort significant contributions are twofold. 1) Defining a comprehensive and large-scale CBIR framework to analyze HIs with high-dimensional features and CI methods successfully. 2) high-performance updating and optimization strategies improve the querying while better handling new training samples than traditional methods.

Semantic Segmentation of Medical Images with Deep Learning: Overview

Semantic segmentation is one of the biggest challenging tasks in computer vision, especially in medical image analysis, it helps to locate and identify pathological structures automatically. It is an active research area. Continuously different techniques are proposed. Recently Deep Learning is the latest technique used intensively to improve the performance in medical image segmentation. For this reason, we present in this non-systematic review a preliminary description about semantic segmentation with deep learning and the most important steps to build a model that deal with this problem.

Acute Pancreatitis Between Practice and Recommendations

Background:Acute inflammation of the pancreas, acute pancreatitis (AP) shows an increasing incidence in the world as well as in Algeria. However, lethality and mortality are decreasing, mainly due to recommendations from Atlanta, the International Pancreatology Association and the American Pancreatology Association. The purpose of this work was to analyze the management of AP in the general surgery department of the UHC of Sidi Bel Abbes. Method: In a retrospective study, 100 acute pancreatitis cases admitted to general surgery between January 2017 and December 2019 were analyzed. The abdominal scanner was systematic at the admission. The rate of lipasemia, when greater than 3 times the normal one, allowed, in association with the evocative pain, to make the diagnosis. The severity of AP was assessed using the Balthazar score. An MRI was requested when the scanner was inconclusive, to look for an etiology and / or possible complications. 79 (79%) patients received broad-spectrum antibiotic therapy and diet was systematic. Results: We have transmitted the recommendations of scholar societies to the various specialists and the management of AP is currently more unified and more effective. Scanner is no longer systematic in the early phase. Antibiotic therapy is no longer administered without biological evidence. Enteral nutrition is now possible. Conclusion: We were able to reach the goal of reducing hospitalization and scanning requests. A further analysis of 100 other PA files is planned to confirm the obtained results.

The American Academy of Anti-Aging Medicine Looks Ahead Towards the 28th Annual World Congress

This editorial report the 28th Annual World Congress ongranized by The American Academy of Anti-Aging Medicine. The congress will be taking place entirely online from December 12-13, 2020. The rest of this edtorial discribs the details and contents oft he congress.