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Assessing the Clinical and Functional Status of COPD Patients Using Speech Analysis During and After Exacerbation.
Int J Chron Obstruct Pulmon Dis
January 2025
Department of Cardiology, Respiratory Medicine and Intensive Care, University Hospital Augsburg, Augsburg, Germany.
Background: Chronic obstructive pulmonary disease (COPD) affects breathing, speech production, and coughing. We evaluated a machine learning analysis of speech for classifying the disease severity of COPD.
Methods: In this single centre study, non-consecutive COPD patients were prospectively recruited for comparing their speech characteristics during and after an acute COPD exacerbation.
The use of an automatic remote weight management system to track treatment response, identified drugs supply shortage and its consequences: A pilot study.
Digit Health
January 2025
Ohad Cohen Endocrinology, Tel Hashomer, Israel.
Objective: The objective of this pilot study is to evaluate the feasibility of using an automatic weight management system to follow patients' response to weight reduction medications and to identify early deviations from weight trajectories.
Methods: The pilot study involved 11 participants using Semaglutide for weight management, monitored over a 12-month period. A cloud-based, Wi-Fi-enabled remote weight management system collected and analyzed daily weight data from smart scales.
Remote management of anaemia in patients with end-stage kidney disease using a wearable, non-invasive sensor.
Clin Kidney J
January 2025
Alio, Inc., Broomfield, CO, USA.
Anaemia is a prevalent complication in patients with end-stage kidney disease (ESKD) undergoing haemodialysis. This study evaluates the accuracy of the Alio SmartPatch™, a non-invasive remote monitoring device, in measuring haemoglobin (Hb) and haematocrit (Hct) levels in haemodialysis patients by comparing its results with standard blood-based laboratory methods. The results from 116 patients across multiple sites in the USA and the Kingdom of Jordan show that SmartPatch measurements align closely with standard blood-based laboratory methods, meeting clinically acceptable limits of agreement.
View Article and Find Full Text PDFHarnessing Spectral Libraries From AVIRIS-NG Data for Precise PFT Classification: A Deep Learning Approach.
Plant Cell Environ
January 2025
Remote Sensing Laboratory, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, India.
The generation of spectral libraries using hyperspectral data allows for the capture of detailed spectral signatures, uncovering subtle variations in plant physiology, biochemistry, and growth stages, marking a significant advancement over traditional land cover classification methods. These spectral libraries enable improved forest classification accuracy and more precise differentiation of plant species and plant functional types (PFTs), thereby establishing hyperspectral sensing as a critical tool for PFT classification. This study aims to advance the classification and monitoring of PFTs in Shoolpaneshwar wildlife sanctuary, Gujarat, India using Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and machine learning techniques.
View Article and Find Full Text PDFRADAR-AD: assessment of multiple remote monitoring technologies for early detection of Alzheimer's disease.
Alzheimers Res Ther
January 2025
Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Sankt Augustin, Germany.
Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder affecting millions worldwide, leading to cognitive and functional decline. Early detection and intervention are crucial for enhancing the quality of life of patients and their families. Remote Monitoring Technologies (RMTs) offer a promising solution for early detection by tracking changes in behavioral and cognitive functions, such as memory, language, and problem-solving skills.
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