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Labor, ObstetricORACLE-AINCT07430358

Obstetric Risk Assessment & Cesarean-delivery in Labor Estimation Using Artificial Intelligence

A study testing Software-based, real-time AI dashboard providing continuous risk estimates for unplanned cesarean delivery during labor. in people with Labor, Obstetric and Pregnancy, run by Hadassah Medical Organization at 1 site in Israel.

Recruiting

Looking for participants now. Contact the study team or a site below to ask about taking part.

Sites and contacts
Official record
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Trial summary

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. 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
Labor, Obstetric, Pregnancy, Cesarean Section Rate
What is being tested?
Software-based, real-time AI dashboard providing continuous risk estimates for unplanned cesarean delivery during labor.
Type of treatment
Medical device
Could I receive a placebo?
No, everyone receives an active treatment
How many people
400 participants
Where
1 siteIsrael
Study dates
19 May 2026to 30 Apr 2027
Phase
Interventional
Are there results?
Not yet: the study is under way

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

Who may be able to join

  • 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
Show all 9Show fewer
  • 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

Who cannot join

  • 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)
Show all 10Show fewer
  • 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

Trial locations and contacts

1 site in 1 country. Contact the study team or a site near you to ask about taking part.

Israel1 site, 1 recruiting

  • Hadassah Mt. Scopus Hebrew University Medical Center

    Jerusalem, Israel, 9308810

    Yishai Sompolinsky, MD MPH, 0546288294, ysompo@hadassah.org.il

    Recruiting

Other trials in Israel

About this study

Official title: 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\&ndash;delivery rates (uCD), with potential secondary benefits in maternal and neonatal outcomes.

Being tested
Software-based, real-time AI dashboard providing continuous risk estimates for unplanned cesarean delivery during labor.

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

  • Software-based, real-time AI dashboard providing continuous risk estimates for unplanned cesarean delivery during labor.Device

    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

Study groups in NCT07430358
Study groupRoleWhat they receive
Dashboard GroupExperimentalParticipants randomized to the intervention arm will receive standard intrapartum obstetric care with the addition of the ORACLE-AI real-time clinical decision-support dashboard.
Receives: Device: Software-based, real-time AI dashboard providing continuous risk estimates for unplanned cesarean delivery during labor.
Control groupNo interventionParticipants randomized to the control arm will receive standard intrapartum obstetric care

What is being measured

The primary outcome is the main question the study is designed to answer.

  1. Primary Endpoint: unplanned cesarean delivery rates.

    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.

    Measured: From randomization at labor admission to delivery (time of birth), up to 7 days.

Secondary outcomes (12)

  1. Postpartum Hemorrhage

    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.

    Measured: From delivery (time of birth) through maternal hospital discharge, up to 30 days.

  2. Maternal ICU Admission

    Admission to a maternal intensive care unit following delivery.

    Measured: From delivery (time of birth) through maternal hospital discharge, up to 30 days.

  3. Chorioamnionitis

    Clinical or histologic diagnosis of chorioamnionitis documented in the electronic health record.

    Measured: From randomization at labor admission through maternal hospital discharge, up to 30 days.

  4. Advanced Perineal Tear

    Third- or fourth-degree perineal laceration documented at delivery.

    Measured: At delivery (time of birth), within 7 days of randomization.

  5. Length of Maternal Hospitalization

    Total length of maternal hospital stay in days, calculated from delivery (time of birth) to maternal hospital discharge.

    Measured: From delivery (time of birth) through maternal hospital discharge, up to 30 days.

  6. Maternal mortality

    Death of the mother prior to hospital discharge.

    Measured: From delivery (time of birth) through maternal hospital discharge, up to 30 days.

  7. Neonatal Mortality

    Death of the neonate prior to hospital discharge.

    Measured: From birth through neonatal hospital discharge, up to 30 days.

  8. Low Apgar Score

    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.

    Measured: At 1 minute and 5 minutes after birth.

  9. Umbilical Cord Arterial pH < 7.10

    Umbilical arterial blood pH less than 7.10 measured at birth.

    Measured: At birth.

  10. Neonatal Intensive Care Unit Admission

    Admission of the neonate to a neonatal intensive care unit.

    Measured: From birth through neonatal hospital discharge, up to 30 days.

  11. Neonatal Mechanical Ventilation

    Requirement for invasive mechanical ventilation during the neonatal hospitalization.

    Measured: From birth through neonatal hospital discharge, up to 30 days.

  12. Length of Neonatal Hospitalization

    Total length of neonatal hospital stay in days, calculated from birth to neonatal hospital discharge.

    Measured: From birth through neonatal hospital discharge, up to 30 days.

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

  1. First posted24 Feb 2026
  2. Study start19 May 2026 (estimated)
  3. Primary completion30 Dec 2026 (estimated)
  4. Study completion30 Apr 2027 (estimated)

Resources

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design
Study type
Interventional
Phase
Phase N/A
Primary purpose
Supportive care
Allocation
Randomised
Intervention model
Parallel groups
Masking
Double blind
Who is masked
Participant, Outcomes assessor
Enrolment
400 participants (estimated)
IDs, dates and publications
ClinicalTrials.gov ID
NCT07430358
Sponsor's protocol ID
0335-25- HMO-CTIL
Acronym
ORACLE-AI
Status last verified
May 2026
First submitted
16 Dec 2025
Last updated
22 May 2026
Results posted
No
Data monitoring committee
Yes
FDA-regulated drug
No
FDA-regulated device
No

Publications

  • 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.

    PubMed 38517902

  • 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.

    PubMed 28157275

  • 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.

    PubMed 32434000

  • 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.

    PubMed 35752169

  • 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.

    PubMed 28213060

  • 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.

    PubMed 40527278

  • 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.

    PubMed 31587401

  • 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.

    PubMed 33713380

  • 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.

    PubMed 37164488

  • 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.

    PubMed 34235291

  • 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.

    PubMed 24252712

  • 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.

    PubMed 30983383

Questions about this trial

What is this clinical trial studying?

A study testing Software-based, real-time AI dashboard providing continuous risk estimates for unplanned cesarean delivery during labor. in people with Labor, Obstetric and Pregnancy, run by Hadassah Medical Organization at 1 site in Israel.

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 9 inclusion and 10 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: looking for participants now, as of the sponsor's last update on 22 May 2026. Status can change between updates, so confirm with the study contact before making plans.

Where is the study taking place?

The record lists 1 site in Israel. 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?

This study is still under way. Results are not available in the source record yet.

How can a doctor refer a patient or contact the research team?

Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.

What does the primary outcome measure?

Primary Endpoint: unplanned cesarean delivery rates. (time frame: From randomization at labor admission to delivery (time of birth), up to 7 days.). 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 NCT07430358, which also lists 12 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 22 May 2026.

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