Background: Remote pregnancy monitoring is one of the most promising applications of telemedicine; however, the diagnostic value of self-examination using mobile cardiotocography (CTG) devices and remote analysis of the subsequent results has never been properly studied.
Objectives: The study aimed to compare the diagnostic usefulness of CTG self-examination using a mobile device to examination performed by a medical professional using a stationary device; and to evaluate the quality of CTG analysis performed remotely.
Material And Methods: Eighty-two pairs of CTG recordings were collected; each pair consisted of a single recording from an examination performed by a midwife using a stationary device, and another recording from an unassisted patient self-examination using a mobile device. Recordings were performed with a maximum time interval of 30 min. Each recording was analyzed twice. Primary analysis included a comparison of the assisted examination evaluated on-site vs the self-examination evaluated remotely in pairs. Secondary analysis was conducted by an independent expert who evaluated the unpaired recordings. Baseline fetal heart rate (BFHR) values were compared independently.
Results: We found that patients were more likely to perform inconclusive recordings than experienced midwives; however, the self-examination feasibility was satisfactory. The primary analysis showed 88.4% agreement of the recorded pairs; 11.6% of inconsistent pairs were due to inter-observer variability or medical reasons. The independent expert's analysis showed 97.1% agreement between the assisted and unassisted examinations. Paired t-test for BFHR values showed a statistically significant but clinically negligible mean difference between the 2 devices at 1.75 bpm.
Conclusions: The CTG examinations performed using mobile devices present satisfactory feasibility and equivalent diagnostic value compared to conventional devices, while the remote evaluation of recordings is as reliable as on-site analysis. Remote pregnancy surveillance is safe, effective and may be implemented into everyday obstetric care.
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http://dx.doi.org/10.17219/acem/111812 | DOI Listing |
Sensors (Basel)
January 2025
Department of Automation, "Dunarea de Jos" University of Galati, 800008 Galati, Romania.
This paper deals with a "digital twin" (DT) approach for processing, reprocessing, and scrapping (P/R/S) technology running on a modular production system (MPS) assisted by a mobile cyber-physical robotic system (MCPRS). The main hardware architecture consists of four line-shaped workstations (WSs), a wheeled mobile robot (WMR) equipped with a robotic manipulator (RM) and a mobile visual servoing system (MVSS) mounted on the end effector. The system architecture integrates a hierarchical control system where each of the four WSs, in the MPS, is controlled by a Programable Logic Controller (PLC), all connected via Profibus DP to a central PLC.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Information Engineering, University of Padova, 35122 Padova, Italy.
Sleep posture is a key factor in assessing sleep quality, especially for individuals with Obstructive Sleep Apnea (OSA), where the sleeping position directly affects breathing patterns: the side position alleviates symptoms, while the supine position exacerbates them. Accurate detection of sleep posture is essential in assessing and improving sleep quality. Automatic sleep posture detection systems, both wearable and non-wearable, have been developed to assess sleep quality.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Environmental Remote Sensing and Geoinformatics, Trier University, Universitätsring 15, 54296 Trier, Germany.
Assessing vines' vigour is essential for vineyard management and automatization of viticulture machines, including shaking adjustments of berry harvesters during grape harvest or leaf pruning applications. To address these problems, based on a standardized growth class assessment, labeled ground truth data of precisely located grapevines were predicted with specifically selected Machine Learning (ML) classifiers (Random Forest Classifier (RFC), Support Vector Machines (SVM)), utilizing multispectral UAV (Unmanned Aerial Vehicle) sensor data. The input features for ML model training comprise spectral, structural, and texture feature types generated from multispectral orthomosaics (spectral features), Digital Terrain and Surface Models (DTM/DSM- structural features), and Gray-Level Co-occurrence Matrix (GLCM) calculations (texture features).
View Article and Find Full Text PDFOral Radiol
January 2025
Division of Dental Radiology, Department of Reconstructive Oral and Maxillofacial Surgery, School of Dentistry, Iwate Medical University, 19-1 Uchimaru, Morioka, Iwate, 020-8508, Japan.
Objectives: This study aims to compare the image processing times of dental cone beam CT (CBCT) images using a remote medical image processing workstation (RW) versus on-premises image processing (OP) and assess its impact on workflow efficiency.
Methods: Data from 100 CBCT cases were randomly selected and processed using the OP3D VISION 17-19DX (EH Japan Co., Ltd.
New Phytol
January 2025
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91011, USA.
A new proliferation of optical instruments that can be attached to towers over or within ecosystems, or 'proximal' remote sensing, enables a comprehensive characterization of terrestrial ecosystem structure, function, and fluxes of energy, water, and carbon. Proximal remote sensing can bridge the gap between individual plants, site-level eddy-covariance fluxes, and airborne and spaceborne remote sensing by providing continuous data at a high-spatiotemporal resolution. Here, we review recent advances in proximal remote sensing for improving our mechanistic understanding of plant and ecosystem processes, model development, and validation of current and upcoming satellite missions.
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