Caption LungAI for Shortness of Breath

Overseen By Angeline Trinidad

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Caption Health, Inc.

Disqualifiers: In extremis, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: 1

220 Participants Needed

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial tests a new tool called Caption LungAI to evaluate its effectiveness in diagnosing shortness of breath linked to suspected B-lines (extra lines on lung scans indicating fluid in the lungs). The study includes two different lung ultrasound exams: one by a skilled expert and another by a healthcare professional using the Caption LungAI tool. Individuals who frequently experience shortness of breath and are suspected of having fluid in their lungs may be suitable for this trial. The goal is to determine if this new tool can make diagnosing lung issues faster and more accurate. As an unphased trial, it offers participants the chance to contribute to innovative research that could enhance diagnostic accuracy and speed for lung conditions.

Do I need to stop my current medications to join the trial?

The trial protocol does not specify whether you need to stop taking your current medications.

What prior data suggests that Caption LungAI is safe for use in lung ultrasound exams?

Research has shown that Caption LungAI uses artificial intelligence to help healthcare workers obtain clear lung images. As a software tool, it lacks the side effects associated with drugs. The FDA has approved this technology for imaging, deeming it safe for use.

Emergency rooms have used the software to detect lung issues such as fluid buildup. Although it did not demonstrate additional benefits over regular care in reducing certain fluid markers, it received positive feedback.

In summary, Caption LungAI is a safe tool for obtaining lung images and has no reported negative effects when used as intended. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about Caption LungAI because it offers a new approach to diagnosing shortness of breath caused by B-Lines in the lungs. Unlike traditional methods relying solely on expert interpretation of lung ultrasounds, Caption LungAI uses artificial intelligence to guide healthcare professionals in conducting and analyzing exams. This technology has the potential to improve the accuracy and efficiency of ultrasound diagnostics, making it accessible to a wider range of healthcare providers and potentially leading to quicker, more accurate assessments for patients.

What evidence suggests that Caption LungAI is effective for shortness of breath?

Studies have shown that Caption LungAI assists non-expert healthcare workers in capturing high-quality lung ultrasound images. One study found that beginners, with AI guidance, produced images comparable to those taken by experts after a brief training session. The software performed effectively when at least 80% of the ultrasound clips met the necessary quality for medical evaluations. This trial will assess the effectiveness of Caption LungAI in enhancing lung ultrasound imaging for patients with clinical suspicion of B-Lines. This could lead to more accurate assessments of lung issues, such as shortness of breath. ^{1 6 7 8 9}

Are You a Good Fit for This Trial?

This trial is for adults over 18 who come to the hospital or outpatient clinics with shortness of breath and suspected B-lines, which are signs on a lung ultrasound that may indicate fluid in the lungs. It's not for patients in critical condition where an ultrasound isn't a priority.

Inclusion Criteria

You are experiencing difficulty breathing and have suspected B-lines.

You must be 18 years old or older.

Exclusion Criteria

Patients who are in critical condition or have other urgent medical needs that would make it difficult to perform a research lung ultrasound.

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

1-2 weeks

Lung Ultrasound Examination

Participants undergo two 8-zone protocol lung ultrasound exams, one by an expert without Caption LungAI and one by a trained healthcare provider using Caption LungAI

1 day

1 visit (in-person)

Follow-up

Participants are monitored for safety and effectiveness after the ultrasound exams

Up to 24 weeks

What Are the Treatments Tested in This Trial?

Interventions

• Caption LungAI

Trial Overview

The study is testing Caption LungAI, a type of artificial intelligence designed to help doctors interpret lung ultrasounds more effectively when evaluating patients with shortness of breath.

How Is the Trial Designed?

Group I: Patients presenting with clinical suspicion of B-Lines Experimental Treatment 1 Intervention

Patients will undergo two 8-zone protocol lung ultrasound exams. One exam will be conducted by an expert lung ultrasound user without Caption LungAI and will last approximately 5-10 minutes. The second exam will be conducted by a healthcare professional with Caption LungAI and will last approximately 15-20 minutes.

Who Is Running the Clinical Trial?

Caption Health, Inc.

Lead Sponsor

Trials

6

Recruited

1,200+

Published Research Related to This Trial

A pictorial COPD action plan was created to help patients with limited literacy understand self-management strategies, showing reasonable comprehension in a study with 21 adults at a COPD clinic.

Most patients were able to effectively use the pictorial plan to identify appropriate self-management behaviors after receiving brief spoken reinforcement, indicating that visual aids can enhance understanding across different literacy levels.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Evaluation of a paper and electronic pictorial COPD action plan. Roberts, NJ., Partridge, MR.[2019]

The study involved 60 COPD patients and 50 healthcare professionals to evaluate the effectiveness of pictorial health information (HI) for patient self-management, with all 42 pictorials achieving high content validity scores (≥0.8).

Patients preferred pictorial HI that reflected their personal experiences, while healthcare professionals favored simpler, more abstract representations, suggesting that involving patients in the development of pictograms could enhance communication and understanding of health information.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Development of Self-Management Pictorial Health Information and Comparison of Korean Patients' With Chronic Obstructive Pulmonary Disease and Health Care Professionals' Perceptions. Choi, JY., Jin, X., Ryu, EJ.[2022]

Over a 5-year period, the implementation of GINA recommendations led to a significant increase in the proportion of asthma patients achieving well-controlled symptoms, rising from 2.6% at baseline to 59.5% by year 5.

The study also showed a marked decrease in persistent airflow limitation among patients, dropping from 26.7% at baseline to 12.2% by year 5, indicating improved lung function alongside better symptom control.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

GINA Implementation Improves Asthma Symptoms Control and Lung Function: A Five-Year Real-World Follow-Up Study. Tho, NV., Quan, VTT., Dung, DV., et al.[2023]

A study evaluating 13 safety communication materials for seven drugs in Japan found that while most included key messages and calls to action, the average clarity scores were below the acceptable threshold, indicating a need for improvement.

The materials often used complex terminology that was difficult for patients to understand, highlighting the necessity for a better evaluation system before these documents are published.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Evaluation of rapid drug safety communication materials for patients in Japan. Yaguchi-Saito, A., Yamamoto, K., Sengoku, T., et al.[2021]

40% of the patient information leaflets for the 50 most prescribed drugs in England did not provide any information about the likelihood of adverse effects, failing to meet patient needs.

Only 8% of the leaflets included numerical indications of risk, while most used vague verbal descriptors that could lead to overestimating the risk of adverse effects, highlighting a significant gap in effective communication of drug safety information.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Adequacy of patient information on adverse effects: an assessment of patient information leaflets in the UK. Carrigan, N., Raynor, DK., Knapp, P.[2022]

The RECEIVER trial demonstrated that a digital COPD support service led to high and sustained utilization among participants, with 64 out of 83 using the application regularly over an average follow-up of 78 weeks.

Participants using the digital service experienced significantly better clinical outcomes, including a longer median time to death or respiratory-related hospital admission (335 days vs 155 days in the control group) and a greater reduction in annual hospital bed days (8.12 days vs 3.38 days).

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Long-Term Usage and Improved Clinical Outcomes with Adoption of a COPD Digital Support Service: Key Findings from the RECEIVER Trial. Taylor, A., Cushing, A., Dow, M., et al.[2023]

This study analyzed data from the Japanese Adverse Drug Event Report (JADER) and the FDA Adverse Event Reporting System (FAERS) to investigate the incidence of interstitial lung disease (ILD) potentially caused by antifibrotic drugs nintedanib and pirfenidone, both of which are first-line treatments for idiopathic pulmonary fibrosis (IPF).

The findings indicated that both nintedanib and pirfenidone were associated with signals for causing ILD, emphasizing the need for careful monitoring of these drugs due to their limited effectiveness and potential to induce lung damage.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Evaluation of the potential complication of interstitial lung disease associated with antifibrotic drugs using data from databases reporting spontaneous adverse effects. Nawa, H., Hamano, H., Niimura, T., et al.[2023]

Adverse drug events lead to two million injuries, hospitalizations, or deaths annually, highlighting the need for better drug information resources.

Using a semantic natural language processing approach, researchers successfully extracted critical safety information from FDA black box warnings, achieving an overall performance of 90% precision, which can enhance clinical decision support systems to reduce adverse drug events.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Semantic processing to identify adverse drug event information from black box warnings. Culbertson, A., Fiszman, M., Shin, D., et al.[2018]

The study developed measures to assess the numeric complexity and density of asthma care instructions provided to patients, revealing that these instructions often contain challenging numeric information that may not match patients' health numeracy skills.

There was no correlation between the complexity of the numeric information in the instructions and the patients' ability to understand it, indicating that patients with lower health numeracy may struggle to effectively interpret their asthma management instructions.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Alignment of Patient Health Numeracy with Asthma Care Instructions in the Patient Portal. Schapira, MM., Mozal, C., Shofer, FS., et al.[2022]

In a focus group study comparing two formats for displaying asthma control scores, the reference range number line (RRNL) was preferred over the stoplight graphic for its clarity and ability to prompt self-management strategies among adults with asthma.

While both formats helped participants understand their asthma control, the RRNL can be adapted for various health metrics and is recommended for use in clinical settings to prevent misinterpretation of abnormal values until patients are familiar with it.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Reference range number line format preferred by adults for display of asthma control status. Arcia, A., George, M.[2021]

The prevalence of childhood asthma in Thailand has significantly increased from 4.2% to 13% over the past decade, highlighting a growing public health concern.

A study involving 60 asthmatic children revealed that using local terminology for asthma symptoms is crucial, as relying on English-based terms may lead to misinterpretation and underreporting of asthma cases, with up to 67% potentially missed in previous surveys.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

How do Thai children and adolescents describe asthma symptoms? Phankingthongkum, S., Daengsuwan, T., Visitsunthorn, N., et al.[2019]

Inhaled corticosteroids are a key treatment strategy for managing mild-to-moderate asthma, helping to reduce chronic inflammation in the airways and improve patients' quality of life.

The article discusses important updates from the GINA group in 2019 regarding asthma severity definitions and management goals, emphasizing the need for tailored therapeutic adjustments for effective asthma control.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

[Strategies for prescription of inhaled corticosteroids in mild-to-moderate asthma]. Beurnier, A., Humbert, M.[2021]

Effective communication is crucial for patients with chronic obstructive pulmonary disease (COPD) to engage in their care, but many patients struggle with written information due to low health literacy.

Improving health outcomes can be achieved by providing information in various formats, including verbal communication and visual aids, to accommodate different literacy levels among patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Health literacy in COPD. Roberts, NJ., Ghiassi, R., Partridge, MR.[2022]

The 2016 GINA report introduces a new definition of asthma that emphasizes its diverse nature and highlights the importance of confirming the diagnosis to avoid under- or overtreatment.

It provides a comprehensive management approach that prioritizes inhaled corticosteroids while also focusing on individualizing patient care, improving inhaler technique, and ensuring adherence to treatment.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Diagnosis and Management of Asthma - The Swiss Guidelines. Rothe, T., Spagnolo, P., Bridevaux, PO., et al.[2022]

A significant majority (66%) of interstitial lung disease (ILD) case reports in the European Medicines Agency's database came from Japan, indicating a geographic imbalance in reporting rates compared to the rest of the world.

The odds of a case report from Japan being classified as ILD were 20.7 times higher than for reports from other regions, prompting the European Medicines Agency to develop targeted risk minimization strategies for ILD in product information.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Geographical variation in reporting Interstitial Lung Disease as an adverse drug reaction: findings from an European Medicines Agency analysis of reports in EudraVigilance. Pinheiro, L., Blake, K., Januskiene, J., et al.[2018]

Citations

1.

clinicaltrials.gov

clinicaltrials.gov/study/NCT05992324

A Validation Study to Evaluate the Performance of Caption ...

Caption LungAI is a software that is designed to help non-expert healthcare professionals acquire diagnostic quality images on an 8-zone lung protocol for both ...

2.

pmc.ncbi.nlm.nih.gov

pmc.ncbi.nlm.nih.gov/articles/PMC11904735/

Artificial Intelligence–Guided Lung Ultrasound by Nonexperts

With AI assistance, trained novices can produce expert-level images that can be used to assess pathology after a short training session.

3.

jamanetwork.com

jamanetwork.com/journals/jamacardiology/fullarticle/2828727

Artificial Intelligence–Guided Lung Ultrasound by Nonexperts

The Lung Guidance AI algorithm's performance was considered successful if at least 80% of LUS clips met clinical assessment quality standards.

4.

centerwatch.com

centerwatch.com/clinical-trials/listings/NCT05992324/a-validation-study-to-evaluate-the-performance-of-caption-health-lung-guidance-and-interpretation

A Validation Study to Evaluate the Performance of Caption ...

The purpose of this study is to assess the efficacy of Caption LungAI.

5.

cdr.lib.unc.edu

cdr.lib.unc.edu/downloads/cn69mk14h

Artificial Intelligence–Guided Lung Ultrasound by Nonexperts

The purpose of this study was to evaluate the ability of AI technology to guide the acquisition of diag- nostic-quality LUS images by trained ...

6.

pmc.ncbi.nlm.nih.gov

pmc.ncbi.nlm.nih.gov/articles/PMC8419011/

A Randomized, Controlled Pilot Trial - PMC - PubMed Central

In this pilot trial, ED use of LUS to target pulmonary congestion conferred no benefit compared to usual care in reducing the number of B-lines at 6 hours or ...

7.

trialstoday.org

trialstoday.org/trial/NCT05992324

A Validation Study to Evaluate the Performance of Caption ...

The purpose of this study is to assess the efficacy of Caption LungAI. Condition. Shortness of Breath. Eligibility; Study Design; Locations ...

8.

onlinelibrary.wiley.com

onlinelibrary.wiley.com/doi/10.1002/jum.16088

New International Guidelines and Consensus on the Use ...

Guidelines. Perform functional assessment of the diaphragm to integrate the data of lung alteration, to address or refine the differential ...

9.

caption-care.com

caption-care.com/technology

Technology

Caption Care is powered by the first FDA cleared AI-guided imaging acquisition software from Caption Health. This breakthrough technology enables more ...