Objective Identification and Characterisation of Pacific ITCZs in ERA5 and CMIP5 models and their representation in RCMs

<p>The Intertropical Convergence Zone (ITCZ) is recognised as the most crucial feature of the tropical climate producing more than 30% of the global precipitation. Its variability dramatically affects the people living in tropical areas. In the eastern Pacific, a pair of ITCZ, one at each side of the equator, during the boreal spring is evident. It is known as the Double Intertropical Convergence Zone (DITCZ). Generally, the ITCZ in the Pacific is located in the Northern Hemisphere (NH); however, during extreme El Niño events, it can cross the equator, or a wide band of deep convection extending over both hemispheres is to be observed. The DITCZ exists more frequently and with much more strength in General Circulation Models (GCMs), resulting in a spurious bias. The DITCZ bias has been a long-standing tropical bias in climate model simulations since the early beginning. Despite the intense research on the tropical climate and its features, fewer studies investigated the state of the ITCZs through an objective and automated algorithm. Also, much fewer studies have applied such an algorithm to the GCMs output. Unfortunately, far too little attention has been paid to examining how DITCZ bias is transmitted to Regional Climate Models (RCMs). Furthermore, the input variables to the RCM from GCM are prognostic such as wind, temperature and humidity. Since precipitation is not an input, it would be interesting to examine how the representation of ITCZs in the GCMs is unfolded in the RCMs. The method adopted in this study depends on an objective and automated algorithm developed and modified by earlier studies. The algorithm uses layer- and time-averaged winds in the lower troposphere (seven layers between 1000 and 850 hPa), in addition to wet-blub potential temperature, to automatically detect the centre latitude of the ITCZs. Furthermore, it uses GPCP or CMIP5 model precipitation to obtain the extents (i.e. boundaries) of the ITCZs and the precipitation intensities. From reanalysis datasets, the NH ITCZs are found near 8°N, while the Southern Hemisphere (SH) ITCZs are near 5°S. In CMIP5 models, the DITCZ is much stronger and more frequent, and it occurs year-round. Generally, the NH ITCZs are located between 8°N and 10°N while the SH ITCZs are located between 5°S and 10°S. Moreover, models overestimate the tropical precipitation mainly, the centre and full ITCZ intensities. Furthermore, it indicates more vigorous convection in the NH ITCZs than in the SH ITCZs. The study also found that the state of ITCZ in GCMs directly influences the bias in RCM monthly precipitation. However, it depends mainly on the RCM employed. The most affected nations by DITCZ bias are Ecuador and Peru. Quantitative in-depth analysis of precipitation of GCMs and RCMs is still <span>on</span>going.</p>

Cardiorespiratory Polygraphy for Detection of Obstructive Sleep Apnea in Patients With Atrial Fibrillation

Background: Obstructive sleep apnea (OSA) is a modifiable risk factor of atrial fibrillation (AF) but is underdiagnosed in these patients due to absence of good OSA screening pathways. Polysomnography (PSG) is the gold standard for diagnosing OSA but too resource-intensive as a screening tool. We explored whether cardiorespiratory polygraphy (PG) devices using an automated algorithm for Apnea-Hypopnea Index (AHI) determination can meet the requirements of a good screening tool in AF patients.Methods: This prospective study validated the performance of three PGs [ApneaLink Air (ALA), SOMNOtouch RESP (STR) and SpiderSAS (SpS)] in consecutive AF patients who were referred for PSG evaluation. Patients wore one of the three PGs simultaneously with PSG, and a different PG during each of three consecutive nights at home. Severity of OSA was classified according to the AHI during PSG (<5 = no OSA, 5–14 = mild, 15–30 = moderate, >30 = severe).Results: Of the 100 included AF patients, PSG diagnosed at least moderate in 69% and severe OSA in 33%. Successful PG execution at home was obtained in 79.1, 80.2 and 86.8% of patients with the ALA, STR and SpS, respectively. For the detection of clinically relevant OSA (AHI ≥ 15), an area under the curve of 0.802, 0.772 and 0.803 was calculated for the ALA, STR and SpS, respectively.Conclusions: This study indicates that home-worn PGs with an automated AHI algorithm can be used as OSA screening tools in AF patients. Based on an appropriate AHI cut-off value for each PG, the device can guide referral for definite PSG diagnosis.

Validation of computer-aided diagnosis of diabetic retinopathy from retinal photographs of diabetic patients from Telecamps

Objective: To validate an algorithm previously developed by the Healthcare Technology Innovation Centre, IIT Madras, India for screening of diabetic retinopathy (DR), in fundus images of diabetic patients from telecamps to examine the screening performance for DR. Design: Photographs of patients with diabetes were examined using the automated algorithm for the detection of DR Setting: Mobile Teleophthalmology camps were conducted in two districts in Tamil Nadu, India from Jan 2015 to May 2017. Participants: 939 eyes of 472 diabetic patients were examined at Mobile Teleophthalmology camps in Thiruvallur and Kanchipuram districts, Tamil Nadu, India,. Fundus images were obtained (40-45-degree posterior pole in each eye) for all patients using a nonmydriatic fundus camera by the fundus photographer. Main Outcome Measures: Fundus images were evaluated for presence or absence of DR using a computer-assisted algorithm, by an ophthalmologist at a tertiary eye care centre (reference standard) and by a fundus photographer. Results: The algorithm demonstrated 85% sensitivity and 80% specificity in detecting DR compared to ophthalmologist. The area under the receiver operating characteristic curve was 0.69 (95%CI=0.65 to 0.73). The algorithm identified 100% of vision-threatening retinopathy just like the ophthalmologist. When compared to the photographer, the algorithm demonstrated 81% sensitivity and 78% specificity. The sensitivity of the photographer to detect DR was found to be 86% and specificity was 99% in detecting DR compared to ophthalmologist. Conclusions: The algorithm can detect the presence or absence of DR in diabetic patients. All findings of vision-threatening retinopathy could be detected with reasonable accuracy and will help to reduce the workload for human graders in remote areas.

DataProVe: Fully Automated Conformance Verification Between Data Protection Policies and System Architectures

Privacy and data protection by design are relevant parts of the General Data Protection Regulation (GDPR), in which businesses and organisations are encouraged to implement measures at an early stage of the system design phase to fulfil data protection requirements. This paper addresses the policy and system architecture design and propose two variants of privacy policy language and architecture description language, respectively, for specifying and verifying data protection and privacy requirements. In addition, we develop a fully automated algorithm based on logic, for verifying three types of conformance relations (privacy, data protection, and functional conformance) between a policy and an architecture specified in our languages’ variants. Compared to related works, this approach supports a more systematic and fine-grained analysis of the privacy, data protection, and functional properties of a system. Our theoretical methods are then implemented as a software tool called DataProVe and its feasibility is demonstrated based on the centralised and decentralised approaches of COVID-19 contact tracing applications.

A study protocol for community use of digital auscultation to improve diagnosis of paediatric pneumonia in Bangladesh

The World Health Organisation Integrated Management of Childhood Illnesses (IMCI) algorithm relies on counting respiratory rate and observing respiratory distress to diagnose childhood pneumonia. IMCI performs with high sensitivity but low specificity, leading to over-diagnosis of child pneumonia and unnecessary antibiotic use. Including lung auscultation in IMCI could improve pneumonia diagnosis. Our objectives are: (i) assess lung sound recording quality by primary health care workers (HCWs) from under-five children with the Feelix Smart Stethoscope; and (ii) determine the reliability and performance of recorded lung sound interpretations by an automated algorithm compared to reference paediatrician interpretations. In a cross-sectional design, Community HCWs will record lung sounds of 1,003 under-five-year-old children with suspected pneumonia at first-level facilities in Zakiganj sub-district, Sylhet, Bangladesh. Enrolled children will be evaluated for pneumonia, including oxygen saturation, and have their lung sounds recorded by the Feelix Smart stethoscope at four sequential chest locations: two back and two front positions. A novel sound-filtering algorithm will be applied to recordings to address ambient noise and optimize recording quality. Recorded sounds will be assessed against a pre-defined quality threshold. A trained paediatric listening panel will classify recordings into one of the following categories: normal, crackle, wheeze, crackle and wheeze, or uninterpretable. All sound files will be classified into the same categories by the automated algorithm and compared with panel classifications. Lung auscultation and reliable interpretation of lung sounds of children are usually not feasible in first-level facilities in Bangladesh and other low- and middle-income countries (LMICs). Incorporating automated lung sound classification within the current IMCI pneumonia diagnostic algorithm may improve childhood pneumonia diagnostic accuracy at LMIC first-level facilities.

Diagnostic Accuracy and Generalizability of a Deep Learning-Based Fully Automated Algorithm for Coronary Artery Stenosis Detection on CCTA: A Multi-Centre Registry Study

Aims: In this retrospective, multi-center study, we aimed to estimate the diagnostic accuracy and generalizability of an established deep learning (DL)-based fully automated algorithm in detecting coronary stenosis on coronary computed tomography angiography (CCTA).Methods and results: A total of 527 patients (33.0% female, mean age: 62.2 ± 10.2 years) with suspected coronary artery disease (CAD) who underwent CCTA and invasive coronary angiography (ICA) were enrolled from 27 hospitals from January 2016 to August 2019. Using ICA as a standard reference, the diagnostic accuracy of the DL algorithm in the detection of ≥50% stenosis was compared to that of expert readers. In the vessel-based evaluation, the DL algorithm had a higher sensitivity (65.7%) and negative predictive value (NPV) (78.8%) and a significantly higher area under the curve (AUC) (0.83, p < 0.001). In the patient-based evaluation, the DL algorithm achieved a higher sensitivity (90.0%), NPV (52.2%) and AUC (0.81). Generalizability analysis of the DL algorithm was conducted by comparing its diagnostic performance in subgroups stratified by sex, age, geographic area and CT scanner type. The AUCs of the DL algorithm in the aforementioned subgroups ranged from 0.79 to 0.86 and from 0.75 to 0.93 in the vessel-based and patient-based evaluations, both without significant group differences (p > 0.05). The DL algorithm significantly reduced post-processing time (160 [IQR:139–192] seconds), in comparison to manual work (p < 0.001).Conclusions: The DL algorithm performed no inferior to expert readers in CAD diagnosis on CCTA and had good generalizability and time efficiency.

Wearable Reduced-Channel EEG System for Remote Seizure Monitoring

Epitel has developed Epilog, a miniature, wireless, wearable electroencephalography (EEG) sensor. Four Epilog sensors are combined as part of Epitel's Remote EEG Monitoring platform (REMI) to create 10 channels of EEG for remote patient monitoring. REMI is designed to provide comprehensive spatial EEG recordings that can be administered by non-specialized medical personnel in any medical center. The purpose of this study was to determine how accurate epileptologists are at remotely reviewing Epilog sensor EEG in the 10-channel “REMI montage,” with and without seizure detection support software. Three board certified epileptologists reviewed the REMI montage from 20 subjects who wore four Epilog sensors for up to 5 days alongside traditional video-EEG in the EMU, 10 of whom experienced a total of 24 focal-onset electrographic seizures and 10 of whom experienced no seizures or epileptiform activity. Epileptologists randomly reviewed the same datasets with and without clinical decision support annotations from an automated seizure detection algorithm tuned to be highly sensitive. Blinded consensus review of unannotated Epilog EEG in the REMI montage detected people who were experiencing electrographic seizure activity with 90% sensitivity and 90% specificity. Consensus detection of individual focal onset seizures resulted in a mean sensitivity of 61%, precision of 80%, and false detection rate (FDR) of 0.002 false positives per hour (FP/h) of data. With algorithm seizure detection annotations, the consensus review mean sensitivity improved to 68% with a slight increase in FDR (0.005 FP/h). As seizure detection software, the automated algorithm detected people who were experiencing electrographic seizure activity with 100% sensitivity and 70% specificity, and detected individual focal onset seizures with a mean sensitivity of 90% and mean false alarm rate of 0.087 FP/h. This is the first study showing epileptologists' ability to blindly review EEG from four Epilog sensors in the REMI montage, and the results demonstrate the clinical potential to accurately identify patients experiencing electrographic seizures. Additionally, the automated algorithm shows promise as clinical decision support software to detect discrete electrographic seizures in individual records as accurately as FDA-cleared predicates.