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

NCT07430358 | Not Yet Recruiting DMC

• 1 other identifier: 0335-25- HMO-CTIL
• Study Type: interventional
• Target: 400
• Locations: 0 countries
• Sites: N/A

Brief 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. 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.

Conditions

Labor, Obstetric; Pregnancy; Cesarean Section Rate

Outcome Measures

Primary Outcomes (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.

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

Secondary Outcomes (12)

• Postpartum Hemorrhage

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

• Maternal ICU Admission

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

• Chorioamnionitis

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

• Advanced Perineal Tear

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

• Length of Maternal Hospitalization

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

Other Outcomes (1)

• Decision Latency to Unplanned Cesarean Delivery

  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).

Study Arms (2)

Dashboard Group

EXPERIMENTAL

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.

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

Control group

NO INTERVENTION

Participants randomized to the control arm will receive standard intrapartum obstetric care

Interventions

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

Dashboard Group

Eligibility Criteria

• Age: 18 Years+
• Sex: female
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• Hadassah Medical Organization: lead
• Israel Innovation Authority: collaborator

Related Publications (12)

• 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.

  PMID: 38517902 BACKGROUND

• 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.

  PMID: 28157275 BACKGROUND

• 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.

  PMID: 32434000 BACKGROUND

• 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.

  PMID: 35752169 BACKGROUND

• 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.

  PMID: 28213060 BACKGROUND

• 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.

  PMID: 40527278 BACKGROUND

• 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.

  PMID: 31587401 BACKGROUND

• 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.

  PMID: 33713380 BACKGROUND

• 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.

  PMID: 37164488 BACKGROUND

• 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.

  PMID: 34235291 BACKGROUND

• 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.

  PMID: 24252712 BACKGROUND

• 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.

  PMID: 30983383 BACKGROUND

Related 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 Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: DOUBLE
• Who Masked: PARTICIPANT, OUTCOMES ASSESSOR
• Purpose: SUPPORTIVE CARE
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Dr.

Study Record Dates

• First Submitted: December 16, 2025
• First Posted: February 24, 2026
• Study Start: March 1, 2026
• Primary Completion (Estimated): December 30, 2026
• Study Completion (Estimated): April 30, 2027
• Last Updated: February 24, 2026

Record last verified: 2026-02