To Validate the Clinical Performance of AI/ML Technologies in First-trimester Pregnant ParticipantsNCT06952439
Clinical Performance Evaluation of the Artificial Intelligence (AI)/ Machine Learning (ML) Technologies Utilized by the Origin Medical EXAM ASSISTANT
An observational study of To Validate the Clinical Performance of AI/ML Technologies in First-trimester Pregnant Participants and First Trimester Pregnancy, run by Origin Medical Systems, Inc. at 15 sites in United States.
Trial summary
A multicenter study will be conducted to assess the role of the AI/ML technologies of Origin Medical EXAM ASSISTANT (OMEA) in interpreting first-trimester fetal ultrasound examinations (11 weeks 0 days - 13 weeks 6 days). Read more about this study
- Who is it for?
- Ages 18 years and older, women onlyHealthy volunteers can take part
- Conditions the study is for
- To Validate the Clinical Performance of AI/ML Technologies in First-trimester Pregnant Participants, First Trimester Pregnancy
- What is being tested?
- Images and cines captured through the capture card (ICC)
- Type of treatment
- Medical device
- How many people
- 551 participants
- Where
- 15 sitesUnited States
- Study dates
- 19 Mar 2025to 19 May 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- Women only
- Healthy volunteers accepted
- Adult, Older adult
Study population: Origin Medical EXAM ASSISTANT™ is indicated for use during the first trimester (11 weeks 0 days to 13 weeks 6 days) fetal/obstetric ultrasound examinations of normal singleton pregnancies.
Who may be able to join
- Maternal age ≥ 18 years
- BMI < 40 kg/m2
- Live non-anomalous singleton pregnancies
- Gestational age between 11 weeks + 0 days and 13 weeks + 6 days, as determined by:
- Last menstrual period (LMP) or, Ultrasound report if the the LMP date is uncertain Note: Gestational age determination follows standard American College of Obstetricians and Gynecologists (ACOG) guidelines.
- Informed consent is obtained from the participant
- Exams obtained as per the Image Acquisition Protocol
Who cannot join
- Multiple Pregnancies
- Cases with fetal demise or other fetal abnormalities observed/suspected after the ultrasound examination
- Cases of planned diagnostic ultrasound follow-up exams within 2 weeks for known or suspected abnormality after the current ultrasound examination for the study
Trial locations and contacts
15 sites in 1 country.
15 sites in 1 country
United States15 sites
Harbinder S Brar MD Inc
Apple Valley, California, 92307
Total Womens Care PLLC
Houston, Texas, 77018
Harbinder S Brar MD Inc
Indio, California, 92201
Harbinder S Brar MD Inc
Murrieta, California, 92562
Sweet Pea 3D/4D Ultrasound Nola
New Orleans, Louisiana, 70002
The Nest 4D Ultrasound LLC, DBA InFocus Ultrasound DBA Little Peanut 4D Ultrasound
Norman, Oklahoma, 73072
The Nest 4D Ultrasound LLC, DBA InFocus Ultrasound DBA Little Peanut 4D Ultrasound
Oklahoma City, Oklahoma, 73106
Tiny Blessings Ultrasound 4D Studio
Owasso, Oklahoma, 74055
Mid-Carolina OB/GYN
Raleigh, North Carolina, 27607
Harbinder S Brar MD Inc
Redlands, California, 92374
Harbinder S Brar MD Inc
Riverside, California, 92506
Harbinder S Brar MD Inc
San Bernardino, California, 92404
Tiny Blessings Ultrasound 4D Studio
Skiatook, Oklahoma, 74070
Mobile Mama Ultrasound, LLC
Troy, New York, 12180
Reveal Ultrasound, LLC-S
Webster, Texas, 77598
About this study
Official title: Prospective Multicenter Study to Evaluate the Performance of the Artificial Intelligence (AI)/ Machine Learning (ML) Technologies Utilized by the Origin Medical EXAM ASSISTANT
A multicenter study will be conducted to assess the role of the AI/ML technologies of Origin Medical EXAM ASSISTANT (OMEA) in interpreting first-trimester fetal ultrasound examinations (11 weeks 0 days - 13 weeks 6 days). The performance of the AI-based system will be compared against the ground truth provided by an independent reading panel of maternal-fetal medicine physicians.
Read the detailed description
Study Brief:
A multicenter, prospective observational study shall be conducted for the performance assessment and validation of the AI/ML technologies used in OMEA for the automated assessment of the first-trimester standard fetal ultrasound examinations. A prospective dataset of at least n=289 fetal ultrasound examinations shall be collected from pregnant participants with 11 weeks 0 days to 13 weeks 6 days weeks of gestational age (first trimester).
Study Objectives:
This study aims to evaluate the performance of the Artificial Intelligence (AI) / Machine Learning (ML) technologies utilized in OMEA for the:
- Automated detection of standard diagnostic views in accordance with practice guidelines;
- Automated verification of quality criteria required for the interpretation of diagnostic views in accordance with practice guidelines; Note: Quality criteria can pertain to the following:
- Presence/Absence of anatomical landmarks/structures identified within the diagnostic views detected
- Verification of imaging parameters (e.g., magnification)
- Verification of clinical features (e.g., orientation of the fetus)
- Automated caliper placements to obtain measurements in accordance with practice guidelines;
Compliance with HIPAA Guidelines:
All data obtained will be de-identified according to the Health Insurance Portability and Accountability Act (HIPAA) guidelines. The sponsor will be responsible for the storage, management, and security of the de-identified data collected. To protect patient privacy, all data collected for the study undergoes de-identification, ensuring the removal of any identifiable patient information. Each data entry is assigned a unique patient number, which serves as the sole identifier for the study. The link between patient numbers and patient identifiers is securely maintained and accessible only to the principal investigator (PI) and research staff at the study site location. This link is strictly confidential and is not shared with other individuals involved in the study. Its purpose is solely for the site's reference, enabling follow-up with medical records if required. By implementing these measures, the study maintains a high level of confidentiality, safeguarding patient identities while allowing for essential record-keeping and potential future reference.
Sample Size Considerations:
Approximately 500 participants will be recruited for the study, the details of which will be captured in a statistical analysis plan that will be submitted to the FDA.
Study Design and Workflow:
The data for the study is collected in line with the Data Collection Plan and the predefined inclusion and exclusion criteria. The ARDMS performing the routine first-trimester ultrasound scan will be trained on the Image Acquisition Protocol and the Maternal Fetal Medicine (MFM)/reading physicians performing clinical benchmarking will be guided through the Reading Physician Training Manual for ensuring standardized data capture and clinical benchmarking processes for evaluating the standalone performance of the AI/ML technologies used in OMEA. All the above mentioned documents will be submitted to the FDA as part of the premarket submission review.
Phase 1: Data Capture:
At each study site, informed consent will be provided and obtained from eligible participants, and the following information will be collected.
Patient Details:
- Fetal gestational age
- Maternal age
- Maternal BMI
- Race/Ethnicity
- Confirmation of diagnosis of fetal anomaly/syndrome prior to the study exam and post the study exam
Site Details:
- Site location
- Sonographer name
- Ultrasound scanner manufacturer and series
Images and cines captured on the ultrasound machine (IUS): Registered diagnostic medical sonographers shall conduct routine first-trimester scans as per the Image Acquisition Protocol.
Images and cines captured through the capture card (ICC): A screen capture/recording of the entire exam performed by the sonographer as per the Image Acquisition Protocol will be obtained, and the images/cines required for the study that correspond to IUS will be obtained. The independent research coordinator from Origin Medical for the study will review the screen recording and identify the frame/cine for each diagnostic view (ICC) that corresponds to IUS based time stamps.
An independent quality reviewer from Origin Medical will verify whether the corresponding pair (i.e., captured on an ultrasound machine vs. obtained through screen recording) of frames/cine for each diagnostic view has been extracted or not.
Phase 2: Clinical Benchmarking and Statistical Analysis
All images/cines (IUS) from all patient exams that meet the study eligibility criteria will be pooled and randomized to prepare the ground truth by an independent Reading Panel (MFM physicians).
AI/ML technologies of OMEA interpretation of ICC The frozen AL/ML technologies used in OMEA shall interpret the images/cines (ICC).
For the sake of clarity, the ICC refers to the images/cines extracted from screen recordings using a capture card that correspond to the same images/cines as obtained by the ARDMS on the ultrasound machine.
The following tasks shall be performed by the AI/ML technologies on ICC:
- Automated detection of standard diagnostic views;
- Automated verification of quality criteria required for interpretation of diagnostic views ;
- Automated caliper placements to obtain fetal measurements;
The performance of the AI/ML technologies used in the OMEA (on all ICC images/ cines that meet the study eligibility criteria) shall be compared against the ground truth for statistical analysis, i.e., against the majority consensus obtained from the Reading Panel for detection of diagnostic views, verification of quality criteria, performing fetal biometry measurements and ACEP grading of the images/cines.
- Conditions
- To Validate the Clinical Performance of AI/ML Technologies in First-trimester Pregnant Participants, First Trimester Pregnancy
- Being tested
- Images and cines captured through the capture card (ICC)
- Keywords
- Artificial Intelligence, Machine Learning, First Trimester Pregnancy, Ultrasound Imaging
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Images and cines captured through the capture card (ICC)Device
A screen capture/recording of the entire exam performed by the ARDMS as per the Image Acquisition Protocol for the FDA Study will be obtained, and the images/cines required for the study that correspond to IUS shall be obtained. The independent research coordinator for the study will review the screen recording and identify the frame/cine for each diagnostic view (ICC) that corresponds to IUS based on the time stamps.
| Study group | Role | What they receive |
|---|---|---|
| Images and cines captured on the ultrasound machine (IUS) | American registered diagnostic medical sonographers (ARDMS; single operator/ultrasound scan room) shall conduct routine first-trimester scans as per the Image Acquisition Protocol for the FDA Study.
Note: Patient exams that do not meet the study eligibility criteria, as identified or observed by the ARDMS, will be excluded at this stage. The exclusion criteria identified or observed by the sonographer are as follows:
1. Intrauterine fetal demise
2. Multiple gestations
3. Incorrect GA identified
4. Sonographer identifies structural abnormalities
5. Inability to continue the exam by the sonographer/patient Only the image frames and cine(s) from exams that satisfy the study inclusion criteria will be collected. Receives: Device: Images and cines captured through the capture card (ICC) |
What is being measured
The primary outcome is the main question the study is designed to answer.
To assess whether the AI/ML technologies used in OMEA can achieve an acceptable sensitivity for identifying the diagnostic view
The overall sensitivity and two-sided 95% confidence interval (CI) will be determined using data pooled across diagnostic views.
Note: The participant is assessed for the primary outcome on the same day of enrollment.
Measured: 11 weeks 0 days to 13 weeks 6 days
To assess whether the AI/ML technologies used in OMEA can achieve an acceptable sensitivity and consistency for verifying the quality criteria of a given image
The overall sensitivity and two-sided 95% confidence interval (CI) will be determined using data pooled across diagnostic views for quality criteria.
Note: The participant is assessed for the primary outcome on the same day of enrollment.
Measured: 11 weeks 0 days to 13 weeks 6 days
To evaluate the performance of the OMEA AI/ML technologies with respect to facilitating the determination of quantitative measure of crown-rump length (CRL) and nuchal translucency (NT), complementary evaluations of agreement will be performed.
The agreement and consistency of the AI/ML technologies used in OMEA to obtain quantitative measurements (i.e., CRL and NT) compared to the MFM physician average will be analyzed by applying Deming regression and Bland-Altman analysis.
Note: The participant is assessed for the primary outcome on the same day of enrollment.
Measured: 11 weeks 0 days to 13 weeks 6 days
Secondary outcomes (1)
To assess the sensitivity and specificity for the detection of each of the individual diagnostic view of each of the individual quality criteria within each diagnostic view.
We will evaluate the sensitivity and two-sided 95% CI (two-sided 90% CI) for each of the individual diagnostic planes and individual quality criteria within each diagnostic plane. Parallelly, specificity shall also be determined for the AI/ML technologies to accurately determine when a diagnostic view/quality criteria is not present, along with the associated 95% confidence interval.
Note: The participant is assessed for the secondary outcome on the same day of enrollment.
Measured: 11 weeks 0 days to 13 week 6 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Apr 2025
- Study start19 Mar 2025
- Primary completion19 May 2025
- Study completion19 May 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (15)Papers the sponsor has linked to this study.
- Other trials for To Validate the Clinical Performance of AI/ML Technologies in First-trimester Pregnant ParticipantsStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Cross-sectional
- Enrolment
- 551 participants (actual)
Sponsor and investigators
- Lead sponsor
- Origin Medical Systems, Inc.
- Sponsor type
- Industry (pharma, biotech, devices)
- Responsible party
- Sponsor
- Investigators
- Jeroen Peter Vanderhoeven, MD, Principal investigator, Providence Swedish Medical Center
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06952439
- Sponsor's protocol ID
- OMS-US-EA-FDA-CPA-01
- Other IDs
- 03/13/2025
- Status last verified
- Oct 2025
- First submitted
- 9 Apr 2025
- Last updated
- 21 Oct 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- No: The Individual Participant Data (IPD) from this FDA study will remain confidential and will not be shared with other researchers due to participant privacy protections and the proprietary nature of the study.
Publications
Bland JM, Altman DG. Comparing methods of measurement: why plotting difference against standard method is misleading. Lancet. 1995 Oct 21;346(8982):1085-7. doi: 10.1016/s0140-6736(95)91748-9.
PubMed 7564793Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986 Feb 8;1(8476):307-10.
PubMed 2868172Payne RB. Deming's regression analysis in method comparison studies. Ann Clin Biochem. 1985 Jul;22 ( Pt 4):430. doi: 10.1177/000456328502200419. No abstract available.
PubMed 4037671Committee Opinion No 700: Methods for Estimating the Due Date. Obstet Gynecol. 2017 May;129(5):e150-e154. doi: 10.1097/AOG.0000000000002046.
PubMed 28426621Ghelich Oghli M, Shabanzadeh A, Moradi S, Sirjani N, Gerami R, Ghaderi P, Sanei Taheri M, Shiri I, Arabi H, Zaidi H. Automatic fetal biometry prediction using a novel deep convolutional network architecture. Phys Med. 2021 Aug;88:127-137. doi: 10.1016/j.ejmp.2021.06.020. Epub 2021 Jul 6.
PubMed 34242884Matthew J, Skelton E, Day TG, Zimmer VA, Gomez A, Wheeler G, Toussaint N, Liu T, Budd S, Lloyd K, Wright R, Deng S, Ghavami N, Sinclair M, Meng Q, Kainz B, Schnabel JA, Rueckert D, Razavi R, Simpson J, Hajnal J. Exploring a new paradigm for the fetal anomaly ultrasound scan: Artificial intelligence in real time. Prenat Diagn. 2022 Jan;42(1):49-59. doi: 10.1002/pd.6059. Epub 2021 Oct 18.
PubMed 34648206Akkus Z, Cai J, Boonrod A, Zeinoddini A, Weston AD, Philbrick KA, Erickson BJ. A Survey of Deep-Learning Applications in Ultrasound: Artificial Intelligence-Powered Ultrasound for Improving Clinical Workflow. J Am Coll Radiol. 2019 Sep;16(9 Pt B):1318-1328. doi: 10.1016/j.jacr.2019.06.004.
PubMed 31492410He F, Wang Y, Xiu Y, Zhang Y, Chen L. Artificial Intelligence in Prenatal Ultrasound Diagnosis. Front Med (Lausanne). 2021 Dec 16;8:729978. doi: 10.3389/fmed.2021.729978. eCollection 2021.
PubMed 34977053Xiao S, Zhang J, Zhu Y, Zhang Z, Cao H, Xie M, Zhang L. Application and Progress of Artificial Intelligence in Fetal Ultrasound. J Clin Med. 2023 May 5;12(9):3298. doi: 10.3390/jcm12093298.
PubMed 37176738Siddique J, Lauderdale DS, VanderWeele TJ, Lantos JD. Trends in prenatal ultrasound use in the United States: 1995 to 2006. Med Care. 2009 Nov;47(11):1129-35. doi: 10.1097/MLR.0b013e3181b58fbf.
PubMed 19786915Kurjak A, Medjedovic E, Stanojevic M. Use and misuse of ultrasound in obstetrics with reference to developing countries. J Perinat Med. 2022 Oct 28;51(2):240-252. doi: 10.1515/jpm-2022-0438. Print 2023 Feb 23.
PubMed 36302110Walsh CA, McAuliffe F, Kinsella V, McParland P. Routine obstetric ultrasound services. Ir Med J. 2013 Nov-Dec;106(10):311-3.
PubMed 24579412Salomon LJ, Alfirevic Z, Bilardo CM, Chalouhi GE, Ghi T, Kagan KO, Lau TK, Papageorghiou AT, Raine-Fenning NJ, Stirnemann J, Suresh S, Tabor A, Timor-Tritsch IE, Toi A, Yeo G. ISUOG practice guidelines: performance of first-trimester fetal ultrasound scan. Ultrasound Obstet Gynecol. 2013 Jan;41(1):102-13. doi: 10.1002/uog.12342. No abstract available.
PubMed 23280739International Society of Ultrasound in Obstetrics and Gynecology; Bilardo CM, Chaoui R, Hyett JA, Kagan KO, Karim JN, Papageorghiou AT, Poon LC, Salomon LJ, Syngelaki A, Nicolaides KH. ISUOG Practice Guidelines (updated): performance of 11-14-week ultrasound scan. Ultrasound Obstet Gynecol. 2023 Jan;61(1):127-143. doi: 10.1002/uog.26106. No abstract available.
PubMed 36594739AIUM Practice Parameter for the Performance of Standard Diagnostic Obstetric Ultrasound. J Ultrasound Med. 2024 Jun;43(6):E20-E32. doi: 10.1002/jum.16406. Epub 2024 Jan 15. No abstract available.
PubMed 38224490
Study at a glance
From the registry record, last updated by the sponsor on 21 Oct 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prospective Multicenter Study to Evaluate the Performance of the Artificial Intelligence (AI)/ Machine Learning (ML) Technologies Utilized by the Origin Medical EXAM ASSISTANT
- ClinicalTrials.gov ID
- NCT06952439
- Acronym / sponsor ID
- OMS-US-EA-FDA-CPA-01
- Conditions
- To Validate the Clinical Performance of AI/ML Technologies in First-trimester Pregnant Participants, First Trimester Pregnancy
- Keywords
- Artificial Intelligence, Machine Learning, First Trimester Pregnancy, Ultrasound Imaging
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 551 participants (actual)
- Lead sponsor
- Origin Medical Systems, Inc.Industry (pharma, biotech, devices)
- Study start
- 19 Mar 2025 (actual)
- Primary completion
- 19 May 2025 (actual)
- Study completion
- 19 May 2025 (actual)
- First posted
- 30 Apr 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
A multicenter study will be conducted to assess the role of the AI/ML technologies of Origin Medical EXAM ASSISTANT (OMEA) in interpreting first-trimester fetal ultrasound examinations (11 weeks 0 days - 13 weeks 6 days). The performance of the AI-based system will be compared against the ground truth provided by an independent reading panel of maternal-fetal medicine physicians.
Detailed description
Study Brief:
A multicenter, prospective observational study shall be conducted for the performance assessment and validation of the AI/ML technologies used in OMEA for the automated assessment of the first-trimester standard fetal ultrasound examinations. A prospective dataset of at least n=289 fetal ultrasound examinations shall be collected from pregnant participants with 11 weeks 0 days to 13 weeks 6 days weeks of gestational age (first trimester).
Study Objectives:
This study aims to evaluate the performance of the Artificial Intelligence (AI) / Machine Learning (ML) technologies utilized in OMEA for the:
- Automated detection of standard diagnostic views in accordance with practice guidelines;
- Automated verification of quality criteria required for the interpretation of diagnostic views in accordance with practice guidelines; Note: Quality criteria can pertain to the following:
- Presence/Absence of anatomical landmarks/structures identified within the diagnostic views detected
- Verification of imaging parameters (e.g., magnification)
- Verification of clinical features (e.g., orientation of the fetus)
- Automated caliper placements to obtain measurements in accordance with practice guidelines;
Compliance with HIPAA Guidelines:
All data obtained will be de-identified according to the Health Insurance Portability and Accountability Act (HIPAA) guidelines. The sponsor will be responsible for the storage, management, and security of the de-identified data collected. To protect patient privacy, all data collected for the study undergoes de-identification, ensuring the removal of any identifiable patient information. Each data entry is assigned a unique patient number, which serves as the sole identifier for the study. The link between patient numbers and patient identifiers is securely maintained and accessible only to the principal investigator (PI) and research staff at the study site location. This link is strictly confidential and is not shared with other individuals involved in the study. Its purpose is solely for the site's reference, enabling follow-up with medical records if required. By implementing these measures, the study maintains a high level of confidentiality, safeguarding patient identities while allowing for essential record-keeping and potential future reference.
Sample Size Considerations:
Approximately 500 participants will be recruited for the study, the details of which will be captured in a statistical analysis plan that will be submitted to the FDA.
Study Design and Workflow:
The data for the study is collected in line with the Data Collection Plan and the predefined inclusion and exclusion criteria. The ARDMS performing the routine first-trimester ultrasound scan will be trained on the Image Acquisition Protocol and the Maternal Fetal Medicine (MFM)/reading physicians performing clinical benchmarking will be guided through the Reading Physician Training Manual for ensuring standardized data capture and clinical benchmarking processes for evaluating the standalone performance of the AI/ML technologies used in OMEA. All the above mentioned documents will be submitted to the FDA as part of the premarket submission review.
Phase 1: Data Capture:
At each study site, informed consent will be provided and obtained from eligible participants, and the following information will be collected.
Patient Details:
- Fetal gestational age
- Maternal age
- Maternal BMI
- Race/Ethnicity
- Confirmation of diagnosis of fetal anomaly/syndrome prior to the study exam and post the study exam
Site Details:
- Site location
- Sonographer name
- Ultrasound scanner manufacturer and series
Images and cines captured on the ultrasound machine (IUS): Registered diagnostic medical sonographers shall conduct routine first-trimester scans as per the Image Acquisition Protocol.
Images and cines captured through the capture card (ICC): A screen capture/recording of the entire exam performed by the sonographer as per the Image Acquisition Protocol will be obtained, and the images/cines required for the study that correspond to IUS will be obtained. The independent research coordinator from Origin Medical for the study will review the screen recording and identify the frame/cine for each diagnostic view (ICC) that corresponds to IUS based time stamps.
An independent quality reviewer from Origin Medical will verify whether the corresponding pair (i.e., captured on an ultrasound machine vs. obtained through screen recording) of frames/cine for each diagnostic view has been extracted or not.
Phase 2: Clinical Benchmarking and Statistical Analysis
All images/cines (IUS) from all patient exams that meet the study eligibility criteria will be pooled and randomized to prepare the ground truth by an independent Reading Panel (MFM physicians).
AI/ML technologies of OMEA interpretation of ICC The frozen AL/ML technologies used in OMEA shall interpret the images/cines (ICC).
For the sake of clarity, the ICC refers to the images/cines extracted from screen recordings using a capture card that correspond to the same images/cines as obtained by the ARDMS on the ultrasound machine.
The following tasks shall be performed by the AI/ML technologies on ICC:
- Automated detection of standard diagnostic views;
- Automated verification of quality criteria required for interpretation of diagnostic views ;
- Automated caliper placements to obtain fetal measurements;
The performance of the AI/ML technologies used in the OMEA (on all ICC images/ cines that meet the study eligibility criteria) shall be compared against the ground truth for statistical analysis, i.e., against the majority consensus obtained from the Reading Panel for detection of diagnostic views, verification of quality criteria, performing fetal biometry measurements and ACEP grading of the images/cines.
Study design
- Observational model
- Other
- Time perspective
- Cross-sectional
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Images and cines captured on the ultrasound machine (IUS) | – | Device: Images and cines captured through the capture card (ICC)American registered diagnostic medical sonographers (ARDMS; single operator/ultrasound scan room) shall conduct routine first-trimester scans as per the Image Acquisition Protocol for the FDA Study. Note: Patient exams that do not meet the study eligibility criteria, as identified or observed by the ARDMS, will be excluded at this stage. The exclusion criteria identified or observed by the sonographer are as follows: 1. Intrauterine fetal demise 2. Multiple gestations 3. Incorrect GA identified 4. Sonographer identifies structural abnormalities 5. Inability to continue the exam by the sonographer/patient Only the image frames and cine(s) from exams that satisfy the study inclusion criteria will be collected. |
Who may qualify
- Ages
- 18 years and older
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Origin Medical EXAM ASSISTANT™ is indicated for use during the first trimester (11 weeks 0 days to 13 weeks 6 days) fetal/obstetric ultrasound examinations of normal singleton pregnancies.
Key inclusion criteria (7 listed)
- Maternal age ≥ 18 years
- BMI < 40 kg/m2
- Live non-anomalous singleton pregnancies
- Gestational age between 11 weeks + 0 days and 13 weeks + 6 days, as determined by:
- Last menstrual period (LMP) or, Ultrasound report if the the LMP date is uncertain Note: Gestational age determination follows standard American College of Obstetricians and Gynecologists (ACOG) guidelines.
Key exclusion criteria (3 listed)
- Multiple Pregnancies
- Cases with fetal demise or other fetal abnormalities observed/suspected after the ultrasound examination
- Cases of planned diagnostic ultrasound follow-up exams within 2 weeks for known or suspected abnormality after the current ultrasound examination for the study
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Images and cines captured through the capture card (ICC)A screen capture/recording of the entire exam performed by the ARDMS as per the Image Acquisition Protocol for the FDA Study will be obtained, and the images/cines required for the study that correspond to IUS shall be obtained. The independent research coordinator for the study will review the screen recording and identify the frame/cine for each diagnostic view (ICC) that corresponds to IUS based on the time stamps. | Device | Images and cines captured on the ultrasound machine (IUS) |
Primary outcome
To assess whether the AI/ML technologies used in OMEA can achieve an acceptable sensitivity for identifying the diagnostic view
Time frame: 11 weeks 0 days to 13 weeks 6 days
The overall sensitivity and two-sided 95% confidence interval (CI) will be determined using data pooled across diagnostic views. Note: The participant is assessed for the primary outcome on the same day of enrollment.
To assess whether the AI/ML technologies used in OMEA can achieve an acceptable sensitivity and consistency for verifying the quality criteria of a given image
Time frame: 11 weeks 0 days to 13 weeks 6 days
The overall sensitivity and two-sided 95% confidence interval (CI) will be determined using data pooled across diagnostic views for quality criteria. Note: The participant is assessed for the primary outcome on the same day of enrollment.
To evaluate the performance of the OMEA AI/ML technologies with respect to facilitating the determination of quantitative measure of crown-rump length (CRL) and nuchal translucency (NT), complementary evaluations of agreement will be performed.
Time frame: 11 weeks 0 days to 13 weeks 6 days
The agreement and consistency of the AI/ML technologies used in OMEA to obtain quantitative measurements (i.e., CRL and NT) compared to the MFM physician average will be analyzed by applying Deming regression and Bland-Altman analysis. Note: The participant is assessed for the primary outcome on the same day of enrollment.
1 secondary outcome
To assess the sensitivity and specificity for the detection of each of the individual diagnostic view of each of the individual quality criteria within each diagnostic view.
Time frame: 11 weeks 0 days to 13 week 6 days
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 21 Oct 2025.
- Background: PubMed 7564793Bland JM, Altman DG. Comparing methods of measurement: why plotting difference against standard method is misleading. Lancet. 1995 Oct 21;346(8982):1085-7. doi: 10.1016/s0140-6736(95)91748-9.
- Background: PubMed 2868172Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986 Feb 8;1(8476):307-10.
- Background: PubMed 4037671Payne RB. Deming's regression analysis in method comparison studies. Ann Clin Biochem. 1985 Jul;22 ( Pt 4):430. doi: 10.1177/000456328502200419. No abstract available.
- Background: PubMed 28426621Committee Opinion No 700: Methods for Estimating the Due Date. Obstet Gynecol. 2017 May;129(5):e150-e154. doi: 10.1097/AOG.0000000000002046.
- Background: PubMed 34242884Ghelich Oghli M, Shabanzadeh A, Moradi S, Sirjani N, Gerami R, Ghaderi P, Sanei Taheri M, Shiri I, Arabi H, Zaidi H. Automatic fetal biometry prediction using a novel deep convolutional network architecture. Phys Med. 2021 Aug;88:127-137. doi: 10.1016/j.ejmp.2021.06.020. Epub 2021 Jul 6.
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- The Sustainable Development Goals (SDGs; Goal 3) and the United Nations Global Strategy for Women's, Children's, and Adolescents' Health targeting reduction in the global maternal mortality ratio.Link provided by the sponsor.
- The Sustainable Development Goals (SDGs; Goal 3) and the United Nations Global Strategy for Women's, Children's, and Adolescents' Health targeting reduction in the global maternal mortality ratio.Link provided by the sponsor.
- Individual participant dataSharing planned: No. The Individual Participant Data (IPD) from this FDA study will remain confidential and will not be shared with other researchers due to participant privacy protections and the proprietary nature of the study.
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Jeroen Peter Vanderhoeven, MD principal investigator
Providence Swedish Medical Center
15 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of To Validate the Clinical Performance of AI/ML Technologies in First-trimester Pregnant Participants and First Trimester Pregnancy, run by Origin Medical Systems, Inc. at 15 sites in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 7 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 21 Oct 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 15 sites in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
To assess whether the AI/ML technologies used in OMEA can achieve an acceptable sensitivity for identifying the diagnostic view (time frame: 11 weeks 0 days to 13 weeks 6 days). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06952439, which also lists 15 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 21 Oct 2025.