Literature DB >> 29184897

Deep learning for cardiac computer-aided diagnosis: benefits, issues & solutions.

Brian C S Loh1, Patrick H H Then1.   

Abstract

Cardiovascular diseases are one of the top causes of deaths worldwide. In developing nations and rural areas, difficulties with diagnosis and treatment are made worse due to the deficiency of healthcare facilities. A viable solution to this issue is telemedicine, which involves delivering health care and sharing medical knowledge at a distance. Additionally, mHealth, the utilization of mobile devices for medical care, has also proven to be a feasible choice. The integration of telemedicine, mHealth and computer-aided diagnosis systems with the fields of machine and deep learning has enabled the creation of effective services that are adaptable to a multitude of scenarios. The objective of this review is to provide an overview of heart disease diagnosis and management, especially within the context of rural healthcare, as well as discuss the benefits, issues and solutions of implementing deep learning algorithms to improve the efficacy of relevant medical applications.

Entities:  

Keywords:  Heart disease; computer-aided diagnosis; deep learning; mHealth; machine learning; rural healthcare; telemedicine

Year:  2017        PMID: 29184897      PMCID: PMC5682365          DOI: 10.21037/mhealth.2017.09.01

Source DB:  PubMed          Journal:  Mhealth        ISSN: 2306-9740


  43 in total

1.  Real-time video streaming of sonographic clips using domestic internet networks and free videoconferencing software.

Authors:  Andrew S Liteplo; Vicki E Noble; Ben H C Attwood
Journal:  J Ultrasound Med       Date:  2011-11       Impact factor: 2.153

2.  Primary care: current problems and proposed solutions.

Authors:  Thomas Bodenheimer; Hoangmai H Pham
Journal:  Health Aff (Millwood)       Date:  2010-05       Impact factor: 6.301

3.  Computer-aided diagnosis via model-based shape analysis: automated classification of wall motion abnormalities in echocardiograms.

Authors:  Johan G Bosch; Francisca Nijland; Steven C Mitchell; Boudewijn P F Lelieveldt; Otto Kamp; Johan H C Reiber; Milan Sonka
Journal:  Acad Radiol       Date:  2005-03       Impact factor: 3.173

4.  Telemedicine and rural health care applications.

Authors:  Anthony C Smith; M Bensink; N Armfield; J Stillman; L Caffery
Journal:  J Postgrad Med       Date:  2005 Oct-Dec       Impact factor: 1.476

5.  Cardiac disease recognition in echocardiograms using spatio-temporal statistical models.

Authors:  David Beymer; Tanveer Syeda-Mahmood
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2008

Review 6.  Telesonography: foundations and future directions.

Authors:  James E Sutherland; Dean Sutphin; Kerry Redican; Fredric Rawlins
Journal:  J Ultrasound Med       Date:  2011-04       Impact factor: 2.153

7.  Automatic classification of left ventricular regional wall motion abnormalities in echocardiography images using nonrigid image registration.

Authors:  Ahmad Shalbaf; Hamid Behnam; Zahra Alizade-Sani; Maryam Shojaifard
Journal:  J Digit Imaging       Date:  2013-10       Impact factor: 4.056

8.  Interpretation of remotely downloaded pocket-size cardiac ultrasound images on a web-enabled smartphone: validation against workstation evaluation.

Authors:  Brian G Choi; Monica Mukherjee; Praveen Dala; Heather A Young; Cynthia M Tracy; Richard J Katz; Jannet F Lewis
Journal:  J Am Soc Echocardiogr       Date:  2011-09-17       Impact factor: 5.251

9.  How to set up a low cost tele-ultrasound capable videoconferencing system with wide applicability.

Authors:  Innes Crawford; Paul B McBeth; Mark Mitchelson; James Ferguson; Corina Tiruta; Andrew W Kirkpatrick
Journal:  Crit Ultrasound J       Date:  2012-05-29

10.  Potential Use of Remote Telesonography as a Transformational Technology in Underresourced and/or Remote Settings.

Authors:  Linping Pian; Lawrence M Gillman; Paul B McBeth; Zhengwen Xiao; Chad G Ball; Michael Blaivas; Douglas R Hamilton; Andrew W Kirkpatrick
Journal:  Emerg Med Int       Date:  2013-01-28       Impact factor: 1.112

View more
  6 in total

Review 1.  How the Smartphone Is Changing Allergy Diagnostics.

Authors:  Ana Margarida Pereira; Cristina Jácome; Rute Almeida; João Almeida Fonseca
Journal:  Curr Allergy Asthma Rep       Date:  2018-10-25       Impact factor: 4.806

2.  A machine learning cardiac magnetic resonance approach to extract disease features and automate pulmonary arterial hypertension diagnosis.

Authors:  Andrew J Swift; Haiping Lu; Johanna Uthoff; Pankaj Garg; Marcella Cogliano; Jonathan Taylor; Peter Metherall; Shuo Zhou; Christopher S Johns; Samer Alabed; Robin A Condliffe; Allan Lawrie; Jim M Wild; David G Kiely
Journal:  Eur Heart J Cardiovasc Imaging       Date:  2021-01-22       Impact factor: 6.875

3.  Deep Neural Network-Aided Histopathological Analysis of Myocardial Injury.

Authors:  Yiping Jiao; Jie Yuan; Oluwatofunmi Modupeoluwa Sodimu; Yong Qiang; Yichen Ding
Journal:  Front Cardiovasc Med       Date:  2022-01-10

Review 4.  Advanced Ultrasound and Photoacoustic Imaging in Cardiology.

Authors:  Min Wu; Navchetan Awasthi; Nastaran Mohammadian Rad; Josien P W Pluim; Richard G P Lopata
Journal:  Sensors (Basel)       Date:  2021-11-28       Impact factor: 3.576

5.  Heartbeat Classification and Arrhythmia Detection Using a Multi-Model Deep-Learning Technique.

Authors:  Saad Irfan; Nadeem Anjum; Turke Althobaiti; Abdullah Alhumaidi Alotaibi; Abdul Basit Siddiqui; Naeem Ramzan
Journal:  Sensors (Basel)       Date:  2022-07-27       Impact factor: 3.847

6.  Automatic Evaluation of Heart Condition According to the Sounds Emitted and Implementing Six Classification Methods.

Authors:  Manuel A Soto-Murillo; Jorge I Galván-Tejada; Carlos E Galván-Tejada; Jose M Celaya-Padilla; Huizilopoztli Luna-García; Rafael Magallanes-Quintanar; Tania A Gutiérrez-García; Hamurabi Gamboa-Rosales
Journal:  Healthcare (Basel)       Date:  2021-03-12
  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.