Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1097/QAD.0b013e32803277d9 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Virtual Reality-based Infrared Pupillometry (VIP) for long COVID.
Ophthalmology
December 2024
Department of Ophthalmology and Visual Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR; Department of Ophthalmology and Visual Sciences, The Prince of Wales Hospital, Hong Kong SAR; Hong Kong Eye Hospital, Hong Kong SAR; Eye Centre, The Chinese University of Hong Kong Medical Centre, Hong Kong SAR. Electronic address:
Objective: To evaluate the use of virtual reality-based infrared pupillometry (VIP) to detect individuals suffering long COVID.
Design: Prospective, case-control cross-sectional study.
Participants: Participants aged 20-60 were recruited from a community eye screening programme.
Background: Sleep staging is critical for diagnosing sleep disorders. Traditional methods in clinical settings involve time-intensive scoring procedures. Recent advancements in data-driven algorithms using photoplethysmogram (PPG) time series have shown promise in automating sleep staging in adults.
View Article and Find Full Text PDFPredicting invasion in early-stage ground-glass opacity pulmonary adenocarcinoma: a radiomics-based machine learning approach.
BMC Med Imaging
September 2024
The First Clinical School of Medicine, Guangdong Medical University, Zhanjiang, Guangdong, China.
Background: To design a pulmonary ground-glass nodules (GGN) classification method based on computed tomography (CT) radiomics and machine learning for prediction of invasion in early-stage ground-glass opacity (GGO) pulmonary adenocarcinoma.
Methods: This retrospective study included pulmonary GGN patients who were histologically confirmed to have adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma cancer (IAC) from 2020 to 2023. CT images of all patients were automatically segmented and 107 radiomic features were obtained for each patient.
Exploring the Interplay of Dataset Size and Imbalance on CNN Performance in Healthcare: Using X-rays to Identify COVID-19 Patients.
Diagnostics (Basel)
August 2024
Software Engineering Department, SCE-Shamoon College of Engineering, Ashdod 77245, Israel.
Introduction: Convolutional Neural Network (CNN) systems in healthcare are influenced by unbalanced datasets and varying sizes. This article delves into the impact of dataset size, class imbalance, and their interplay on CNN systems, focusing on the size of the training set versus imbalance-a unique perspective compared to the prevailing literature. Furthermore, it addresses scenarios with more than two classification groups, often overlooked but prevalent in practical settings.
View Article and Find Full Text PDFTracking Vigilance Fluctuations in Real-Time: A Sliding-Window HRV-based Machine-Learning Approach.
Sleep
August 2024
Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education; Center for Sleep Research, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health & Cognitive Science, School of Psychology, South China Normal University, Guangzhou, China.
Study Objectives: Heart rate variability (HRV)-based machine learning models hold promise for real-world vigilance evaluation, yet their real-time applicability is limited by lengthy feature extraction times and reliance on subjective benchmarks. This study aimed to improve the objectivity and efficiency of HRV-based vigilance evaluation by associating HRV and behavior metrics through a sliding-window approach.
Methods: Forty-four healthy adults underwent psychomotor vigilance tasks under both well-rested and sleep-deprived conditions, with simultaneous electrocardiogram recording.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!