NCT04580095

Brief Summary

The purpose of this study is to assess the effect of artificial intelligence algorithms on image quality in echocardiography.

Trial Health

87
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
88

participants targeted

Target at P50-P75 for not_applicable

Timeline
Completed

Started Sep 2020

Shorter than P25 for not_applicable

Geographic Reach
1 country

1 active site

Status
completed

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

September 23, 2020

Completed
6 days until next milestone

Study Start

First participant enrolled

September 29, 2020

Completed
9 days until next milestone

First Posted

Study publicly available on registry

October 8, 2020

Completed
9 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

June 30, 2021

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

June 30, 2021

Completed
Last Updated

April 14, 2022

Status Verified

April 1, 2022

Enrollment Period

9 months

First QC Date

September 23, 2020

Last Update Submit

April 7, 2022

Conditions

Heart Diseases

Outcome Measures

Primary Outcomes (1)

  • Left ventricular apical foreshortening

    Apical foreshortening will be evaluated in a blinded manner by echocardiography experts post hoc. The two study arms will be compared.

    0 days

Other Outcomes (8)

  • Image quality

    0 days

  • Inter-observer variation of left ventricular size

    0 days

  • Inter-observer variation of left ventricular function

    0 days

  • +5 more other outcomes

Study Arms (2)

With AI algorithm

EXPERIMENTAL

In the "With AI algorithm" arm, the sonographer will perform the echocardiographic exam using the AI algorithm.

Other: AI algorithm for apical foreshortening in echocardiography

Without AI algorithm

NO INTERVENTION

In the "Without AI algorithm", the echocardiographic exam will be performed without the use of the AI algorithm.

Interventions

AI algorithm for apical foreshortening in echocardiographyOTHER

The algorithm is based on artificial intelligence, giving the sonographer performing the echocardiographic exam real-time feedback on left ventricular apical foreshortening.The algorithm is developed using deep learning techniques by technologists at the Department of Circulation and Medical Imaging, NTNU.

With AI algorithm

Eligibility Criteria

Age18 Years+
Sexall
Healthy VolunteersYes
Age GroupsAdult (18-64), Older Adult (65+)

You may qualify if:

  • Capacity to consent

You may not qualify if:

  • Indication for echocardiographic contrast agents

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

St. Olav University Hospital

Trondheim, 7491, Norway

Location

Related Publications (3)

  • Pettersen H, Sabo S, Pasdeloup D, Smistad E, Olaisen S, Ostvik A, Stolen S, Grenne BL, Lovstakken L, Dalen H, Holte E. Real-time deep learning-based image guiding and automated left ventricular measurements to reduce test-retest variability. Open Heart. 2025 Dec 7;12(2):e003783. doi: 10.1136/openhrt-2025-003783.

    PMID: 41360622DERIVED

  • Sabo S, Pasdeloup D, Pettersen HN, Smistad E, Ostvik A, Olaisen SH, Stolen SB, Grenne BL, Holte E, Lovstakken L, Dalen H. Real-time guidance by deep learning of experienced operators to improve the standardization of echocardiographic acquisitions. Eur Heart J Imaging Methods Pract. 2023 Nov 27;1(2):qyad040. doi: 10.1093/ehjimp/qyad040. eCollection 2023 Sep.

    PMID: 39045079DERIVED

  • Sabo S, Pettersen HN, Smistad E, Pasdeloup D, Stolen SB, Grenne BL, Lovstakken L, Holte E, Dalen H. Real-time guiding by deep learning during echocardiography to reduce left ventricular foreshortening and measurement variability. Eur Heart J Imaging Methods Pract. 2023 Aug 1;1(1):qyad012. doi: 10.1093/ehjimp/qyad012. eCollection 2023 May.

    PMID: 39044792DERIVED

MeSH Terms

Conditions

Heart Diseases

Condition Hierarchy (Ancestors)

Cardiovascular Diseases

Study Design

Study Type
interventional
Phase
not applicable
Allocation
RANDOMIZED
Masking
SINGLE
Who Masked
OUTCOMES ASSESSOR
Purpose
OTHER
Intervention Model
PARALLEL
Sponsor Type
OTHER
Responsible Party
SPONSOR

Study Record Dates

First Submitted

September 23, 2020

First Posted

October 8, 2020

Study Start

September 29, 2020

Primary Completion

June 30, 2021

Study Completion

June 30, 2021

Last Updated

April 14, 2022

Record last verified: 2022-04

Locations

NO(1)