BREATHE SMART- Breathlessness Rapid Evaluation And THErapy- Screening, Management And IntegRated Technology: Uptake of the self-screening for dyspnoea using the BREATHE SMART tool in adults attending GP clinics

Start Date03 Feb 2025

Sponsor / Collaborator

The University of Notre Dame Australia

ACTRN12624001242516

/ Not yet recruitingNot ApplicableIIT

Practice Nurses to Augment the Clinical Evaluation and cAre of people at high-risk of Heart Failure (PANACEA-HF) Trial: A multicentre. pragmatic health care surveillance and nested intervention trial

Start Date01 Nov 2024

Sponsor / Collaborator

The University of Notre Dame Australia

ACTRN12624000697583

/ RecruitingNot ApplicableIIT

The Canberra Family Study: A prospective study of new parents following the birth of their child to improve maternity service and family healthcare outcomes.

Start Date28 Oct 2024

Sponsor / Collaborator

Canberra Hospital Foundation

[+1]

100 Clinical Results associated with The University of Notre Dame Australia

0 Patents (Medical) associated with The University of Notre Dame Australia

5,555

Literatures (Medical) associated with The University of Notre Dame Australia

01 Jul 2025·Journal of Orthopaedics

The effect of adductor canal block on outcomes of total knee arthroplasty: A single centre, historical cohort study

Article

Author: Nguyen, Luc ; Olesnicky, Ben ; Roe, Justin P ; Elhage, Tania ; Lyons, Matthew C ; Salmon, Lucy J

BackgroundAdductor canal blocks (ACBs) have been associated with reduced pain following total knee arthroplasty (TKA). There is a paucity of evidence regarding whether these early differences impact longer term outcomes. This study aimed to identify whether using ACB in TKA was associated with improvements in both early and late outcomes.MethodsPatients who underwent a unilateral TKA between 2021 and 2022 were retrospectively assessed for pain scores, time to first mobilization and opioid use over the first 72 h. At 6 weeks, complications, pain scores and opioid use were assessed. At 12 months validated patient reported outcome measures (PROMs) and patient satisfaction with their surgery were assessed.Results262 unilateral TKA, of whom 129 received ACB (ACB group) and 133 did not (control group) were assessed. The ACB group had significantly lower median day 1 pain (median difference -0.44 (-0.09 to -0.79), p = 0.015). There was no significant difference between groups for pain after 24 h, time to mobilization or opioid use over 72 h. There was no significant difference in pain (p = 0.892), opioid use (p = 0.913) or complications (p = 0.348) at 6 weeks, or median change in PROMs (p = 0.436 and p = 0.307), opioid use (p = 0.187), or satisfaction with surgery (p = 0.262) at 12 months.ConclusionACBs were associated with a clinically insignificant difference in median pain on day 1. there was no association with pain after 24 h, opioid use, time to mobilization or longer term outcomes. Our findings do not support the use of routine ACB during TKA.

01 Mar 2025·The American Journal of Surgery

Artificial intelligence based assessment of minimally invasive surgical skills using standardised objective metrics – A narrative review

Review

Author: Wilson, M ; Preda, T. ; Wijeweera, C ; Preda, T ; Carney, T. ; Carney, T ; Ong, Z ; Preda, V ; Preda, V. ; Kankanamge, D ; Ong, Z. ; Wijeweera, C. ; Kankanamge, D. ; Wilson, M.

INTRODUCTIONMany studies display significant heterogeneity in the reliability of artificial intelligence (AI) assessment of minimally invasive surgical (MIS) skills. Our objective is to investigate whether AI systems utilising standardised objective metrics (SOMs) as the basis of skill assessment can provide a clearer understanding of the current state of such technology.METHODSWe systematically searched Medline, Embase, Scopus, CENTRAL and Web of Science from March 2023 to September 2023. Results were compiled as a narrative review.RESULTSTwenty-four citations were analysed. Overall accuracy of AI systems in predicting overall SOM score of a procedure ranged from 63 % to 100 %. The most frequently used SOM by AI algorithms were Objective Structured Assessment of Technical Skills (OSATS) (8/24) and Global Evaluative Assessment of Robotic Skills (GEARS) (8/24).CONCLUSIONSStratifying for AI studies which employed SOMs to assess surgical skill did not reduce heterogeneity of reported reliability. Our study identifies key issues within the current literature, which, once addressed, could allow more meaningful comparisons between studies.

01 Mar 2025·ASME Journal of Heat and Mass Transfer

Physics-Informed Bayesian Neural Networks for Solving Phonon Boltzmann Transport Equation in Forward and Inverse Problems With Sparse and Noisy Data

Author: Li, Ruiyang ; Zhou, Jiahang ; Wang, Jian-Xun ; Luo, Tengfei

AbstractNondiffusive phonon transport presents significant challenges in micro/nanoscale thermal characterization, compounded by the limitations of experimental-numerical techniques and the presence of measurement noise. Additionally, inverse modeling and uncertainty quantification (UQ) for submicron thermal transport remain under-explored. In this study, we introduce a physics-informed Bayesian deep learning framework designed to address phonon Boltzmann transport equation (BTE)-based forward and inverse problems leveraging limited and noisy data. Our approach combines Bayesian neural networks with a nonparametric variational inference method, formulating the BTE-constrained training in a Bayesian manner. This enables the estimation of the posterior distribution of neural network parameters and unknown equation parameters based on a likelihood function that incorporates uncertainties from both the measurement data and the BTE model. Through numerical experiments on various phonon transport scenarios, we demonstrate that our method can accurately reconstruct temperature and heat flux profiles, infer critical quantities of interest (e.g., Knudsen number), and provide robust uncertainty quantification, even when data is sparse and noisy. This framework enhances our capability to conduct nondiffusive thermal simulations and inverse modeling with quantified uncertainty, offering a powerful tool for advancing thermal transport research and optimization in micro/nanoscale devices.

100 Deals associated with The University of Notre Dame Australia

100 Translational Medicine associated with The University of Notre Dame Australia

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