Point of Care Lung Ultrasound Examination in Patients With Shortness of Breath

NCT ID: NCT07046234

Last Updated: 2025-10-03

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

ENROLLING_BY_INVITATION

Clinical Phase

NA

Total Enrollment

150 participants

Study Classification

INTERVENTIONAL

Study Start Date

2025-08-19

Study Completion Date

2025-12-31

Brief Summary

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This is a prospective, non-randomized clinical validation research study. Subjects will consent and have two ultrasounds as part of the study.

Detailed Description

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This study is to collect data to validate the performance of Caption Lung AI in enabling trained healthcare professionals to perform LUS to obtain diagnostic-quality Lung Ultrasound Studies.

Conditions

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Lung Disease Lung Ultrasound

Study Design

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Allocation Method

NON_RANDOMIZED

Intervention Model

PARALLEL

Primary Study Purpose

HEALTH_SERVICES_RESEARCH

Blinding Strategy

NONE

Study Groups

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Cohort 1 -Device 1

Cohort 1 will utilize the Vscan Air CL (curvilinear and linear probes) and SL (sector probe) handheld ultrasound to perform an 8-zone lung ultrasound (LUS) exam.

The Trained HCP will be required to capture at least 2 clips per zone using an ultrasound with Caption AI before moving onto the next zone. The scanner can capture multiple clips per zone, via-auto-capture or Save Best Clip (Study Exam).

The Expert LUS user will use the same ultrasound system but without Caption Lung AI (Control Exam).

Group Type EXPERIMENTAL

Lung ultrasound

Intervention Type DEVICE

Lung ultrasound software.

Cohort 2 -Device 2

Cohort 2 will utilize the Venue GO ultrasound system to perform a 12-zone lung ultrasound (LUS) exam.

All Qualified users using Caption Lung AI will be required to capture at least 2 clips per zone before moving onto the next zone. The scanner can capture multiple clips per zone, via-auto-capture or Save Best Clip (Study Exam).

The Qualified user will use the same ultrasound system but without Caption Lung AI (Control Exam).

Group Type EXPERIMENTAL

Lung ultrasound

Intervention Type DEVICE

Lung ultrasound software.

Cohort 0 - Vscan (no AI)

Cohort 0 will utilize the Vscan Air CL (curvilinear and linear probes) and SL (sector probe) handheld ultrasound to perform the following lung ultrasound (LUS) exams.: Each patient will undergo 2 8-zone lung examinations which will be a combination of the enrollment distribution listed below equating to up to 30 subjects with 2 scans per subject (60 scans).

Group Type ACTIVE_COMPARATOR

Lung ultrasound

Intervention Type DEVICE

Lung ultrasound software.

Interventions

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Lung ultrasound

Lung ultrasound software.

Intervention Type DEVICE

Other Intervention Names

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SaMD

Eligibility Criteria

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Inclusion Criteria

* 1\. Patients over the age of 22; AND
* 2\. Patients with known lung pathology or patients with no known lung pathology in the hospital or presenting to the hospital or outpatient setting with shortness of breath.

Exclusion Criteria

* 1\. Patients in extremis and/or patients in whom a lung ultrasound would not normally be performed;
* 2\. Patients who are unable to consent;
* 3\. Patients who are prisoners; AND
* 4\. Patients who do not speak English fluently.
Minimum Eligible Age

22 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Northwestern University

OTHER

Sponsor Role collaborator

Rush University Medical Center

OTHER

Sponsor Role collaborator

Yale University

OTHER

Sponsor Role collaborator

GE Healthcare

INDUSTRY

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Locations

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Yale University School of Medicine

New Haven, Connecticut, United States

Site Status

Rush University Medical Center

Chicago, Illinois, United States

Site Status

Northwestern University

Evanston, Illinois, United States

Site Status

Countries

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United States

Other Identifiers

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SA-000147

Identifier Type: -

Identifier Source: org_study_id

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