Automated pneumothorax triaging in chest X-rays in the New Zealand population using deep-learning algorithms.

Introduction: The primary aim was to develop convolutional neural network (CNN)-based artificial intelligence (AI) models for pneumothorax classification and segmentation for automated chest X-ray (CXR) triaging. A secondary aim was to perform interpretability analysis on the best-performing candidate model to determine whether the model's predictions were susceptible to bias or confounding.

Method: A CANDID-PTX dataset, that included 19,237 anonymized and manually labelled CXRs, was used for training and testing candidate models for pneumothorax classification and segmentation.

Artificial Intelligence-Enabled DDoS Detection for Blockchain-Based Smart Transport Systems.

A smart public transport system is expected to be an integral part of our human lives to improve our mobility and reduce the effect of our carbon footprint. The safety and ongoing maintenance of the smart public transport system from cyberattacks are vitally important. To provide more comprehensive protection against potential cyberattacks, we propose a novel approach that combines blockchain technology and a deep learning method that can better protect the smart public transport system.

Entitlement-Based Access Control for Smart Cities Using Blockchain.

Smart cities use the Internet of Things (IoT) devices such as connected sensors, lights, and meters to collect and analyze data to improve infrastructure, public utilities, and services. However, the true potential of smart cities cannot be leveraged without addressing many security concerns. In particular, there is a significant challenge for provisioning a reliable access control solution to share IoT data among various users across organizations.

Design of a multi-site multi-state clinical trial of home monitoring of chronic disease in the community in Australia.

Background: Telehealth services based on at-home monitoring of vital signs and the administration of clinical questionnaires are being increasingly used to manage chronic disease in the community, but few statistically robust studies are available in Australia to evaluate a wide range of health and socio-economic outcomes. The objectives of this study are to use robust statistical methods to research the impact of at home telemonitoring on health care outcomes, acceptability of telemonitoring to patients, carers and clinicians and to identify workplace cultural factors and capacity for organisational change management that will impact on large scale national deployment of telehealth services. Additionally, to develop advanced modelling and data analytics tools to risk stratify patients on a daily basis to automatically identify exacerbations of their chronic conditions.

Barriers for delivering telehealth in rural australia: a review based on Australian trials and studies.

Background: Australians in rural and remote areas live with far poorer health outcomes than those in urban areas. Telehealth services have emerged as a promising solution to narrow this health gap, as they improve the level and diversity of health services delivery to rural and remote Australian communities. Although the benefits of telehealth services are well studied and understood, the uptake has been very slow.

A framework for telehealth program evaluation.

Evaluating telehealth programs is a challenging task, yet it is the most sensible first step when embarking on a telehealth study. How can we frame and report on telehealth studies? What are the health services elements to select based on the application needs? What are the appropriate terms to use to refer to such elements? Various frameworks have been proposed in the literature to answer these questions, and each framework is defined by a set of properties covering different aspects of telehealth systems. The most common properties include application, technology, and functionality.