Application of mobile health, telemedicine and artificial intelligence to echocardiography

The intersection of global broadband technology and miniaturized high-capability computing devices has led to a revolution in the delivery of healthcare and the birth of telemedicine and mobile health (mHealth). Rapid advances in handheld imaging devices with other mHealth devices such as smartphone...

Full description

Saved in:
Bibliographic Details
Published inEcho research and practice Vol. 6; no. 2; pp. R41 - R52
Main Authors Seetharam, Karthik, Kagiyama, Nobuyuki, Sengupta, Partho P
Format Journal Article
LanguageEnglish
Published London Bioscientifica Ltd 01.06.2019
BioMed Central
Springer Nature B.V
BMC
Subjects
Online AccessGet full text
ISSN2055-0464
2055-0464
DOI10.1530/ERP-18-0081

Cover

More Information
Summary:The intersection of global broadband technology and miniaturized high-capability computing devices has led to a revolution in the delivery of healthcare and the birth of telemedicine and mobile health (mHealth). Rapid advances in handheld imaging devices with other mHealth devices such as smartphone apps and wearable devices are making great strides in the field of cardiovascular imaging like never before. Although these technologies offer a bright promise in cardiovascular imaging, it is far from straightforward. The massive data influx from telemedicine and mHealth including cardiovascular imaging supersedes the existing capabilities of current healthcare system and statistical software. Artificial intelligence with machine learning is the one and only way to navigate through this complex maze of the data influx through various approaches. Deep learning techniques are further expanding their role by image recognition and automated measurements. Artificial intelligence provides limitless opportunity to rigorously analyze data. As we move forward, the futures of mHealth, telemedicine and artificial intelligence are increasingly becoming intertwined to give rise to precision medicine.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Review-3
content type line 23
ISSN:2055-0464
2055-0464
DOI:10.1530/ERP-18-0081