A Next Available Appointment (NAA) Tool to Better Manage Patient Delay Risk and Patient Scheduling Expectations in Specialist Clinics.

Every year there are approximately 3 million new outpatient specialist clinic appointments at local hospital networks in Victoria, Australia. Growing daily demand for these services leads to high-volume waiting lists and therefore long appointment delays for patients. This phenomenon emphasises the importance of providing analytics and tools to assist with waiting list management in outpatient specialist clinics.

Factors Associated with Adherence to Diabetic Retinopathy Screening Among Patients Attending a Nurse-Led Community Clinic in Australia: A Qualitative Study.

Despite the availability of screening services, the rate of diabetic retinopathy (DR) screening continues to be suboptimal in Australia, necessitating improvement. However, improving DR screening rates requires a more comprehensive understanding of the factors influencing adherence to the screening recommendations. This study aimed to explore the factors that influence adherence to DR screening among people with diabetes attending a community screening clinic in Australia.

Exploring the relationship between melanopsin gene variants, sleep, and markers of brain health.

Introduction: Melanopsin is a photopigment with roles in mediating sleep and circadian-related processes, which are often disrupted in Alzheimer's disease (AD). Melanopsin also impacts cognition and synaptogenesis. This study investigated the associations between melanopsin genetic variants, sleep, and markers of brain health.

Digital Twins for Clinical and Operational Decision-Making: Scoping Review.

Background: The health care industry must align with new digital technologies to respond to existing and new challenges. Digital twins (DTs) are an emerging technology for digital transformation and applied intelligence that is rapidly attracting attention. DTs are virtual representations of products, systems, or processes that interact bidirectionally in real time with their actual counterparts.

Development and Validation of a Tool to Predict Onset of Mild Cognitive Impairment and Alzheimer Dementia.

Importance: The ability to predict the onset of mild cognitive impairment (MCI) and Alzheimer dementia (AD) could allow older adults and clinicians to make informed decisions about dementia care.

Objective: To assess whether the age at onset of MCI and AD can be predicted using a statistical modeling approach.

Design, Setting, And Participants: This prognostic study used data from 2 aging and dementia cohort studies-the Australian Imaging, Biomarker and Lifestyle (AIBL) study and the Alzheimer's Disease Neuroimaging Initiative (ADNI)-for model development and validation of the Florey Dementia Index (FDI), a tool used to predict the age at onset of MCI and AD in older adults.

Diagnostic accuracy of early neonatal MRI in predicting adverse motor outcomes in children born preterm: Systematic review and meta-analysis.

Aim: To examine the diagnostic accuracy of Early structural and diffusion-weighted magnetic resonance imaging (MRI) (acquired at < 36 weeks postmenstrual age) to detect cerebral palsy (CP) or other adverse motor outcomes at or beyond 3 years corrected age in infants born preterm.

Method: In this systematic review and meta-analysis, we searched the CINAHL, Embase, PubMed, and Web of Science databases for studies without language restrictions and a prospectively registered protocol up to October 2023. We extracted the study details, associations presented, and meta-analyses conducted with pooled sensitivity and specificity.

Sleep discrepancy and brain glucose metabolism in community-dwelling older adults.

Sleep discrepancy (negative discrepancy reflects worse self-reported sleep than objective measures, such as actigraphy, and positive discrepancy the opposite) has been linked to adverse health outcomes. This study is first to investigate the relationship between sleep discrepancy and brain glucose metabolism (assessed globally and regionally via positron emission tomography), and to evaluate the contribution of insomnia severity and depressive symptoms to any associations. Using data from cognitively unimpaired community-dwelling older adults ( = 68), cluster analysis was used to characterise sleep discrepancy (for total sleep time (TST), wake after sleep onset (WASO), and sleep efficiency (SE)), and logistic regression was used to explore sleep discrepancy's associations with brain glucose metabolism, while controlling for insomnia severity and depressive symptoms.

Benchmarking the most popular XAI used for explaining clinical predictive models: Untrustworthy but could be useful.

Objective: This study aimed to assess the practicality and trustworthiness of explainable artificial intelligence (XAI) methods used for explaining clinical predictive models.

Methods: Two popular XAIs used for explaining clinical predictive models were evaluated based on their ability to generate domain-appropriate representations, impact clinical workflow, and consistency. Explanations were benchmarked against true clinical deterioration triggers recorded in the data system and agreement was quantified.

Diagnostic accuracy of neonatal structural MRI scores to predict 6-year motor outcomes of children born very preterm.

Aims: This study aimed to (1) evaluate associations between Early and Term structural MRI (sMRI) brain abnormality scores and adverse motor outcomes at 6-years corrected age (CA), (2) determine their diagnostic accuracy in predicting adverse motor outcomes and cerebral palsy (CP) at 6-years CA.

Methods: Infants born < 31-weeks gestational age (GA) returning for 6-year follow-up were included. Early and Term sMRI were scored using a validated method, deriving white matter, cortical grey matter, deep grey matter, cerebellar and global brain abnormality scores (GBAS).

A Machine Learning Model to Harmonize Volumetric Brain MRI Data for Quantitative Neuroradiologic Assessment of Alzheimer Disease.

Purpose To extend a previously developed machine learning algorithm for harmonizing brain volumetric data of individuals undergoing neuroradiologic assessment of Alzheimer disease not encountered during model training. Materials and Methods Neuroharmony is a recently developed method that uses image quality metrics as predictors to remove scanner-related effects in brain-volumetric data using random forest regression. To account for the interactions between Alzheimer disease pathology and image quality metrics during harmonization, the authors developed a multiclass extension of Neuroharmony for individuals with and without cognitive impairment.

A Pilot Study of PSMA PET/CT and MRI Fusion for Prostate Cancer: Software to Replace PET/MRI Hardware.

: Prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA PET/CT), in combination with magnetic resonance imaging (MRI), may enhance the diagnosis and staging of prostate cancer. Image fusion of separately acquired PET/CT and MRI images serve to facilitate clinical integration and treatment planning. This study aimed to investigate different PSMA PET/CT and MRI image fusion workflows for prostate cancer visualisation.

Tailoring capsid-directed evolution technology for improved AAV-mediated CAR-T generation.

Chimeric antigen receptor (CAR) T cell (CAR-T) therapies present options for patients diagnosed with certain leukemias. Recent advances of the technology included a method to integrate the CAR into the T cell receptor alpha constant (TRAC) locus to take advantage of the endogenous promoter and regulatory elements for CAR expression. This method used adeno-associated viral (AAV) vectors based on AAV6 to deliver the donor template encoding the CAR construct.

A multitiered analysis platform for genome sequencing: Design and initial findings of the Australian Genomics Cardiovascular Disorders Flagship.

Purpose: The Australian Genomics Cardiovascular Disorders Flagship was a national multidisciplinary collaboration. It aimed to investigate the feasibility of genome sequencing (GS) and functional genomics to resolve variants of uncertain significance (VUS) in the clinical management of patients and families with cardiomyopathies, primary arrhythmias, and congenital heart disease (CHD).

Methods: Between April 2019 and December 2021, 600 probands meeting cardiovascular disorder criteria from 17 cardiology and genetics clinics across Australia were enrolled in the Flagship and underwent GS.

Novel Multicomponent Digital Care Assistant and Support Program for People After Stroke or Transient Ischaemic Attack: A Pilot Feasibility Study.

Evidence is increasing for digital health programs targeting the secondary prevention of stroke. We aimed to determine the feasibility of the novel Care Assistant and support Program for people after Stroke (CAPS) or transient ischaemic attack (TIA) by combining person-centred goal setting and risk-factor monitoring through a web-based clinician portal, SMS messages, a mobile application (app), and a wearable device. We conducted a 12-week mixed-methods, open-label feasibility study.

Generating synthetic clinical text with local large language models to identify misdiagnosed limb fractures in radiology reports.

Large language models (LLMs) demonstrate impressive capabilities in generating human-like content and have much potential to improve the performance and efficiency of healthcare. An important application of LLMs is to generate synthetic clinical reports that could alleviate the burden of annotating and collecting real-world data in training AI models. Meanwhile, there could be concerns and limitations in using commercial LLMs to handle sensitive clinical data.

Standardizing MRI orientation improves reliability of entorhinal and transentorhinal cortical volume measurement.

The current study compared the reliability of manual collateral sulcus depth and entorhinal and transentorhinal cortical volume measurements between native oriented MRI scans versus MRI scans realigned to the hippocampal long axis. Data included 10 participants with two serial 3.0T MRI scans from the Alzheimer's Disease Neuroimaging Initiative.

An Artificial Intelligent System for Prostate Cancer Diagnosis in Whole Slide Images.

In recent years a significant demand to develop computer-assisted diagnostic tools to assess prostate cancer using whole slide images has been observed. In this study we develop and validate a machine learning system for cancer assessment, inclusive of detection of perineural invasion and measurement of cancer portion to meet clinical reporting needs. The system analyses the whole slide image in three consecutive stages: tissue detection, classification, and slide level analysis.

Evolutionary Insights from Association Rule Mining of Co-Occurring Mutations in Influenza Hemagglutinin and Neuraminidase.

Seasonal influenza viruses continuously evolve via antigenic drift. This leads to recurring epidemics, globally significant mortality rates, and the need for annually updated vaccines. Co-occurring mutations in hemagglutinin (HA) and neuraminidase (NA) are suggested to have synergistic interactions where mutations can increase the chances of immune escape and viral fitness.

Autoantibodies to BACE1 promote Aβ accumulation and neurodegeneration in Alzheimer's disease.

The profile of autoantibodies is dysregulated in patients with Alzheimer's disease (AD). Autoantibodies to beta-site amyloid precursor protein (APP)-cleaving enzyme 1 (BACE1) are present in human blood. This study aims to investigate the clinical relevance and pathophysiological roles of autoantibodies to BACE1 in AD.