Intelligent decision support systems for dementia care: A scoping review.

In the context of dementia care, Artificial Intelligence (AI) powered clinical decision support systems have the potential to enhance diagnosis and management. However, the scope and challenges of applying these technologies remain unclear. This scoping review aims to investigate the current state of AI applications in the development of intelligent decision support systems for dementia care. We conducted a comprehensive scoping review of empirical studies that utilised AI-powered clinical decision support systems in dementia care. The results indicate that AI applications in dementia care primarily focus on diagnosis, with limited attention to other aspects outlined in the World Health Organization (WHO) Global Action Plan on the Public Health Response to Dementia 2017-2025 (GAPD). A trifecta of challenges, encompassing data availability, cost considerations, and AI algorithm performance, emerges as noteworthy barriers in adoption of AI applications in dementia care. To address these challenges and enhance AI reliability, we propose a novel approach: a digital twin-based patient journey model. Future research should address identified gaps in GAPD action areas, navigate data-related obstacles, and explore the implementation of digital twins. Additionally, it is imperative to emphasize that addressing trust and combating the stigma associated with AI in healthcare should be a central focus of future research directions.

Web-Based Intervention for Multilingual Family Carers of People with Dementia: Insights from the DrawCare Study.

Addressing the needs of ethnically diverse multilingual people can be challenging in environments that are non-native to them. The consequences of this issue become more significant in healthcare contexts. Insights from the DrawCare study-an Australian study that explores the effectiveness of a web-based intervention for multilingual family carers of people with dementia-are presented illustrating the enabling role of digital health.

Digital Twins for More Precise and Personalized Treatment.

The use of Digital Twins (DTs) or the digital replicas of physical entities has provided benefits to several industry sectors, most notably manufacturing. To date, the application of DTs in the healthcare sector has been minimal, however. But, as pressure increases for more precise and personalized treatments, it behooves us to investigate the potential for DTs in the healthcare context. As a proof-of-concept demonstration prior to working with real patients, we attempt in this paper, to explore the potential for creating and using DTs. We do this in a synthetic environment at this stage, making use of data that is all computer-generated. DTs of synthetic present patients are created making use of data of synthetic past patients. In the real world, the clinical objective for creating such DTs of real patients would be to enable enhanced real-time clinical decision support to enable more precise and personalized care. The objective of the numerical experiment reported in this paper, is to envisage the possibilities and challenges of such an approach. We attempt to better understand the strengths and weaknesses of applying DTs in the healthcare context to support more precise and personalized treatments.

Improving the lives of ethnically diverse family carers and people living with dementia using digital media resources - Protocol for the Draw-Care randomised controlled trial.

Objectives: Ethnically diverse family carers of people living with dementia (hereafter carers and people with dementia) experience more psychological distress than other carers. To reduce this inequality, culturally adapted, multilingual, evidence-based practical assistance is needed. This paper details the Draw-Care study protocol including a randomised control trial (RCT) to test the effectiveness of a digital intervention comprising a multilingual website, virtual assistant, animated films, and information, on the lives of carers and people with dementia in Australia. Methods: The Draw-Care intervention will be evaluated in a 12-week active waitlist parallel design RCT with 194 carers from Arabic, Cantonese, Greek, Hindi, Italian, Mandarin, Spanish, Tamil, and Vietnamese-speaking language groups. Our intervention was based on the World Health Organization's (WHO) iSupport Lite online carer support messages and was co-designed with carers, people with dementia, service providers, and clinicians. Culturally adapted multilingual digital resources were created in nine languages and English. Results: In Phase I (2022), six co-design workshops with stakeholders and interviews with people with dementia informed the development of the intervention which will be trialled and evaluated in Phases II and III (2023 and 2024). Conclusions: Digital media content is a novel approach to providing cost-effective access to health care information. This study protocol details the three study phases including the RCT of a co-designed, culturally adapted, multilingual, digital intervention for carers and people with dementia to advance the evidence in dementia and digital healthcare research and help meet the needs of carers and people with dementia in Australia and globally.

'It's Too nice': Adapting iSupport Lite for Ethnically Diverse Family Carers of a Person with Dementia.

OBJECTIVES: Resources to support dementia carers from ethnically diverse families are limited. We explored carers' and service providers' views on adapting the World Health Organization's iSupport Lite messages to meet their needs. METHODS: Six online workshops were conducted with ethnically diverse family carers and service providers (n = 21) from nine linguistic groups across Australia. Recruitment was via convenience and snowball sampling from existing networks. Data were analyzed using thematic analysis. RESULTS: Participants reported that iSupport Lite over-emphasized support from family and friends and made help-seeking sound "too easy". They wanted messages to dispel notions of carers as "superheroes", demonstrate that caring and help-seeking is stressful and time-consuming, and that poor decision-making and relationship breakdown does occur. Feedback was incorporated to co-produce a revised suite of resources. CONCLUSIONS: Beyond language translation, cultural adaptation using co-design provided participants the opportunity to develop more culturally relevant care resources that meet their needs. These resources will be evaluated for clinical and cost-effectiveness in future research. CLINICAL IMPLICATIONS: By design, multilingual resources for carers must incorporate cultural needs to communicate support messages. If this intervention is effective, it could help to reduce dementia care disparities in ethnically diverse populations in Australia and globally.

Digital twins to enable better precision and personalized dementia care.

In this perspective paper, we want to highlight the potential benefits of incorporating digital twins to support better dementia care. In particular, we assert that, by doing so, it is possible to ensure greater precision regarding dementia care while simultaneously enhancing personalization. Digital twins have been used successfully in manufacturing to enable better prediction and tailoring of solutions to meet required needs, and thereby have enabled more effective and efficient deployment of resources. We develop a model for digital twin in the healthcare domain as a clinical decision support tool by extrapolating its current uses from the manufacturing domain. We illustrate the power of the developed model in the context of dementia. Given the rapid rise of chronic conditions and the pressures on healthcare delivery to provide high quality, cost-effective care anywhere and anytime, we assert that such an approach is consistent with a value-based healthcare philosophy and thus important as the numbers of people with dementia continues to grow exponentially and this pressing healthcare issue is yet to be optimally addressed. Further research and development in this rapidly evolving domain is a strategic priority for ensuring the delivery of superior dementia care.

Quantifying the Relative Thickness of Conductive Ferromagnetic Materials Using Detector Coil-Based Pulsed Eddy Current Sensors.

Thickness quantification of conductive ferromagnetic materials by means of non-destructive evaluation (NDE) is a crucial component of structural health monitoring of infrastructure, especially for assessing the condition of large diameter conductive ferromagnetic pipes found in the energy, water, oil, and gas sectors. Pulsed eddy current (PEC) sensing, especially detector coil-based PEC sensor architecture, has established itself over the years as an effective means for serving this purpose. Approaches for designing PEC sensors as well as processing signals have been presented in previous works. In recent years, the use of the decay rate of the detector coil-based time domain PEC signal for the purpose of thickness quantification has been studied. Such works have established that the decay rate-based method holds generality to the detector coil-based sensor architecture, with a degree of immunity to factors such as sensor shape and size, number of coil turns, and excitation current. Moreover, this method has shown its effectiveness in NDE of large pipes made of grey cast iron. Following such literature, the focus of this work is explicitly PEC sensor detector coil voltage decay rate-based conductive ferromagnetic material thickness quantification. However, the challenge faced by this method is the difficulty of calibration, especially when it comes to applications such as in situ pipe condition assessment since measuring electrical and magnetic properties of certain pipe materials or obtaining calibration samples is difficult in practice. Motivated by that challenge, in contrast to estimating actual thickness as done by some previous works, this work presents a protocol for using the decay rate-based method to quantify relative thickness (i.e., thickness of a particular location with respect to a maximum thickness), without the requirement for calibration.

Pulsed Eddy Current Sensing for Critical Pipe Condition Assessment.

Pulsed Eddy Current (PEC) sensing is used for Non-Destructive Evaluation (NDE) of the structural integrity of metallic structures in the aircraft, railway, oil and gas sectors. Urban water utilities also have extensive large ferromagnetic structures in the form of critical pressure pipe systems made of grey cast iron, ductile cast iron and mild steel. The associated material properties render NDE of these pipes by means of electromagnetic sensing a necessity. In recent years PEC sensing has established itself as a state-of-the-art NDE technique in the critical water pipe sector. This paper presents advancements to PEC inspection in view of the specific information demanded from water utilities along with the challenges encountered in this sector. Operating principles of the sensor architecture suitable for application on critical pipes are presented with the associated sensor design and calibration strategy. A Gaussian process-based approach is applied to model a functional relationship between a PEC signal feature and critical pipe wall thickness. A case study demonstrates the sensor's behaviour on a grey cast iron pipe and discusses the implications of the observed results and challenges relating to this application.