Wearable continuous glucose monitoring sensors: A revolution in diabetes treatment

Worldwide, the number of people affected by diabetes is rapidly increasing due to aging populations and sedentary lifestyles, with the prospect of exceeding 500 million cases in 2030, resulting in one of the most challenging socio-health emergencies of the third millennium. Daily management of diabetes by patients relies on the capability of correctly measuring glucose concentration levels in the blood by using suitable sensors. In recent years, glucose monitoring has been revolutionized by the development of Continuous Glucose Monitoring (CGM) sensors, wearable non/minimally-invasive devices that measure glucose concentration by exploiting different physical principles, e.g., glucose-oxidase, fluorescence, or skin dielectric properties, and provide real-time measurements every 1–5 min. CGM opened new challenges in different disciplines, e.g., medicine, physics, electronics, chemistry, ergonomics, data/signal processing, and software development to mention but a few. This paper first makes an overview of wearable CGM sensor technologies, covering both commercial devices and research prototypes. Then, the role of CGM in the actual evolution of decision support systems for diabetes therapy is discussed. Finally, the paper presents new possible horizons for wearable CGM sensor applications and perspectives in terms of big data analytics for personalized and proactive medicine

Deriving statistical inference from the application of artificial neural networks to clinical metabolomics data

Metabolomics data are complex with a high degree of multicollinearity. As such, multivariate linear projection methods, such as partial least squares discriminant analysis (PLS-DA) have become standard. Non-linear projections methods, typified by Artificial Neural Networks (ANNs) may be more appropriate to model potential nonlinear latent covariance; however, they are not widely used due to difficulty in deriving statistical inference, and thus biological interpretation. Herein, we illustrate the utility of ANNs for clinical metabolomics using publicly available data sets and develop an open framework for deriving and visualising statistical inference from ANNs equivalent to standard PLS-DA methods

Automated Cardiac Resting Phase Detection Targeted on the Right Coronary Artery

Static cardiac imaging such as late gadolinium enhancement, mapping, or 3-D coronary angiography require prior information, e.g., the phase during a cardiac cycle with least motion, called resting phase (RP). The purpose of this work is to propose a fully automated framework that allows the detection of the right coronary artery (RCA) RP within CINE series. The proposed prototype system consists of three main steps. First, the localization of the regions of interest (ROI) is performed. Second, the cropped ROI series are taken for tracking motions over all time points. Third, the output motion values are used to classify RPs. In this work, we focused on the detection of the area with the outer edge of the cross-section of the RCA as our target. The proposed framework was evaluated on 102 clinically acquired dataset at 1.5T and 3T. The automatically classified RPs were compared with the reference RPs annotated manually by a expert for testing the robustness and feasibility of the framework. The predicted RCA RPs showed high agreement with the experts annotated RPs with 92.7% accuracy, 90.5% sensitivity and 95.0% specificity for the unseen study dataset. The mean absolute difference of the start and end RP was 13.6 $\pm$ 18.6 ms for the validation study dataset (n=102). In this work, automated RP detection has been introduced by the proposed framework and demonstrated feasibility, robustness, and applicability for static imaging acquisitions.Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2023:00

ECGDT: a graphical software tool for ECG diagnosis

While cardiovascular diseases are the leading causes of death in developed countries, detection of cardiac abnormalities can reduce mortality rates, through early and accurate diagnosis. One of the main assets used to help in the diagnosis process is the electrocardiogram (ECG). A free software tool for electrocardiogram analysis and diagnosis is presented. The tool, named ECGDT, allows: (1) to detect beats present on the ECG, both in single and multi-channel levels, (2) to identify beat waves, and (3) to diagnose different cardiac abnormalities. System evaluation was performed in two ways: (1) diagnostic capabilities were tested with Receiver Operating Characteristic (ROC) analysis, and (2) Graphical Software Interface (GUI) aspects, such as attraction, efficiency, or novelty, were evaluated employing User Experience Questionnaire (UEQ) scores. For disease diagnosis, the mean Area Under the ROC Curve (AUC) was 0.821. The system was also capable of detecting 100% of several cardiac abnormalities, such as bradycardia or tachycardia. Related to the GUI, all usability estimators scored values ranged between 2.208 and 2.750 (overall positive evaluations are obtained for values over 0.8). ECGDT could serve as an aid in the diagnosis of different medical abnormalities. In addition, the suitability of the developed interface has been proven.Xunta de Galicia | Ref. ED431B 2017/86Universidade de Vigo/CISU

Fruktsamhet före och efter installation av Herd Navigator

The fertility of dairy cows is of great importance in order to maintain high production. The decline in fertility of dairy cows the latest decades can have several explanations. The large emphasis on high yielding cows and the negative genetic correlation between milk production and fertility traits could be one of the main factors for impaired fertility. To find heats and inseminate the cow at the right time is crucial to receive high conception rate. Delaval has, in corporation with FOSS, developed Herd Navigator which is a management program that measure four biological parameters in the milk; progesterone, betahydroxybutyrate, lactate dehydrogenase and urea. In this study the focus will be on progesterone which is a hormone produced by the corpus luteum. The concentration of this hormone indicates where the cow is in her estrus cycle. The study was performed on three Swedish and three Dutch farms where the parameters for fertility were compared one year before and one year after the installation of Herd Navigator. Calving interval, number of inseminations per pregnancy, conception rate, open days and days from calving to first insemination were analyzed. Unfortunately several inseminations were found to be missing in the data set before the installation of Herd Navigator. The fertility measures based on number of inseminations were therefore not reliable; however calving dates seemed to be complete reported. No significant differences in open days and calving interval before and after the installation of Herd Navigator could be found in this study. However, a significant reduction in number of days from calving to first insemination could be seen. There was also a tendency of a shorter calving interval on the Swedish farms after the installation. To get additional information of the practical experiences of Herd Navigator an interview with the farmers was made. They found that the measurements of progesterone were very helpful and time saving in order to find heats. It would be interesting to implement further and more comprehensive studies on a larger data set that include all inseminations. Moreover, it would be interesting to see what impact continuous measurements of progesterone will have on the reproduction performance.Fruktsamheten hos mjölkkor är av stor betydelse för att upprätthålla hög mjölkproduktion. De senaste årtiondena har fruktsamheten inom mjölkproduktion försämrats vilket kan ha flera förklaringar. Alltför stor vikt på avkastning i avelsmålet och den negativa genetiska korrelationen med fruktsamhet kan nämnas som en bidragande orsak. Att hitta brunster och seminera vid rätt tidpunkt är avgörande för att få korna dräktiga. Delaval har i samarbete med FOSS utvecklat Herd Navigator som är ett managementprogram vars uppgift är att underlätta skötseln av korna för lantbrukaren. Fyra biologiska parametrar mäts i mjölken; progesteron, betahydroxybuturat, laktatdehydrogenas och urea. I denna studie ligger fokuset på hormonet progesteron som produceras av gulkroppen och vars nivåer kan ge en indikation om var kon befinner sig i brunstcykeln. Studien genomfördes på basis av data på tre svenska och tre holländska gårdar där fruktsamhetsparametrar jämfördes ett år innan installationen och ett år efter installationen av Herd Navigator. Kalvningsintervall, antal insemineringar per dräktighet, dräktighetsprocent, tomdagar och antal dagar från kalvning till första inseminering var de parametrar som analyserades. Då information om en stor andel insemineringar saknades i datamaterialet innan installationen av Herd Navigator, kan analyserna av de fruktsamhetsmått som baseras på antalet inseminationer inte bedömas som tillförlitliga. Inga signifikanta skillnader i tomdagar eller kalvningsintervall kunde påvisas före och efter installation av Herd Navigator. Däremot kunde en signifikant reduktion i antalet dagar från kalvning till första inseminering ses. Dessutom kunde en tendens till kortare kalvningsinterval noteras hos de Svenska gårdarna efter att Herd Navigator installerades. I samband med studien gjordes också en intervju med de lantbrukarna som har programmet installerat. De uppgav att mätningarna av progesteron var till stor hjälp för att hitta brunster och att det sparade dem mycket tid. Det vore intressant att genomföra vidare studier på ett större datamaterial där alla insemineringar fanns rapporterade för att se hur fruktsamhetsmåtten påverkas av kontinuerliga progesteronmätningar

Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal

Investigation of advanced navigation and guidance system concepts for all-weather rotorcraft operations

Results are presented of a survey conducted of active helicopter operators to determine the extent to which they wish to operate in IMC conditions, the visibility limits under which they would operate, the revenue benefits to be gained, and the percent of aircraft cost they would pay for such increased capability. Candidate systems were examined for capability to meet the requirements of a mission model constructed to represent the modes of flight normally encountered in low visibility conditions. Recommendations are made for development of high resolution radar, simulation of the control display system for steep approaches, and for development of an obstacle sensing system for detecting wires. A cost feasibility analysis is included

Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal