Secure data sharing with blockchain for remote health monitoring applications: a review.

Remote Health Monitoring (RHM) is going to reinvent the future healthcare industry and bring about abundant value to hospitals, doctors, and patients by overcoming the many challenges currently being faced in monitoring patient's well-being, promoting preventive care, and managing the quality of drugs and equipment. Despite the many benefits of RHM, it is yet to be widely deployed due to the healthcare data security and privacy challenges. Healthcare data are highly sensitive and require fail-safe measures against unauthorized data access, leakages, and manipulations, and as such, there are stringent regulations governing how healthcare data can be secured, communicated, and stored, such as General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA).

Federated Learning for Medical Image Analysis with Deep Neural Networks.

Medical image analysis using deep neural networks (DNN) has demonstrated state-of-the-art performance in image classification and segmentation tasks, aiding disease diagnosis. The accuracy of the DNN is largely governed by the quality and quantity of the data used to train the model. However, for the medical images, the critical security and privacy concerns regarding sharing of local medical data across medical establishments precludes exploiting the full DNN potential for clinical diagnosis.

Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks.

Artificial Intelligence (AI) techniques of deep learning have revolutionized the disease diagnosis with their outstanding image classification performance. In spite of the outstanding results, the widespread adoption of these techniques in clinical practice is still taking place at a moderate pace. One of the major hindrance is that a trained Deep Neural Networks (DNN) model provides a prediction, but questions about why and how that prediction was made remain unanswered.

Automated grading of chest x-ray images for viral pneumonia with convolutional neural networks ensemble and region of interest localization.

Following its initial identification on December 31, 2019, COVID-19 quickly spread around the world as a pandemic claiming more than six million lives. An early diagnosis with appropriate intervention can help prevent deaths and serious illness as the distinguishing symptoms that set COVID-19 apart from pneumonia and influenza frequently don't show up until after the patient has already suffered significant damage. A chest X-ray (CXR), one of many imaging modalities that are useful for detection and one of the most used, offers a non-invasive method of detection.

Outcome after transcatheter occlusion of patent ductus arteriosus in infants less than 6 kg: A national study from United Kingdom and Ireland.

Objectives: This study aimed to report our national experience with transcatheter patent ductus arteriosus (PDA) occlusion in infants weighing <6 kg.

Background: The technique of transcatheter PDA closure has evolved in the past two decades and is increasingly used in smaller patients but data on safety and efficacy are limited.

Methods: Patients weighing < 6 kg in whom transcatheter PDA occlusion was attempted in 13 tertiary paediatric cardiology units in the United Kingdom and Ireland were retrospectively analyzed to review the outcome and complications.

WiseEye: Next Generation Expandable and Programmable Camera Trap Platform for Wildlife Research.

The widespread availability of relatively cheap, reliable and easy to use digital camera traps has led to their extensive use for wildlife research, monitoring and public outreach. Users of these units are, however, often frustrated by the limited options for controlling camera functions, the generation of large numbers of images, and the lack of flexibility to suit different research environments and questions. We describe the development of a user-customisable open source camera trap platform named 'WiseEye', designed to provide flexible camera trap technology for wildlife researchers.

Limitations of recreational camera traps for wildlife management and conservation research: a practitioner's perspective.

The availability of affordable 'recreational' camera traps has dramatically increased over the last decade. We present survey results which show that many conservation practitioners use cheaper 'recreational' units for research rather than more expensive 'professional' equipment. We present our perspective of using two popular models of 'recreational' camera trap for ecological field-based studies.

SPECTRUM OF CENTRAL NERVOUS SYSTEM TUMOURS--A SINGLE CENTER HISTOPATHOLOGICAL REVIEW OF 761 CASES OVER 5 YEARS.

Background: The incidence of central nervous system (CNS) tumours has rapidly increased over the past few years. There is no accurate nationwide CNS tumour epidemiology in Pakistan that makes policy making for tumour screening and early treatment difficult. The purpose of this study was to provide the spectrum of CNS tumours in a premier diagnostic and referral centre of Pakistan.