OffshoreMuster: An Integrated Real Time Localisation, Mustering and Evacuation Management System for Offshore Oil & Gas Health and Safety Operations

Oil & Gas offshore platforms are industrial "towns", ranked among the most hazardous working environments. Emergency situations in such environments are unpredictable and characterized by time pressure and rapidly changing conditions. OffshoreMuster brings together the latest technological advancements in real-time personnel tracking and e-mustering, complementing the existing Health and Safety (HSE) procedures, by enabling situation awareness over personnel location and status which is a key factor supporting better decision-making towards zero casualty in emergency situations. The system's underlying technology, developed after years of dedicated research and development efforts, consists of specialised low-power wireless wearable devices, customised gateways and a secure backbone network infrastructure feeding a modular decision support software system with real-time streams of data for processing and visualisation of information relevant to personnel situation assessment. HSE processes have been transformed into systematic procedures, allowing additional computer-aided decision support features, like the real-time observation of the fire-fighting team response status, the concentration of people in specific areas, instant alerting or the last-known position of a missing person. Lightweight ubiquitous devices in the form of a bracelet or embedded in the uniform are assigned to People on Board (PoB) and periodically transmit real-time location and status awareness data. A network of dedicated gateways, which are placed at specific locations on the platform or vessel, connected through the infrastructure's ethernet or wireless network, relay the data to a central decision support system. Specialised localisation algorithms and data analytics tools process the data to estimate the personnel positions and PoB status information, interactively visualising in real-time location awareness, incident escalation and alerting, which can significantly reduce response time and speed up a safe evacuation procedure. Computer-aided decision support combined with ultra-low power autonomous IoT technologies systems play a significant role in controlling, managing, and preventing critical incidents in harsh working environments, contributing into minimisation of accidents occurrence in Oil & Gas environments. The presented underling technology has been validated in maritime environments with more than 500 people taking part in real drills (TRL-8). The technology has been tailored to enhance the safety of personnel working in offshore Oil & Gas assets, currently being under laboratory testing and evaluation while a full-scale industrial deployment is scheduled for the autumn of 2021. The OffshoreMuster hardware and software components, integrated into a unified solution tailored for the offshore Oil & Gas industry, are presented for the first time. The system has been designed and developed with the support of the European Commission, co-funded by the Fast Track to Innovation Program (Grant Agreement Number 878950).

Computer-Aided Decision-Making System for Endometrial Atypical Hyperplasia based on Multimodal & Multi-Instance Deep Convolution Networks

The pathological diagnosis is the gold standard for neoplasms and their precursors, which is highly relevant to the treatment planning and the prognostic analysis. Currently, deep learning networks have been used for the pathological computer-assisted diagnosis and treatment decision-makings. However, due to extremely large size of the whole slide images (WSIs) of pathological slides, the prevailing deep learning models are un-applicable directly in the WSIs analysis. Moreover, the precise exclusion of the blank regions and interfere regions, as well as the manual annotation of various lesioned and normal regions in super large WSIs are infeasible and unavailable in clinical practice. To address aforementioned problems, we develop an computer-aided decision-making system based on multimodal and multi-instance deep convolution networks (CNN) to assist in the diagnosis and treatment of endometrial atypical hyperplasia (AH)/ endometrial intraepithelial hyperplasia (EIH). Firstly, we set up the frame-work of computer-aided decision-making system based on the WSIs image patterns of AH/EIH, and then transfer the large-scale WSI analysis to the small-scale analysis of multiple suspected lesion regions which can be accomplished the major computer vision models, and eventually the results of prognostic analysis for multiple small-scale suspected lesion regions are summarized to obtain the prognostic results of WSIs by the decision supporting algorithm based on the cognition intelligence. We validate the method via experimental analysis of 102 endometrial atypical hyperplasia patients at the West China Second University Hospital of Sichuan University. The performance achieved for endometrial AH/EIH prognostic analysis includes accuracy (85.3%), precision (84.6%), recall (86.3%). Meanwhile, the method has superior performance to prognostic judgment of a single pathologist as well as approximates to analysis results determined by three pathologists according to the majority voting method.

A Survey on Biomedical Informatics for Computer-Aided Decision Support System

Biomedical computing for computer-aided biomedical diagnostics and the decision support system has developed a platform for the biomedical setting during the last few decades. As early as 1971, there were elaborate and basic applications of management information systems driven by biomedical informatics. According to a 1994 assessment, this field's literature stretches back to the 1950s. Medical decision is more challenging than ever for doctors and other caregivers due to the amount and complexity of contemporary patient information. This circumstance necessitates the application of medical computing technologies to evaluate data and formulate suggestions and/or forecasts to aid decision makers. Over the past two decades, healthcare informatics tools, such as computer-aided decision support, have grown indispensable and extensively employed. This article gives a quick overview of such technologies, their productivity applications and methodology, as well as the problems and directions they imply for the future.

An Analysis of Medical Informatics and Application of Computer-Aided Decision Support Framework

Due to the advent of technology in the medical sector, the future of the Computer-Aided Decision (CAD) is promising as a support system for the processing of images. Since there is significant results reported due to the application of diagnostic radiology in the healthcare facility, radiologists are looking forward to enhancing medical and bioinformatics using CAD. Medical evaluations and trials have been done over the past few decades to aid in the optimization of accurate programs and evaluate the real contributions of CAD in the medical informatics interpretation procedures. Health experts and radiologists utilizing patient outputs from fundamental application of CAD are placed in the best position to focus on the final decisions concerning the performance of patients and diagnosis. However, researches have shown that the computer outputs require not projecting significant general accuracy compared to a certain radiologist to enhance patients’ performance. The volume and measure of the present patient data including their complexity to enhance the process of making proper healthcare decisions while making it problematic for healthcare practitioners and physicians to facilitate the management of patients. This condition calls for the usage of biomedical informatics methodologies to effectively process information, create biomedical implementations and informatics frameworks for CAD support systems. With that regard, this paper evaluates the medical and bioinformatics based on the application of CAD systems. It further projects on the applications of the systems, their application guidelines and techniques. The paper ends with the analysis of the future problems and directions of the CAD support framework.