Objective: To assess costs and effectiveness of deep brain stimulation (DBS) of the internal globus pallidum (GPi) versus subthalamic nucleus (STN) from the provider and societal perspectives for Parkinson's disease (PD) patients in a multicenter randomized trial.
Methods: All costs from randomization to 36 months were included. Costs were from Department of Veterans Affairs (VA) and Medicare databases and clinical trial data. Quality adjusted life years (QALYs) were from Quality of Well Being questionnaires.
Results: Provider costs were similar for the 144 GPi and 130 STN patients (GPi: $138,044 vs. STN: $131,822; difference = $6,222, 95% confidence interval [CI]: -$42,125 to $45,343). Societal costs were also similar (GPi: $171,061 vs. STN: $167,706; difference = $3,356, 95% CI: -$57,371 to $60,294). The GPi patients had nonsignificantly more QALYs.
Conclusions: The QALYs and costs were similar; the level of uncertainty given the sample size suggests that these factors should not direct treatment or resource allocation decisions in selecting or making available either procedure for eligible PD patients.
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http://dx.doi.org/10.1002/mds.26029 | DOI Listing |
Neural Netw
January 2025
Tsinghua University, Beijing, China. Electronic address:
Artificial neural networks (ANNs) can help camera-based remote photoplethysmography (rPPG) in measuring cardiac activity and physiological signals from facial videos, such as pulse wave, heart rate and respiration rate with better accuracy. However, most existing ANN-based methods require substantial computing resources, which poses challenges for effective deployment on mobile devices. Spiking neural networks (SNNs), on the other hand, hold immense potential for energy-efficient deep learning owing to their binary and event-driven architecture.
View Article and Find Full Text PDFHeliyon
January 2025
BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
Deformable image registration is a cornerstone of many medical image analysis applications, particularly in the context of fetal brain magnetic resonance imaging (MRI), where precise registration is essential for studying the rapidly evolving fetal brain during pregnancy and potentially identifying neurodevelopmental abnormalities. While deep learning has become the leading approach for medical image registration, traditional convolutional neural networks (CNNs) often fall short in capturing fine image details due to their bias toward low spatial frequencies. To address this challenge, we introduce a deep learning registration framework comprising multiple cascaded convolutional networks.
View Article and Find Full Text PDFHeliyon
July 2024
College of Information Science and Engineering, Northeastern University, Shenyang 110819, China.
Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive neuroimaging technique widely utilized in the research of Autism Spectrum Disorder (ASD), providing preliminary insights into the potential biological mechanisms underlying ASD. Deep learning techniques have demonstrated significant potential in the analysis of rs-fMRI. However, accurately distinguishing between healthy control group and ASD has been a longstanding challenge.
View Article and Find Full Text PDFCogn Neurodyn
December 2025
Department of Computational Intelligence, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Tamilnadu India.
Autism spectrum disorder (ASD) is one of the complicated neurodevelopmental disorders that impacts the daily functioning and social interactions of individuals. It includes diverse symptoms and severity levels, making it challenging to diagnose and treat efficiently. Various deep learning (DL) based methods have been developed for diagnosing ASD, which rely heavily on behavioral assessment.
View Article and Find Full Text PDFClin Neuropsychol
January 2025
Department of Internal Medicine (Pulmonary, Critical Care, and Sleep Medicine Division), University of South Florida, Tampa, FL, USA.
Obstructive sleep apnea (OSA) has been associated with structural and functional brain changes and cognitive impairment in sleep clinic samples. Persons with traumatic brain injury (TBI) are at increased risk of OSA compared to community samples, and many experience chronic cognitive disability. However, the impact of OSA on cognitive outcome after TBI is unknown.
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