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. Evaluation metrics for classification performance included Area under the receiver operating characteristic curve (AUC-ROC), sensitivity and specificity, whilst segmentation performance was measured using mean Dice and true-positive (TP)-Dice coefficients. Interpretability analysis was performed using Grad-CAM heatmaps. Finally, the best-performing model was implemented for a triage simulation. RESULTS: The best-performing model demonstrated a sensitivity of 0.93, specificity of 0.95 and AUC-ROC of 0.94 in identifying the presence of pneumothorax. A TP-Dice coefficient of 0.69 is given for segmentation performance. In triage simulation, mean reporting delay for pneumothorax-containing CXRs is reduced from 9.8 ± 2 days to 1.0 ± 0.5 days (P-value < 0.001 at 5% significance level), with sensitivity 0.95 and specificity of 0.95 given for the classification performance. Finally, interpretability analysis demonstrated models employed logic understandable to radiologists, with negligible bias or confounding in predictions. CONCLUSION: AI models can automate pneumothorax detection with clinically acceptable accuracy, and potentially reduce reporting delays for urgent findings when implemented as triaging tools.

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. We present a novel entitlement-based blockchain-enabled access control architecture that can be used for smart cities (and for any ap-plication domains that require large-scale IoT deployments). Our proposed entitlement-based access control model is flexible as it facilitates a resource owner to safely delegate access rights to any entities beyond the trust boundary of an organization. The detailed design and implementation on Ethereum blockchain along with a qualitative evaluation of the security and access control aspects of the proposed scheme are presented in the paper. The experimental results from private Ethereum test networks demonstrate that our proposal can be easily implemented with low latency. This validates that our proposal is applicable to use in the real world IoT environments.

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. By the creation of signed and verified blockchain blocks and chaining of hashed blocks, the blockchain in our proposal can withstand unauthorized integrity attack that tries to forge sensitive transport maintenance data and transactions associated with it. A hybrid deep learning-based method, which combines autoencoder (AE) and multi-layer perceptron (MLP), in our proposal can effectively detect distributed denial of service (DDoS) attempts that can halt or block the urgent and critical exchange of transport maintenance data across the stakeholders. The experimental results of the hybrid deep learning evaluated on three different datasets (i.e., CICDDoS2019, CIC-IDS2017, and BoT-IoT) show that our deep learning model is effective to detect a wide range of DDoS attacks achieving more than 95% F1-score across all three datasets in average. The comparison of our approach with other similar methods confirms that our approach covers a more comprehensive range of security properties for the smart public transport system.

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. METHODS/DESIGN: A clinical trial is proposed at five locations in five states and territories along the Eastern Seaboard of Australia. Each site will have 25 Test patients and 50 case matched control patients. All participants will be selected based on clinical criteria of at least two hospitalisations in the previous year or four or more admissions over the last five years for a range of one or more chronic conditions. Control patients are matched according to age, sex, major diagnosis and their Socio-Economic Indexes for Areas (SEIFA). The Trial Design is an Intervention control study based on the Before-After-Control-Impact (BACI) design. DISCUSSION: Our preliminary data indicates that most outcome variables before and after the intervention are not stationary, and accordingly we model this behaviour using linear mixed-effects (lme) models which can flexibly model within-group correlation often present in longitudinal data with repeated measures. We expect reduced incidence of unscheduled hospitalisation as well as improvement in the management of chronically ill patients, leading to better and more cost effective care. Advanced data analytics together with clinical decision support will allow telehealth to be deployed in very large numbers nationally without placing an excessive workload on the monitoring facility or the patient's own clinicians. TRIAL REGISTRATION: Registered with Australian New Zealand Clinical Trial Registry on 1st April 2013. Trial ID: ACTRN12613000635763.

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. MATERIALS AND METHODS: To understand the underpinning issues, we conducted a literature review on barriers to telehealth adoption in rural and remote Australian communities, based on the published works of Australian clinical trials and studies. RESULTS: This article presents our findings using a comprehensive barrier matrix. This matrix is composed of four stakeholders (governments, technology developers and providers, health professionals, and patients) and five different categorizations of barriers (regulatory, financial, cultural, technological, and workforce). We explain each cell of the matrix (four stakeholders×five categories) and map the reported work into the matrix. CONCLUSIONS: Several exemplary barrier cases are also described to give more insights into the complexity and dilemma of adopting telehealth services. Finally, we outline recent technological advancements that have a great potential to overcome some of the identified barriers.

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. With the proliferation of telehealth, it is important not only to understand these properties, but also to define new properties to account for a wider range of context of use and evaluation outcomes. This article presents a comprehensive framework for delivery design, implementation, and evaluation of telehealth services. We first survey existing frameworks proposed in the literature and then present our proposed comprehensive multidimensional framework for telehealth. Six key dimensions of the proposed framework include health domains, health services, delivery technologies, communication infrastructure, environment setting, and socioeconomic analysis. We define a set of example properties for each dimension. We then demonstrate how we have used our framework to evaluate telehealth programs in rural and remote Australia. A few major international studies have been also mapped to demonstrate the feasibility of the framework. The key characteristics of the framework are as follows: (a) loosely coupled and hence easy to use, (b) provides a basis for describing a wide range of telehealth programs, and (c) extensible to future developments and needs.