CoChat
Sign Up
Cited by 111Artificial Intelligence in Healthcare and EducationArtificial Intelligence in HealthcareUltrasound in Clinical Applications

The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.

Application of mobile health, telemedicine and artificial intelligence to echocardiography

Open access
Seetharam, Karthik, Kagiyama, Nobuyuki, Sengupta, Partho P.·2019·Echo Research & Practice
DOI 10.1530/erp-18-0081PMC PMC6432977
Open this paper in CoChat — free account

Abstract

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1530/erp-18-0081

Cites

65 of 65 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 Type of handheld ultrasound machines. There are several types of handheld ultrasounds with various capabilities; a laptop-based equipment has almost every 2D echocardiographic application (panel A), while a pocket-size ultrasound does not usually have full-scale color-flow and spectral Doppler capabilities (panel B). Reproduced, with permission, from Chamsi-Pasha et al. (4).
Figure 1 Type of handheld ultrasound machines. There are several types of handheld ultrasounds with various capabilities; a laptop-based equipment has almost every 2D echocardiographic application (panel A), while a pocket-size ultrasound does not usually have full-scale color-flow and spectral Doppler capabilities (panel B). Reproduced, with permission, from Chamsi-Pasha et al. (4).CC BY-NC · PubMed Central
Figure 2 Interrogation of mHealth devices and use of artificial intelligence. Technological advancement has created a number of mobile health devices, which are available even in resource-limited areas. Involving remote experts using telemedicine helps appropriate diagnosis and management. Artificial intelligence can efficiently address the lack of experts and the influx of complex data generated by mHealth and telemedicine as well as advanced imaging modalities.
Figure 2 Interrogation of mHealth devices and use of artificial intelligence. Technological advancement has created a number of mobile health devices, which are available even in resource-limited areas. Involving remote experts using telemedicine helps appropriate diagnosis and management. Artificial intelligence can efficiently address the lack of experts and the influx of complex data generated by mHealth and telemedicine as well as advanced imaging modalities.CC BY-NC · PubMed Central
Figure 3 Growth of publications in machine learning. The x- and y-axis shows the year and the number of publications in PubMed with ‘Cardiology’ and ‘Machine Learning’. The number of publications is rapidly growing, representing huge interest in the field. Reproduced, with permission, from Shameer et al. (50).
Figure 3 Growth of publications in machine learning. The x- and y-axis shows the year and the number of publications in PubMed with ‘Cardiology’ and ‘Machine Learning’. The number of publications is rapidly growing, representing huge interest in the field. Reproduced, with permission, from Shameer et al. (50).CC BY-NC · PubMed Central
Figure 4 Association of artificial intelligence, machine learning and deep learning. Artificial intelligence (AI), though there are various definitions by itself, represents any techniques which enables computers mimic human behavior when it’s used in medical field. Machine learning is a subfield of AI, which aims at automatic discovery of regularities in data through the use of computer algorithms and generalizing those into new but similar data. Deep learning is a subset of machine learning, which makes the computation of multi-layer neural networks feasible.
Figure 4 Association of artificial intelligence, machine learning and deep learning. Artificial intelligence (AI), though there are various definitions by itself, represents any techniques which enables computers mimic human behavior when it’s used in medical field. Machine learning is a subfield of AI, which aims at automatic discovery of regularities in data through the use of computer algorithms and generalizing those into new but similar data. Deep learning is a subset of machine learning, which makes the computation of multi-layer neural networks feasible.CC BY-NC · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 65 references

Research works printed but not linked: 2

  1. GE Healthcare. Vscan Extend datasheet. Chicago, IL, USA: General Electric Company, 2018. (available at: https://www.gehealthcare.com/-/media/fdbbc3f456914f5dbc3cc44cb866ffb5.pdf)
  2. DeepMind. AlphaGo. London, UK: DeepMind Technologies Limited, 2019. (available at: https://deepmind.com/research/alphago/)

Cited by

Sorted by citations

Related

See all

Metadata

Authors
Seetharam, K., Kagiyama, N., & Sengupta, P. P.
Affiliations
West Virginia University (US)
ORCID
Venue
Echo Research & Practice
Publisher
Springer Science and Business Media LLC
Published
2019-06
Volume
6
Issue
2
Pages
R41-R52
ISSN
  • 2055-0456 (ISSN-L)
  • 2055-0464
DOI
10.1530/erp-18-0081
PMC
PMC6432977
OpenAlex
W2921183828
Cited by
111
References
65
Citations by year
2026: 7 · 2025: 10 · 2024: 18 · 2023: 14 · 2022: 23 · 2021: 22 · 2020: 13 · 2019: 4
FWCI
0.76
Type
journal article
Language
en
Open access
Licence
Locations
  • Echo Research and Practice (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • The Research Repository @ WVU (West Virginia University) (submittedVersion, cc-by-nc)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Critical Care and Intensive Care Medicine › Ultrasound in Clinical Applications
Keywords
mobile health, telemedicine, artificial intelligence, echocardiography, cardiovascular imaging, handheld ultrasound, mobile application, wearable devices, deep learning, automated image analysis, machine learning, precision medicine

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review