- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT07430358
Obstetric Risk Assessment & Cesarean-delivery in Labor Estimation Using Artificial Intelligence (ORACLE-AI)
May 20, 2026 updated by: Yishai Sompolinsky, Hadassah Medical Organization
Obstetric Risk Assessment & Cesarean-delivery in Labor Estimation Using Artificial Intelligence Trial (ORACLE-AI)
ORACLE-AI is a single-center, open-label, randomized clinical trial comparing primiparous women managed with a real-time machine-learning dashboard against a concurrent control group receiving standard intrapartum care.
Participants are randomized 1:1 at the onset of labor.
The intervention group has the AI dashboard visible in their electronic health record, while the control group does not.
The primary hypothesis is that the use of continuous AI-based risk estimates will be non-inferior to standard care in terms of unplanned cesarean–delivery rates (uCD), with potential secondary benefits in maternal and neonatal outcomes.
Study Overview
Status
Recruiting
Conditions
Study Type
Interventional
Enrollment (Estimated)
400
Phase
- Not Applicable
Contacts and Locations
This section provides the contact details for those conducting the study, and information on where this study is being conducted.
Study Locations
-
-
Israel
-
Jerusalem, Israel, Israel, 9308810
- Recruiting
- Hadassah Mt. Scopus Hebrew University Medical Center
-
Contact:
- Yishai Sompolinsky, MD MPH
- Phone Number: 0546288294
- Email: ysompo@hadassah.org.il
-
-
Participation Criteria
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Yes
Description
Inclusion Criteria:
- Age ≥ 18 years at the time of consent
- Able and willing to provide written informed consent
- Nulliparous (no prior birth ≥ 24 weeks' gestation)
- Singleton live pregnancy
- Cephalic (vertex) fetal presentation
- Gestational age ≥ 37+0 weeks
- Admitted to the labor ward in labor (cervical dilation ≥ 3 cm with regular contractions) or undergoing induction or augmentation of labor with intent to proceed to vaginal delivery
- Planned trial of labor (no scheduled or elective cesarean delivery)
- Receiving intrapartum care at Hadassah-Hebrew University Medical Center, Mount Scopus campus
Exclusion Criteria:
- Planned or elective cesarean delivery prior to labor admission
- Multifetal gestation
- Non-cephalic fetal presentation
- Gestational age < 37+0 weeks
- Major fetal anomaly expected to affect labor or neonatal management
- Contraindication to vaginal delivery (e.g., placenta previa, invasive placentation, prior uterine surgery precluding labor)
- Category III fetal heart rate tracing on admission requiring immediate delivery
- Maternal hemodynamic instability or other life-threatening condition necessitating urgent surgical or critical-care intervention
- Inability to provide informed consent due to cognitive impairment, intoxication, or other incapacity
- Concurrent participation in another interventional obstetric study that could confound outcomes or increase risk
Study Plan
This section provides details of the study plan, including how the study is designed and what the study is measuring.
How is the study designed?
Design Details
- Primary Purpose: Supportive Care
- Allocation: Randomized
- Interventional Model: Parallel Assignment
- Masking: Double
Arms and Interventions
Participant Group / Arm |
Intervention / Treatment |
|---|---|
|
Experimental: Dashboard Group
Participants randomized to the intervention arm will receive standard intrapartum obstetric care with the addition of the ORACLE-AI real-time clinical decision-support dashboard.
|
The intervention is a software-based, real-time clinical decision-support dashboard (ORACLE-AI) integrated into the electronic health record and used during intrapartum care.
The system continuously analyzes admission characteristics and dynamic labor data, including serial cervical examinations, uterine activity, and cardiotocography (CTG) annotations, to generate individualized estimates of the probability of unplanned cesarean delivery.
Risk estimates are updated automatically every 5-7 minutes and displayed as a continuous numeric percentage with a graphical time trend and 95% confidence intervals.
The dashboard is visible only to the clinical care team and is advisory in nature; it does not provide prescriptive recommendations or automated alerts, and it does not replace clinical judgment.
All obstetric management decisions, medications, and procedures follow standard institutional protocols at the discretion of the treating clinicians.
No drugs, implants, or additional procedures
|
|
No Intervention: Control group
Participants randomized to the control arm will receive standard intrapartum obstetric care
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Primary Endpoint: unplanned cesarean delivery rates.
Time Frame: From randomization at labor admission to delivery (time of birth), up to 7 days.
|
Unplanned cesarean delivery is defined as any cesarean delivery performed after the onset of labor or during induction of labor, in participants randomized to the study, excluding scheduled or elective cesarean deliveries.
The outcome is assessed from the time of randomization at labor admission through delivery and is recorded as a binary variable (yes/no) per participant, based on electronic health record documentation.
|
From randomization at labor admission to delivery (time of birth), up to 7 days.
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Postpartum Hemorrhage
Time Frame: From delivery (time of birth) through maternal hospital discharge, up to 30 days.
|
Occurrence of postpartum hemorrhage as documented in the electronic health record, defined by >500ml in vaginal delivery and >1000ml in cesarean delivery and/or hemodynamic instability requiring clinical intervention, and/or need for blood transfusion.
|
From delivery (time of birth) through maternal hospital discharge, up to 30 days.
|
|
Maternal ICU Admission
Time Frame: From delivery (time of birth) through maternal hospital discharge, up to 30 days.
|
Admission to a maternal intensive care unit following delivery.
|
From delivery (time of birth) through maternal hospital discharge, up to 30 days.
|
|
Chorioamnionitis
Time Frame: From randomization at labor admission through maternal hospital discharge, up to 30 days.
|
Clinical or histologic diagnosis of chorioamnionitis documented in the electronic health record.
|
From randomization at labor admission through maternal hospital discharge, up to 30 days.
|
|
Advanced Perineal Tear
Time Frame: At delivery (time of birth), within 7 days of randomization.
|
Third- or fourth-degree perineal laceration documented at delivery.
|
At delivery (time of birth), within 7 days of randomization.
|
|
Length of Maternal Hospitalization
Time Frame: From delivery (time of birth) through maternal hospital discharge, up to 30 days.
|
Total length of maternal hospital stay in days, calculated from delivery (time of birth) to maternal hospital discharge.
|
From delivery (time of birth) through maternal hospital discharge, up to 30 days.
|
|
Maternal mortality
Time Frame: From delivery (time of birth) through maternal hospital discharge, up to 30 days.
|
Death of the mother prior to hospital discharge.
|
From delivery (time of birth) through maternal hospital discharge, up to 30 days.
|
|
Neonatal Mortality
Time Frame: From birth through neonatal hospital discharge, up to 30 days.
|
Death of the neonate prior to hospital discharge.
|
From birth through neonatal hospital discharge, up to 30 days.
|
|
Low Apgar Score
Time Frame: At 1 minute and 5 minutes after birth.
|
Apgar score assessed at 1 minute and 5 minutes after birth.
The Apgar score ranges from 0 to 10, with higher scores indicating better neonatal condition.
The proportion of neonates with Apgar score ≤7 will be reported.
|
At 1 minute and 5 minutes after birth.
|
|
Umbilical Cord Arterial pH < 7.10
Time Frame: At birth.
|
Umbilical arterial blood pH less than 7.10 measured at birth.
|
At birth.
|
|
Neonatal Intensive Care Unit Admission
Time Frame: From birth through neonatal hospital discharge, up to 30 days.
|
Admission of the neonate to a neonatal intensive care unit.
|
From birth through neonatal hospital discharge, up to 30 days.
|
|
Neonatal Mechanical Ventilation
Time Frame: From birth through neonatal hospital discharge, up to 30 days.
|
Requirement for invasive mechanical ventilation during the neonatal hospitalization.
|
From birth through neonatal hospital discharge, up to 30 days.
|
|
Length of Neonatal Hospitalization
Time Frame: From birth through neonatal hospital discharge, up to 30 days.
|
Total length of neonatal hospital stay in days, calculated from birth to neonatal hospital discharge.
|
From birth through neonatal hospital discharge, up to 30 days.
|
Other Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Decision Latency to Unplanned Cesarean Delivery
Time Frame: From the first documented intrapartum triggering event during labor to surgical skin incision for unplanned cesarean delivery, occurring during the index hospitalization (up to 7 days after randomization).
|
Time interval, in minutes, from the first documented intrapartum triggering event (e.g., vaginal bleeding or non-reassuring cardiotocography) to skin incision for unplanned cesarean delivery, derived from electronic health record timestamps.
|
From the first documented intrapartum triggering event during labor to surgical skin incision for unplanned cesarean delivery, occurring during the index hospitalization (up to 7 days after randomization).
|
Collaborators and Investigators
This is where you will find people and organizations involved with this study.
Sponsor
Collaborators
Publications and helpful links
The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.
General Publications
- Hollins Martin CJ, Martin CR. Development and psychometric properties of the Birth Satisfaction Scale-Revised (BSS-R). Midwifery. 2014 Jun;30(6):610-9. doi: 10.1016/j.midw.2013.10.006. Epub 2013 Oct 24.
- Alfirevic Z, Devane D, Gyte GM, Cuthbert A. Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database Syst Rev. 2017 Feb 3;2(2):CD006066. doi: 10.1002/14651858.CD006066.pub3.
- Schepman A, Rodway P. Initial validation of the general attitudes towards Artificial Intelligence Scale. Comput Hum Behav Rep. 2020 Jan-Jul;1:100014. doi: 10.1016/j.chbr.2020.100014. Epub 2020 May 18.
- Huurnink JME, Blix E, Hals E, Kaasen A, Bernitz S, Lavender T, Ahlberg M, Oian P, Hoifodt AI, Miltenburg AS, Pay ASD. Labor curves based on cervical dilatation over time and their accuracy and effectiveness: A systematic scoping review. PLoS One. 2024 Mar 22;19(3):e0298046. doi: 10.1371/journal.pone.0298046. eCollection 2024.
- Guedalia J, Lipschuetz M, Novoselsky-Persky M, Cohen SM, Rottenstreich A, Levin G, Yagel S, Unger R, Sompolinsky Y. Real-time data analysis using a machine learning model significantly improves prediction of successful vaginal deliveries. Am J Obstet Gynecol. 2020 Sep;223(3):437.e1-437.e15. doi: 10.1016/j.ajog.2020.05.025. Epub 2020 May 17.
- Wong MS, Wells M, Zamanzadeh D, Akre S, Pevnick JM, Bui AAT, Gregory KD. Applying Automated Machine Learning to Predict Mode of Delivery Using Ongoing Intrapartum Data in Laboring Patients. Am J Perinatol. 2024 May;41(S 01):e412-e419. doi: 10.1055/a-1885-1697. Epub 2022 Jun 25.
- Burke N, Burke G, Breathnach F, McAuliffe F, Morrison JJ, Turner M, Dornan S, Higgins JR, Cotter A, Geary M, McParland P, Daly S, Cody F, Dicker P, Tully E, Malone FD; Perinatal Ireland Research Consortium. Prediction of cesarean delivery in the term nulliparous woman: results from the prospective, multicenter Genesis study. Am J Obstet Gynecol. 2017 Jun;216(6):598.e1-598.e11. doi: 10.1016/j.ajog.2017.02.017. Epub 2017 Feb 16.
- Wakefield BM, Zapf MA, Ende HB. Artificial intelligence in prediction of postpartum hemorrhage: a primer and review. Int J Obstet Anesth. 2025 Aug;63:104694. doi: 10.1016/j.ijoa.2025.104694. Epub 2025 Jun 2.
- Tsur A, Batsry L, Toussia-Cohen S, Rosenstein MG, Barak O, Brezinov Y, Yoeli-Ullman R, Sivan E, Sirota M, Druzin ML, Stevenson DK, Blumenfeld YJ, Aran D. Development and validation of a machine-learning model for prediction of shoulder dystocia. Ultrasound Obstet Gynecol. 2020 Oct;56(4):588-596. doi: 10.1002/uog.21878.
- Guedalia J, Sompolinsky Y, Novoselsky Persky M, Cohen SM, Kabiri D, Yagel S, Unger R, Lipschuetz M. Prediction of severe adverse neonatal outcomes at the second stage of labour using machine learning: a retrospective cohort study. BJOG. 2021 Oct;128(11):1824-1832. doi: 10.1111/1471-0528.16700. Epub 2021 Apr 15.
- Hamilton EF, Romero R, Tarca AL, Warrick PA. The evolution of the labor curve and its implications for clinical practice: the relationship between cervical dilation, station, and time during labor. Am J Obstet Gynecol. 2023 May;228(5S):S1050-S1062. doi: 10.1016/j.ajog.2022.12.005. Epub 2023 Mar 16.
- Skvirsky V, Taubman-Ben-Ari O, Hollins Martin CJ, Martin CR. Validation of the Hebrew Birth Satisfaction Scale - Revised (BSS-R) and its relationship to perceived traumatic labour. J Reprod Infant Psychol. 2020 Apr;38(2):214-220. doi: 10.1080/02646838.2019.1600666. Epub 2019 Apr 13.
Helpful Links
- link to pubmed abstract for this pmid 38517902
- link to pubmed abstract for this pmid 28157275
- link to pubmed abstract for this pmid 32434000
- link to pubmed abstract for this pmid 35752169
- link to pubmed abstract for this pmid 28213060
- link to pubmed abstract for this pmid 40527278
- link to pubmed abstract for this pmid 31587401
- link to pubmed abstract for this pmid 33713380
- link to pubmed abstract for this pmid 37164488
- link to pubmed abstract for this pmid 34235291
- link to pubmed abstract for this pmid 24252712
- link to pubmed abstract for this pmid 30983383
Study record dates
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.
Study Major Dates
Study Start (Estimated)
May 19, 2026
Primary Completion (Estimated)
December 30, 2026
Study Completion (Estimated)
April 30, 2027
Study Registration Dates
First Submitted
December 16, 2025
First Submitted That Met QC Criteria
February 22, 2026
First Posted (Actual)
February 24, 2026
Study Record Updates
Last Update Posted (Actual)
May 22, 2026
Last Update Submitted That Met QC Criteria
May 20, 2026
Last Verified
May 1, 2026
More Information
Terms related to this study
Other Study ID Numbers
- 0335-25- HMO-CTIL
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
No
Studies a U.S. FDA-regulated device product
No
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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