Improved Three-Component Decomposition Technique for Forest Parameters Estimation from PolInSAR Image

Polarimetric SAR interferometry (PolInSAR) is an efficient remote sensing technique that allows to extract forest heights by means of model-based inversion. Recently, there have been plenty of researches on the retrieval of vegetation parameters by single frequency single baseline PolInSAR, such as the ESPRIT method and three-stage inversion method. However, these methods have several shortcomings which tend to underestimate the forest height due to attenuations of the electromagnetic waves in the ground medium. In order to overcome these shortcomings, an improved three-component decomposition technique using PolInSAR image is proposed in this paper. By means of coherence set and a Newton-Raphson method, the proposed method improves the accuracy of forest height estimation. The proposed algorithm performance is evaluated with simulated data from PolSARProSim software and L-band PolInSAR image pair of Tien-Shan test site which is acquired by the SIR-C/X-SAR system

Automated pupillometry and optic nerve sheath diameter ultrasound to define tuberculous meningitis disease severity and prognosis

Background: Tuberculous meningitis (TBM) causes high mortality and morbidity, in part due to raised intracranial pressure (ICP). Automated pupillometry (NPi) and optic nerve sheath diameter (ONSD) are both low-cost, easy-to-use and non-invasive techniques that correlate with ICP and neurological status. However, it is uncertain how to apply these techniques in the management of TBM. Methods: We conducted a pilot study enrolling 20 adults with TBM in the Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam. Our objective was to investigate the relationships between baseline and serial measurements of NPi and ONSD and disease severity and outcome. Serial NPi and ONSD were performed for 30 days, at discharge, and at 3-months, with measurements correlated with clinical progression and outcomes. Results: ONSD and NPi measurements had an inverse relationship. Higher ONSD and lower NPi values were associated with lower Glasgow coma score. Baseline NPi was a strong predictor 3-month outcome (median NPi 4.55, interquartile range 4.35–4.65 for good outcomes versus 2.60, IQR 0.65–3.95 for poor outcomes, p = 0.002). Pupil inequality (NPi ≥0.7) was also strongly associated with poor 3-month outcomes (p = 0.006). Individual participants' serial NPi and ONSD were variable during initial treatment and correlated with clinical condition and outcome. Conclusion: Pupillometry and ONSD may be used to predict clinical deterioration and outcome from TBM. Future, larger studies are need explore the optimal timing of measurements and to define how they might be used to optimise treatments and improve outcomes from TBM

Deployment and validation of an AI system for detecting abnormal chest radiographs in clinical settings

BackgroundThe purpose of this paper is to demonstrate a mechanism for deploying and validating an AI-based system for detecting abnormalities on chest X-ray scans at the Phu Tho General Hospital, Vietnam. We aim to investigate the performance of the system in real-world clinical settings and compare its effectiveness to the in-lab performance.MethodThe AI system was directly integrated into the Hospital's Picture Archiving and Communication System (PACS) after being trained on a fixed annotated dataset from other sources. The system's performance was prospectively measured by matching and comparing the AI results with the radiology reports of 6,285 chest X-ray examinations extracted from the Hospital Information System (HIS) over the last 2 months of 2020. The normal/abnormal status of a radiology report was determined by a set of rules and served as the ground truth.ResultsOur system achieves an F1 score—the harmonic average of the recall and the precision—of 0.653 (95% CI 0.635, 0.671) for detecting any abnormalities on chest X-rays. This corresponds to an accuracy of 79.6%, a sensitivity of 68.6%, and a specificity of 83.9%.ConclusionsComputer-Aided Diagnosis (CAD) systems for chest radiographs using artificial intelligence (AI) have recently shown great potential as a second opinion for radiologists. However, the performances of such systems were mostly evaluated on a fixed dataset in a retrospective manner and, thus, far from the real performances in clinical practice. Despite a significant drop from the in-lab performance, our result establishes a reasonable level of confidence in applying such a system in real-life situations

Farming systems in the sandy area of the Thua Thien Hue Province, Central Vietnam, survey of socio-economic situation and constraints identified by farmers

peer reviewe

Medication Adherence in Cardiovascular Diseases

Cardiovascular disease is a significant cause of death globally. While effective long-term medications that reduce the risk of morbidity and mortality related to cardiovascular disease are readily available, nonadherence to prescribed medications remains a significant reason for suboptimal management. Consequently, this might lead to increased morbidity and mortality and healthcare costs. Medication nonadherence causes are myriad and complicated, with factors at the patient, healthcare provider, and health system levels. Many clinical trials have investigated interventions to target these factors for improving medication adherence, including improving patient education, testing behavioral interventions, implementing medication reminder tools, reducing medication costs, utilizing social support, utilizing healthcare team members, and simplifying medication dosing regimens. This book chapter describes factors influencing medication adherence and highlights the impact of varying levels of adherence on patients’ clinical and economic outcomes. We also summarize interventions for improving medication adherence in cardiovascular disease