Clinical Performance Evaluation of the Artificial Intelligence (AI)/ Machine Learning (ML) Technologies Utilized by the Origin Medical EXAM ASSISTANT

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.

Study Overview

• Status: Completed
• Conditions: First Trimester Pregnancy; To Validate the Clinical Performance of AI/ML Technologies in First-trimester Pregnant Participants
• Intervention / Treatment: Device: Images and cines captured through the capture card (ICC)

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:

1. Automated detection of standard diagnostic views in accordance with practice guidelines;

2. 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:

   1. Presence/Absence of anatomical landmarks/structures identified within the diagnostic views detected
   2. Verification of imaging parameters (e.g., magnification)
   3. Verification of clinical features (e.g., orientation of the fetus)
3. 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:

1. Fetal gestational age
2. Maternal age
3. Maternal BMI
4. Race/Ethnicity
5. Confirmation of diagnosis of fetal anomaly/syndrome prior to the study exam and post the study exam

Site Details:

1. Site location
2. Sonographer name
3. 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:

1. Automated detection of standard diagnostic views;
2. Automated verification of quality criteria required for interpretation of diagnostic views ;
3. 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 Type: Observational
• Enrollment (Actual): 551

Contacts and Locations

Study Locations

• United States
  • California
    • Apple Valley, California, United States, 92307
      • Harbinder S Brar MD Inc
    • Indio, California, United States, 92201
      • Harbinder S Brar MD Inc
    • Murrieta, California, United States, 92562
      • Harbinder S Brar MD Inc
    • Redlands, California, United States, 92374
      • Harbinder S Brar MD Inc
    • Riverside, California, United States, 92506
      • Harbinder S Brar MD Inc
    • San Bernardino, California, United States, 92404
      • Harbinder S Brar MD Inc
  • Louisiana
    • New Orleans, Louisiana, United States, 70002
      • Sweet Pea 3D/4D Ultrasound Nola
  • New York
    • Troy, New York, United States, 12180
      • Mobile Mama Ultrasound, LLC
  • North Carolina
    • Raleigh, North Carolina, United States, 27607
      • Mid-Carolina OB/GYN
  • Oklahoma
    • Norman, Oklahoma, United States, 73072
      • The Nest 4D Ultrasound LLC, DBA InFocus Ultrasound DBA Little Peanut 4D Ultrasound
    • Oklahoma City, Oklahoma, United States, 73106
      • The Nest 4D Ultrasound LLC, DBA InFocus Ultrasound DBA Little Peanut 4D Ultrasound
    • Owasso, Oklahoma, United States, 74055
      • Tiny Blessings Ultrasound 4D Studio
    • Skiatook, Oklahoma, United States, 74070
      • Tiny Blessings Ultrasound 4D Studio
  • Texas
    • Houston, Texas, United States, 77018
      • Total Womens Care PLLC
    • Webster, Texas, United States, 77598
      • Reveal Ultrasound, LLC-S

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Probability Sample

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.

Description

Inclusion Criteria:

1. Maternal age ≥ 18 years
2. BMI < 40 kg/m2
3. Live non-anomalous singleton pregnancies

4. 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.

5. Informed consent is obtained from the participant
6. Exams obtained as per the Image Acquisition Protocol

Exclusion Criteria:

1. Multiple Pregnancies
2. Cases with fetal demise or other fetal abnormalities observed/suspected after the ultrasound examination
3. Cases of planned diagnostic ultrasound follow-up exams within 2 weeks for known or suspected abnormality after the current ultrasound examination for the study

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

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: Intrauterine fetal demise; Multiple gestations; Incorrect GA identified; Sonographer identifies structural abnormalities; 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.

Device: 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	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.	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.	11 weeks 0 days to 13 weeks 6 days

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	11 weeks 0 days to 13 week 6 days

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
Evaluate the inter-observer agreement in the quantitative values of CRL and NT among the MFM physicians reading panel.	We will analyze the inter-rater agreement in the quantitative values of CRL and NT among the three (3) MFM physicians during clinical benchmarking.	11 weeks 0 days to 13 weeks 6 days

Collaborators and Investigators

• Sponsor: Origin Medical Systems, Inc.
• Investigators: Principal Investigator: Jeroen Peter Vanderhoeven, MD, Providence Swedish Medical Center

Publications and helpful links

General Publications

• Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986 Feb 8;1(8476):307-10.
• Committee Opinion No 700: Methods for Estimating the Due Date. Obstet Gynecol. 2017 May;129(5):e150-e154. doi: 10.1097/AOG.0000000000002046.
• Akkus 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.
• 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.
• Payne 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.
• Ghelich 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.
• Matthew 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.
• He 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.
• Xiao 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.
• Siddique 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.
• Kurjak 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.
• Walsh CA, McAuliffe F, Kinsella V, McParland P. Routine obstetric ultrasound services. Ir Med J. 2013 Nov-Dec;106(10):311-3.
• International 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.
• AIUM 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.
• Salomon 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.

Helpful Links

• 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.
• 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.

Study record dates

Study Major Dates

• Study Start (Actual): March 19, 2025
• Primary Completion (Actual): May 19, 2025
• Study Completion (Actual): May 19, 2025

Study Registration Dates

• First Submitted: April 9, 2025
• First Submitted That Met QC Criteria: April 23, 2025
• First Posted (Actual): April 30, 2025

Study Record Updates

• Last Update Posted (Estimated): October 21, 2025
• Last Update Submitted That Met QC Criteria: October 17, 2025
• Last Verified: October 1, 2025

More Information

Terms related to this study

• Keywords: Artificial Intelligence; Machine Learning; Ultrasound Imaging; First Trimester Pregnancy
• Other Study ID Numbers: OMS-US-EA-FDA-CPA-01; 03/13/2025 (Other Identifier: WCG IRB)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: 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.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: Yes
• product manufactured in and exported from the U.S.: No