ORCID Profile
0000-0002-9421-7344
Current Organisation
University of Tasmania
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Publisher: Springer Science and Business Media LLC
Date: 10-08-2023
DOI: 10.1007/S44230-023-00038-Y
Abstract: In recent years, artificial intelligence (AI) technology has been used in most if not all domains and has greatly benefited our lives. While AI can accurately extract critical features and valuable information from large amounts of data to help people complete tasks faster, there are growing concerns about the non-transparency of AI in the decision-making process. The emergence of explainable AI (XAI) has allowed humans to better understand and control AI systems, which is motivated to provide transparent explanations for the decisions made by AI. This article aims to present a comprehensive overview of recent research on XAI approaches from three well-defined taxonomies. We offer an in-depth analysis and summary of the status and prospects of XAI applications in several key areas where reliable explanations are urgently needed to avoid mistakes in decision-making. We conclude by discussing XAI’s limitations and future research directions.
Publisher: Springer Science and Business Media LLC
Date: 22-07-2023
DOI: 10.1007/S13755-023-00231-0
Abstract: With ageing populations around the world, there is a rapid rise in the number of people with Alzheimer’s disease (AD) and Parkinson’s disease (PD), the two most common types of neurodegenerative disorders. There is an urgent need to find new ways of aiding early diagnosis of these conditions. Multimodal learning of clinically accessible data is a relatively new approach that holds great potential to support early precise diagnosis. This scoping review follows the PRSIMA guidelines and we analysed 46 papers, comprising 11,750 participants, 3569 with AD, 978 with PD, and 2482 healthy controls the recency of this topic was highlighted by nearly all papers being published in the last 5 years. It highlights the effectiveness of combining different types of data, such as brain scans, cognitive scores, speech and language, gait, hand and eye movements, and genetic assessments for the early detection of AD and PD. The review also outlines the AI methods and the model used in each study, which includes feature extraction, feature selection, feature fusion, and using multi-source discriminative features for classification. The review identifies knowledge gaps around the need to validate findings and address limitations such as small s le sizes. Applying multimodal learning of clinically accessible tests holds strong potential to aid the development of low-cost, reliable, and non-invasive methods for early detection of AD and PD.
Publisher: Springer Science and Business Media LLC
Date: 10-12-2022
Publisher: Elsevier BV
Date: 2024
Publisher: Elsevier BV
Date: 2023
DOI: 10.2139/SSRN.4409606
Publisher: Springer Science and Business Media LLC
Date: 18-07-2022
DOI: 10.1186/S12883-022-02772-5
Abstract: The worldwide prevalence of dementia is rapidly rising. Alzheimer’s disease (AD), accounts for 70% of cases and has a 10–20-year preclinical period, when brain pathology covertly progresses before cognitive symptoms appear. The 2020 Lancet Commission estimates that 40% of dementia cases could be prevented by modifying lifestyle/medical risk factors. To optimise dementia prevention effectiveness, there is urgent need to identify in iduals with preclinical AD for targeted risk reduction. Current preclinical AD tests are too invasive, specialist or costly for population-level assessments. We have developed a new online test, TAS Test, that assesses a range of motor-cognitive functions and has capacity to be delivered at significant scale. TAS Test combines two innovations: using hand movement analysis to detect preclinical AD, and computer-human interface technologies to enable robust ‘self-testing’ data collection. The aims are to validate TAS Test to [1] identify preclinical AD, and [2] predict risk of cognitive decline and AD dementia. Aim 1 will be addressed through a cross-sectional study of 500 cognitively healthy older adults, who will complete TAS Test items comprising measures of motor control, processing speed, attention, visuospatial ability, memory and language. TAS Test measures will be compared to a blood-based AD biomarker, phosphorylated tau 181 (p-tau181). Aim 2 will be addressed through a 5-year prospective cohort study of 10,000 older adults. Participants will complete TAS Test annually and subtests of the Cambridge Neuropsychological Test Battery (CANTAB) biennially. 300 participants will undergo in-person clinical assessments. We will use machine learning of motor-cognitive performance on TAS Test to develop an algorithm that classifies preclinical AD risk (p-tau181-defined) and determine the precision to prospectively estimate 5-year risks of cognitive decline and AD. This study will establish the precision of TAS Test to identify preclinical AD and estimate risk of cognitive decline and AD. If accurate, TAS Test will provide a low-cost, accessible enrichment strategy to pre-screen in iduals for their likelihood of AD pathology prior to more expensive tests such as blood or imaging biomarkers. This would have wide applications in public health initiatives and clinical trials. ClinicalTrials.gov Identifier: NCT05194787 , 18 January 2022. Retrospectively registered.
Publisher: Wiley
Date: 07-11-2020
DOI: 10.1111/ADD.15253
Abstract: Cannabis products with high delta-9-tetrahydrocannabinol (THC) concentrations carry an increased risk of addiction and mental health disorders, while it has been suggested that cannabidiol (CBD) may moderate the effects of THC. This study aimed to systematically review and meta-analyse changes in THC and CBD concentrations in cannabis over time (PROSPERO registration: CRD42019130055). Embase, MEDLINE® and Epub Ahead of Print, In-Process and Other Non-Indexed Citations and Daily, Global Health, PsycINFO and Scopus were searched from inception to 27/03/2019 for observational studies reporting changes in mean THC and/or CBD concentration in cannabis over at least three annual time points. Searches and extraction were conducted by two independent reviewers. Random effects meta-regression models estimated annual changes in THC and CBD for each product within each study these estimates were pooled across studies in random effects models. We identified 12 eligible studies from the USA, UK, Netherlands, France, Denmark, Italy and New Zealand. For all herbal cannabis, THC concentrations increased by 0.29% each year (95% CI: 0.11, 0.47), P < 0.001 based on 66 747 cannabis s les from eight studies, 1970-2017. For cannabis resin, THC concentrations increased by 0.57% each year (95% CI: 0.10, 1.03), P = 0.017 based on 17 371 s les from eight studies, 1975-2017. There was no evidence for changes in CBD in herbal cannabis [-0.01% (95% CI: -0.02, 0.01), P = 0.280 49 434 s les from five studies, 1995-2017] or cannabis resin [0.03% (95% CI: -0.11, 0.18), P = 0.651 11 382 s les from six studies, 1992-2017]. Risk of bias was low apart from non-random s ling in most studies. There was evidence of moderate to substantial heterogeneity. Concentrations of delta-9-tetrahydrocannabinol (THC) in international cannabis markets increased from 1970 to 2017 while cannabidiol (CBD) remained stable. Increases in THC were greater in cannabis resin than herbal cannabis. Rising THC in herbal cannabis was attributable to an increased market share of high-THC sinsemilla relative to low-THC traditional herbal cannabis.
No related grants have been discovered for Guan Huang.