Motion Artifact removal in Ambulatory ECG Signal using ICA

No Abstrac

Diagnosis of Pulmonary Nodules on CT Images Using YOLOv4

In this paper, the Scale-Invariant Feature Transform (SIFT) and Fast Library for Approximate Nearest Neighbors (FLANN) based algorithm is used to detect the abnormalities in the National Lung Screening Trial (NLST) CT scans as the exact clinical nodule locations are not provided in the dataset. These identified nodules on NLST CT Scans are then annotated using LabelImg tool. This process consumes time and so furthermore, the automatic nodule detection, You Only Look Once version 4 (YOLOv4) object detection model is implemented. The YOLOv4 object detection model is provided with total of 4187 labelled images in form of training (70%), validation (20%), and test (10%) datasets. Our YOLOv4 model achieves precision of 95%, sensitivity of 81% and mean Average Precision (mAP) of 89.1%

Abstracts of Scientifica 2022

This book contains the abstracts of the papers presented at Scientifica 2022, Organized by the Sancheti Institute College of Physiotherapy, Pune, Maharashtra, India, held on 12–13 March 2022. This conference helps bring researchers together across the globe on one platform to help benefit the young researchers. There were six invited talks from different fields of Physiotherapy and seven panel discussions including over thirty speakers across the globe which made the conference interesting due to the diversity of topics covered during the conference. Conference Title:  Scientifica 2022Conference Date: 12–13 March 2022Conference Location: Sancheti Institute College of PhysiotherapyConference Organizer: Sancheti Institute College of Physiotherapy, Pune, Maharashtra, Indi

Global Burden of Cardiovascular Diseases and Risks, 1990-2022

The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) is a multinational collaborative research study with >10,000 collaborators around the world. GBD generates a time series of summary measures of health, including prevalence, cause-specific mortality (CSMR), years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life years (DALYs) to provide a comprehensive view of health burden for a wide range of stakeholders including clinicians, public and private health systems, ministries of health, and other policymakers. These estimates are produced for 371 causes of death and 88 risk factors according to mutually exclusive, collectively exhaustive hierarchies of health conditions and risks. The study is led by a principal investigator and governed by a study protocol, with oversight from a Scientific Council, and an Independent Advisory Committee.1 GBD is performed in compliance with Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER).2 GBD uses de-identified data, and the waiver of informed consent was reviewed and approved by the University of Washington Institutional Review Board (study number 9060). This almanac presents results for 18 cardiovascular diseases (CVD) and the CVD burden attributed to 15 risk factors (including an aggregate grouping of dietary risks) by GBD region. A summary of methods follows. Additional information can be found online at https://ghdx.healthdata.org/record/ihme-data/cvd-1990-2022, including:Funding was provided by the Bill and Melinda Gates Foundation, and the American College of Cardiology Foundation. The authors have reported that they have no relationships relevant to the contents of this paper to disclose. The contents and views expressed in this report are those of the authors and do not necessarily reflect the official views of the National Institutes of Health, the Department of Health and Human Services, the U.S. Government, or the affiliated institutions