Ultrasound Algorithms for Pregnancy

(MOBUS Trial)

Recruiting at 1 trial location

Overseen By Reshma Rajan-Joy, PhD

Age: 18+

Sex: Female

Trial Phase: Academic

Sponsor: Caption Health, Inc.

Disqualifiers: Unable to consent, Compromised care

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

Compensation: Varies

Number of Visits: 1

700 Participants Needed

Trial Summary

Do I need to stop my current medications for this trial?

The trial information does not specify whether you need to stop taking your current medications. It seems unlikely that you would need to stop, as the trial involves only ultrasound scans.

What data supports the effectiveness of the treatment Research Ultrasound scan for pregnancy?

Research shows that routine ultrasound scans during pregnancy can improve management by helping with the timing and mode of delivery and detecting conditions like placenta praevia (a condition where the placenta covers the cervix) and multiple pregnancies early. However, the benefits in low-risk pregnancies are still uncertain and need more study. ^{1 2 3 4 5}

Is the use of ultrasound algorithms for pregnancy generally safe for humans?

The research articles provided do not contain specific safety data about ultrasound algorithms for pregnancy, but they emphasize the importance of monitoring adverse events (unintended negative effects) to improve safety in medical treatments. ^{6 7 8 9 10}

How does the ultrasound algorithm treatment for pregnancy differ from other treatments?

The ultrasound algorithm treatment for pregnancy is unique because it uses advanced computer-assisted systems to analyze and manage ultrasound data, allowing for real-time evaluation and reporting during the examination. This approach enhances the accuracy and efficiency of prenatal assessments compared to traditional methods. ^{11 12 13 14 15}

What is the purpose of this trial?

The goal of this clinical trial is to * Collect ultrasound data from pregnant and non-pregnant individuals presenting to multiple study sites. * Use the collected data and ultrasound images to train and validate Artificial intelligence algorithms developed by the Sponsor Consented participants will be asked to take part in a research ultrasound scan

Eligibility Criteria

This trial is for pregnant individuals who consent to participate in research ultrasound scans. The study aims to gather data from both pregnant and non-pregnant participants across various sites.

Inclusion Criteria

I am 18 or older, not pregnant, and have given written consent.

I am 18 or older, pregnant, and have consented to participate.

Exclusion Criteria

Joining this study won't delay or harm my current care.

I am able to understand and provide written consent.

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Data Collection

Participants undergo research ultrasound scans to collect data for algorithm development

9 months

1 visit (in-person) for non-pregnant subjects, 2 visits (in-person) for pregnant subjects

Follow-up

Participants are monitored for safety and effectiveness after data collection

4 weeks

Treatment Details

Interventions

• Research Ultrasound scan

Trial Overview The intervention being studied is a research ultrasound scan. This clinical trial will collect ultrasound images and data to help train and validate artificial intelligence algorithms designed by the sponsor of the study.

Participant Groups

Group I: Group 2- Non pregnant Subjects Experimental Treatment 1 Intervention

This group is for non-pregnant subjects. They will have one ultrasound scan performed- it will be the research scan

Group II: Group 1: Pregnant Subjects Experimental Treatment 1 Intervention

This group is for pregnant subjects. They will have two ultrasound scans performed- One will be the standard of care ultrasound scan and the other will be research scan

Who Is Running the Clinical Trial?

Caption Health, Inc.

Lead Sponsor

Trials

6

Recruited

1,200+

Findings from Research

A new framework for monitoring adverse events (AEs) in pregnancy trials has been developed, which includes specific definitions and severity grading for both maternal and fetal AEs, enhancing safety assessments for drugs and interventions.

The framework was created through a comprehensive literature review and consensus from an international group of experts, resulting in 12 maternal and 19 fetal AE definitions that can be systematically applied in clinical trials.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Development of standard definitions and grading for Maternal and Fetal Adverse Event Terminology. Spencer, RN., Hecher, K., Norman, G., et al.[2022]

In a study of 796 women undergoing reproductive surgery, the overall rate of adverse events (AEs) was 6%, with 12 of these events deemed preventable, highlighting the importance of monitoring for safety in surgical settings.

The duration of surgery was identified as a significant risk factor for AEs, with longer surgeries increasing the likelihood of complications, suggesting that surgical time management could improve patient outcomes.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Clinical outcome monitoring in a reproductive surgery unit: a prospective cohort study in 796 patients. Bentz, EK., Imhof, M., Pateisky, N., et al.[2009]

A new NICU-focused trigger tool was developed and tested, revealing a high incidence of adverse events in hospitalized children, with 554 unique adverse events identified across 749 charts, averaging 0.74 events per patient.

The study found that 56% of these adverse events were preventable, highlighting the need for improved safety measures, as only 8% were reported through traditional hospital reporting systems.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Adverse events in the neonatal intensive care unit: development, testing, and findings of an NICU-focused trigger tool to identify harm in North American NICUs. Sharek, PJ., Horbar, JD., Mason, W., et al.[2021]

A quality improvement initiative in 14 hospitals led to a significant increase in the adoption of standardized care processes in perinatal units, rising from 38% to 81% between 2008 and 2012, which is crucial for enhancing safety practices.

The study found a 14% reduction in adverse outcomes, indicating that implementing multiple integrated strategies, including teamwork training and standardized care, can effectively improve perinatal safety and reduce harm.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Integrated Approach to Reduce Perinatal Adverse Events: Standardized Processes, Interdisciplinary Teamwork Training, and Performance Feedback. Riley, W., Begun, JW., Meredith, L., et al.[2019]

References

1. England pubmed.ncbi.nlm.nih.gov

Does routine ultrasound scanning improve outcome in pregnancy? Meta-analysis of various outcome measures. [2019]

2. England pubmed.ncbi.nlm.nih.gov

Obstetric Ultrasound Efficiency and Accuracy Using a Protocol-Based Examination. [2018]

3. United States pubmed.ncbi.nlm.nih.gov

Routine ultrasound screening in low-risk pregnancies: imperatives for further study. [2018]

4. England pubmed.ncbi.nlm.nih.gov

Effects of two ultrasound scanning regimens on the management of pregnancy. [2019]

5. United States pubmed.ncbi.nlm.nih.gov

The value of adding a universal booking scan to an existing protocol of routine mid-gestation ultrasound scan. [2016]

6. England pubmed.ncbi.nlm.nih.gov

Development of standard definitions and grading for Maternal and Fetal Adverse Event Terminology. [2022]

7. United States pubmed.ncbi.nlm.nih.gov

Clinical outcome monitoring in a reproductive surgery unit: a prospective cohort study in 796 patients. [2009]

8. England pubmed.ncbi.nlm.nih.gov

Using adverse events in health-care quality improvement: results from a British acute hospital. [2019]

9. United States pubmed.ncbi.nlm.nih.gov

Adverse events in the neonatal intensive care unit: development, testing, and findings of an NICU-focused trigger tool to identify harm in North American NICUs. [2021]

10. United States pubmed.ncbi.nlm.nih.gov

Integrated Approach to Reduce Perinatal Adverse Events: Standardized Processes, Interdisciplinary Teamwork Training, and Performance Feedback. [2019]

11. United States pubmed.ncbi.nlm.nih.gov

Second- and third-trimester ultrasound evaluation of chorionicity and amnionicity in twin pregnancy. A simple algorithm. [2016]

12. Switzerland pubmed.ncbi.nlm.nih.gov

First-Trimester Ultrasound Imaging for Prenatal Assessment of the Extended Cardiovascular System Using the Cardiovascular System Sonographic Evaluation Algorithm (CASSEAL). [2023]

13. United States pubmed.ncbi.nlm.nih.gov

Reproducibility of 3-dimensional sonographic measurements of fetal and placental volume at gestational ages of 11-18 weeks. [2016]

14. United States pubmed.ncbi.nlm.nih.gov

Ultrasonic prediction of term birth weight in Hispanic women. Accuracy in an outpatient clinic. [2021]

15. England pubmed.ncbi.nlm.nih.gov

Minicomputer-based system for obstetric ultrasound analysis, with multi-user and networking capability. [2019]