PEPS: Polygenic Epistatic Phenotype Simulation.

Genetic data is limited and generating new datasets is often an expensive, time-consuming process, involving countless moving parts to genotype and phenotype individuals. While sharing data is beneficial for quality control and software development, privacy and security are of utmost importance. Generating synthetic data is a practical solution to mitigate the cost, time and sensitivities that hamper developers and researchers in producing and validating novel biotechnological solutions to data intensive problems.

Modelling Planned vs. Actual Start Time to Control the Efficiency of Surgery.

To control the efficiency of surgery, it is ideal to have actual starting times of surgical procedures coincide with their planned start time. This study analysed over 4 years of data from a large metropolitan hospital and identified factors associated with surgery commencing close to the planned starting time via statistical modelling. A web application comprising novel visualisations to complement the statistical analysis was developed to facilitate translational impact by providing theatre administrators and clinical staff with a tool to assist with continuous quality improvement.

Data Visualization of CRISPR-Cas9 Guide RNA Design Tools.

With the advancement of genomic engineering and genetic modification techniques, the uptake of computational tools to design guide RNA increased drastically. Searching for genomic targets to design guides with maximum on-target activity (efficiency) and minimum off-target activity (specificity) is now an essential part of genome editing experiments. Today, a variety of tools exist that allow the search of genomic targets and let users customize their search parameters to better suit their experiments.

Developing Robust Clinical Text Deep Learning Models - A "Painless" Approach.

The success of deep learning in natural language processing relies on ample labelled training data. However, models in the health domain often face data inadequacy due to the high cost and difficulty of acquiring training data. Developing such models thus requires robustness and performance on new data.

Dolores: A Mobile Chatbot for People Living with Chronic Pain.

We provide an outline of the Dolores chatbot designed to gather data and provide information to people living with chronic pain. Dolores is equipped with selective language levels to provide language appropriate responses for all ages. A recent pilot study (N = 60) of adolescents, young-adults and adults was completed and the frequented topics that were accessed are summarised here.

Informing Personalised Gamification Interventions Through a Novel Gamified Quiz.

Nonadherence to medical interventions and other advice leads to increased care costs and poorer health outcomes across a range of medical fields. An approach to increasing adherence is gamification. To maximize the benefits of gamification, a more structured and informed implementation is required.

Innovative Implemented Tools for Outpatient Clinic Scheduling.

Every year there are approximately three million new specialist clinic appointments at local hospital networks in Victoria. CSIRO, in collaboration with Austin Health, have developed two algorithms to assist with waitlist management in their outpatient specialist clinics. This study describes the implementation of these algorithms in software tools developed to support their use and trial in the clinical setting at Austin Health.

Towards Accurate Search for Neonatal Heartbeat: Weighted Algorithm for Reliable ECG Analysis of Premature Infants.

Accurate identification of the QRS complex is critical to analyse heart rate variability (HRV), which is linked to various adverse outcomes in premature infants. Reliable and accurate extraction of HRV characteristics at a large scale in the neonatal context remains a challenge. In this paper, we investigate the capabilities of 15 state-of-the-art QRS complex detection implementations using two real-world preterm neonatal datasets.

Automated Diabetic Retinopathy Diagnosis for Improved Clinical Decision Support.

We report on the prediction performance of artificial intelligence components embedded into a telehealth platform underlying a newly established eye screening service connecting metropolitan-based ophthalmologists to patients in remote indigenous communities in Northern Territory and Queensland. Two AI-based components embedded into the telehealth platform were evaluated on retinal images collected from 328 unique patients: an image quality alert system and a diabetic retinopathy detection system. Compared to ophthalmologists, at an individual image level, the image quality detection algorithm was correct 72% of the time, and 85% accurate at a patient level.

Automated anomaly-aware 3D segmentation of bones and cartilages in knee MR images from the Osteoarthritis Initiative.

In medical image analysis, automated segmentation of multi-component anatomical entities, with the possible presence of variable anomalies or pathologies, is a challenging task. In this work, we develop a multi-step approach using U-Net-based models to initially detect anomalies (bone marrow lesions, bone cysts) in the distal femur, proximal tibia and patella from 3D magnetic resonance (MR) images in individuals with varying grades of knee osteoarthritis. Subsequently, the extracted data are used for downstream tasks involving semantic segmentation of individual bone and cartilage volumes as well as bone anomalies.

Five dominant dimensions of brain aging are identified via deep learning: associations with clinical, lifestyle, and genetic measures.

Brain aging is a complex process influenced by various lifestyle, environmental, and genetic factors, as well as by age-related and often co-existing pathologies. MRI and, more recently, AI methods have been instrumental in understanding the neuroanatomical changes that occur during aging in large and diverse populations. However, the multiplicity and mutual overlap of both pathologic processes and affected brain regions make it difficult to precisely characterize the underlying neurodegenerative profile of an individual from an MRI scan.

Leveraging Unlabeled Clinical Data to Boost Performance of Risk Stratification Models for Suspected Acute Coronary Syndrome.

The performance of deep learning models in the health domain is desperately limited by the scarcity of labeled data, especially for specific clinical-domain tasks. Conversely, there are vastly available clinical unlabeled data waiting to be exploited to improve deep learning models where their training labeled data are limited. This paper investigates the use of task-specific unlabeled data to boost the performance of classification models for the risk stratification of suspected acute coronary syndrome.

From Free-text Drug Labels to Structured Medication Terminology with BERT and GPT.

We present a method to enrich controlled medication terminology from free-text drug labels. This is important because, while controlled medication terminology capture well-structured medication information, much of the information pertaining to medications is still found in free-text. First, we compared different Named Entity Recognition (NER) models including rule-based, feature-based, deep learning-based models with Transformers as well as ChatGPT, few-shot and fine-tuned GPT-3 to find the most suitable model that accurately extracts medication entities (ingredients, brand, dose, etc.

Genomic loci influence patterns of structural covariance in the human brain.

Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size.

A novel, multidomain, primary care nurse-led and mHealth-assisted intervention for dementia risk reduction in middle-aged adults (HAPPI MIND): study protocol for a cluster randomised controlled trial.

Introduction: Middle-aged multidomain risk reduction interventions targeting modifiable risk factors for dementia may delay or prevent a third of dementia cases in later life. We describe the protocol of a cluster randomised controlled trial (cRCT), HAPPI MIND (Holistic Approach in Primary care for PreventIng Memory Impairment aNd Dementia). HAPPI MIND will evaluate the efficacy of a multidomain, nurse-led, mHealth supported intervention for assessing dementia risk and reducing associated risk factors in middle-aged adults in the Australian primary care setting.

Accuracy of TrUE-Net in comparison to established white matter hyperintensity segmentation methods: An independent validation study.

White matter hyperintensities (WMH) are nearly ubiquitous in the aging brain, and their topography and overall burden are associated with cognitive decline. Given their numerosity, accurate methods to automatically segment WMH are needed. Recent developments, including the availability of challenge data sets and improved deep learning algorithms, have led to a new promising deep-learning based automated segmentation model called TrUE-Net, which has yet to undergo rigorous independent validation.

Computed tomography synthesis from magnetic resonance imaging using cycle Generative Adversarial Networks with multicenter learning.

Addressing the need for accurate dose calculation in MRI-only radiotherapy, the generation of synthetic Computed Tomography (sCT) from MRI has emerged. Deep learning (DL) techniques, have shown promising results in achieving high sCT accuracies. However, existing sCT synthesis methods are often center-specific, posing a challenge to their generalizability.

Improving preoperative prediction of surgery duration.

Background: Operating rooms (ORs) are one of the costliest units in a hospital, therefore the cumulative consequences of any kind of inefficiency in OR management lead to a significant loss of revenue for the hospital, staff dissatisfaction, and patient care disruption. One of the possible solutions to improving OR efficiency is knowing a reliable estimate of the duration of operations. The literature suggests that the current methods used in hospitals, e.

How Can We Use Mathematical Modeling of Amyloid-β in Alzheimer's Disease Research and Clinical Practices?

The accumulation of amyloid-β (Aβ) plaques in the brain is considered a hallmark of Alzheimer's disease (AD). Mathematical modeling, capable of predicting the motion and accumulation of Aβ, has obtained increasing interest as a potential alternative to aid the diagnosis of AD and predict disease prognosis. These mathematical models have provided insights into the pathogenesis and progression of AD that are difficult to obtain through experimental studies alone.

Physical activity and brain amyloid beta: A longitudinal analysis of cognitively unimpaired older adults.

Introduction: The current study evaluated the relationship between habitual physical activity (PA) levels and brain amyloid beta (Aβ) over 15 years in a cohort of cognitively unimpaired older adults.

Methods: PA and Aβ measures were collected over multiple timepoints from 731 cognitively unimpaired older adults participating in the Australian Imaging, Biomarkers and Lifestyle (AIBL) Study of Aging. Regression modeling examined cross-sectional and longitudinal relationships between PA and brain Aβ.

Introducing Quin: The Design and Development of a Prototype Chatbot to Support Smoking Cessation.

Introduction: Chatbots emulate human-like interactions and may usefully provide on-demand access to tailored smoking cessation support. We have developed a prototype smartphone application-based smoking cessation chatbot, named Quin, grounded in real-world, evidence-, and theory-based smoking cessation counseling sessions.

Methods: Conversation topics and interactions in Quitline counseling sessions (N = 30; 18 h) were characterized using thematic, content, and proponent analyses of transcripts.

Conversational artificial intelligence interventions to support smoking cessation: A systematic review and meta-analysis.

Background: Conversational artificial intelligence (chatbots and dialogue systems) is an emerging tool for tobacco cessation that has the potential to emulate personalised human support and increase engagement. We aimed to determine the effect of conversational artificial intelligence interventions with or without standard tobacco cessation interventions on tobacco cessation outcomes among adults who smoke, compared to no intervention, placebo intervention or an active comparator.

Methods: A comprehensive search of six databases was completed in June 2022.