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Transformers for Neuroimage Segmentation: Scoping Review.
J Med Internet Res
January 2025
Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain, United Arab Emirates.
Background: Neuroimaging segmentation is increasingly important for diagnosing and planning treatments for neurological diseases. Manual segmentation is time-consuming, apart from being prone to human error and variability. Transformers are a promising deep learning approach for automated medical image segmentation.
View Article and Find Full Text PDFExploring the Credibility of Large Language Models for Mental Health Support: Protocol for a Scoping Review.
JMIR Res Protoc
January 2025
Data and Web Science Group, School of Business Informatics and Mathematics, University of Manneim, Mannheim, Germany.
Background: The rapid evolution of large language models (LLMs), such as Bidirectional Encoder Representations from Transformers (BERT; Google) and GPT (OpenAI), has introduced significant advancements in natural language processing. These models are increasingly integrated into various applications, including mental health support. However, the credibility of LLMs in providing reliable and explainable mental health information and support remains underexplored.
View Article and Find Full Text PDFEstimating the economic consequences of leveraging the novel cobas® HIV-1/HIV-2 Qualitative NAT Test in routine HIV testing.
Clin Infect Dis
January 2025
IQVIA Inc., Falls Church, VA.
Article Synopsis
- The study compared the costs of the current CDC 3-step HIV testing algorithm with a new single-test alternative (cobas) for efficiency in diagnosing HIV.
- A decision-tree model estimated costs and testing needs for 1 million people, revealing significant reductions in total tests and retests required when using the alternative method.
- Findings indicate that the new algorithm could simplify HIV testing processes, cut overall testing numbers, and keep healthcare costs stable, thereby improving patient outcomes.
Retinopathy of prematurity: Accuracy of ROPScore and WINROP algorithms in a Brazilian population.
Arq Bras Oftalmol
January 2025
Department of Ophthalmology and Visual Sciences, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, SP, Brazil.
Purpose: To assess the sensitivity and specificity of the retinopathy of prematurity score (ROPScore) and weight, insulin-like growth factor-1, retinopathy of prematurity algorithm in predicting the risk of developing severe retinopathy of prematurity (prethreshold type 1) in a sample of preterm infants in Brazil.
Methods: Retrospective analysis of medical records of preterm infants (n=288) with birth weight of ≤1500 g and/or gestational age of 23-32 weeks in a neonatal unit in Southern Brazil from May 2013 to December 2020 (92 months).
Results: The incidence of confirmed severe retinopathy of prematurity was 6.
Inferring the genetic relationships between unsupervised deep learning-derived imaging phenotypes and glioblastoma through multi-omics approaches.
Brief Bioinform
November 2024
Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China.
This study aimed to investigate the genetic association between glioblastoma (GBM) and unsupervised deep learning-derived imaging phenotypes (UDIPs). We employed a combination of genome-wide association study (GWAS) data, single-nucleus RNA sequencing (snRNA-seq), and scPagwas (pathway-based polygenic regression framework) methods to explore the genetic links between UDIPs and GBM. Two-sample Mendelian randomization analyses were conducted to identify causal relationships between UDIPs and GBM.
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