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Glaucoma detection and staging from visual field images using machine learning techniques.
PLoS One
January 2025
School of Optometry and Vision Science, UNSW Sydney, Sydney, New South Wales, Australia.
Purpose: In this study, we investigated the performance of deep learning (DL) models to differentiate between normal and glaucomatous visual fields (VFs) and classify glaucoma from early to the advanced stage to observe if the DL model can stage glaucoma as Mills criteria using only the pattern deviation (PD) plots. The DL model results were compared with a machine learning (ML) classifier trained on conventional VF parameters.
Methods: A total of 265 PD plots and 265 numerical datasets of Humphrey 24-2 VF images were collected from 119 normal and 146 glaucomatous eyes to train the DL models to classify the images into four groups: normal, early glaucoma, moderate glaucoma, and advanced glaucoma.
Large-scale and long-term wildlife research and monitoring using camera traps: a continental synthesis.
Biol Rev Camb Philos Soc
January 2025
Wildlife Observatory of Australia (WildObs), Queensland Cyber Infrastructure Foundation (QCIF), Brisbane, Queensland, 4072, Australia.
Camera traps are widely used in wildlife research and monitoring, so it is imperative to understand their strengths, limitations, and potential for increasing impact. We investigated a decade of use of wildlife cameras (2012-2022) with a case study on Australian terrestrial vertebrates using a multifaceted approach. We (i) synthesised information from a literature review; (ii) conducted an online questionnaire of 132 professionals; (iii) hosted an in-person workshop of 28 leading experts representing academia, non-governmental organisations (NGOs), and government; and (iv) mapped camera trap usage based on all sources.
View Article and Find Full Text PDFImproving detection of monogenic diabetes through reanalysis of GCK variants of uncertain significance.
Acta Diabetol
January 2025
Adelaide Medical School, University of Adelaide, Adelaide, Australia.
Aims: To assess the utility of reanalysing GCK variants of uncertain significance (VUS) as an intervention to improve the detection of monogenic diabetes.
Methods: We examined GCK VUS in a local cohort of individuals with suspected monogenic diabetes and re-curated each variant against the recent ClinGen GCK-specific variant classification guidelines.
Results: Variant reanalysis achieved a new 'likely pathogenic' classification (i.
An open source convolutional neural network to detect and localize distal radius fractures on plain radiographs.
Eur J Trauma Emerg Surg
January 2025
Trauma Research Unit, Department of Surgery, Erasmus MC, University Medical Centre Rotterdam, Rotterdam, the Netherlands.
Purpose: Distal radius fractures (DRFs) are often initially assessed by junior doctors under time constraints, with limited supervision, risking significant consequences if missed. Convolutional Neural Networks (CNNs) can aid in diagnosing fractures. This study aims to internally and externally validate an open source algorithm for the detection and localization of DRFs.
View Article and Find Full Text PDFRecommendations for mitochondria transfer and transplantation nomenclature and characterization.
Nat Metab
January 2025
Centre for Orthopaedic Research, Medical School of the University of Western Australia, Nedlands, Western Australia, Australia.
Intercellular mitochondria transfer is an evolutionarily conserved process in which one cell delivers some of their mitochondria to another cell in the absence of cell division. This process has diverse functions depending on the cell types involved and physiological or disease context. Although mitochondria transfer was first shown to provide metabolic support to acceptor cells, recent studies have revealed diverse functions of mitochondria transfer, including, but not limited to, the maintenance of mitochondria quality of the donor cell and the regulation of tissue homeostasis and remodelling.
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