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NCT06844916
Evaluation of AI Models in Determining the Optimal PEEP
trial testing AI-Assisted PEEP Optimization in PEEP, Occult in 145 participants. Currently enrolling.
1 February 2026
Quick facts
| Lead sponsor | Kanuni Sultan Suleyman Training and Research Hospital |
|---|---|
| Status | Recruiting now |
| Study type | OBSERVATIONAL |
| Enrollment | 145 |
| Start date | 1 March 2025 |
| Primary completion | 1 February 2026 |
| Estimated completion | 2 February 2026 |
| Sites | 1 location across Turkey (Türkiye) |
Drugs / interventions tested
- AI-Assisted PEEP Optimization
Conditions studied
- PEEP, Occult — all drugs for PEEP, Occult →
Sponsor
Kanuni Sultan Suleyman Training and Research Hospital
Who can join
18 and older, any sex, with PEEP, Occult. Patients with the condition only — healthy volunteers not accepted.
Sponsor's own description
his study is designed as a prospective observational clinical trial. Patients over 18 years old who are hemodynamically stable and require mechanical ventilation in the Intensive Care Unit (ICU) will be included. The inclusion criteria ensure that participants require individualized ventilatory optimization. The study will involve a comparison between Artificial Intelligence (AI)-generated Positive End-Expiratory Pressure (PEEP) recommendations and expert-determined PEEP levels. ICU specialists will perform PEEP titration manually based on standardized protocols, identifying the lower inflection point (LIP) and upper inflection point (UIP) to optimize ventilation. The pressure-volume (P-V) curve will be analyzed to ensure optimal alveolar recruitment and prevent overdistension. Study Procedures Participants will: Undergo systematic mechanical ventilation assessments, including inspiratory hold and expiratory hold maneuvers, compliance, elastance, auto-PEEP, and time constant evaluations. Have ventilation data collected and analyzed using three AI models: ChatGPT, DeepSeek, and Gemini. Receive AI-generated recommendations regarding optimal PEEP levels, abnormal ventilation parameters, and potential treatment suggestions. Have their AI-based PEEP recommendations compared with those determined by ICU specialists with at least five years of experience. Outcome Measures The study will compare AI and expert assessments based on the following primary and secondary measures: Primary Outcome: Agreement between AI-generated PEEP levels and expert-determined PEEP levels. Secondary Outcomes: AI sensitivity and specificity in detecting abnormal ventilation parameters. Accuracy of AI-generated diagnoses. Clinical applicability of AI-recommended treatment strategies. This study aims to determine whether AI models can serve as reliable clinical decision support tools for ventilator management in ICU patients.
Publications & conference data
No peer-reviewed publications indexed yet for this trial.
Verify or expand the search:
- PubMed search for NCT06844916
- Europe PMC full search
- ASCO Meeting Library
- ESMO Meeting Library
- bioRxiv preprints
- medRxiv preprints
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Related trials
Other recruiting trials for PEEP, Occult
Currently open trials in the same condition.
- NCT06836830 — PEEP vs. ZEEP in Out-of-Hospital-Cardiac-Arrest · NA · recruiting
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Trials by the same sponsor.
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Verify against primary sources
- ClinicalTrials.gov — authoritative US registry record
- WHO ICTRP — international registry index
- EU Clinical Trials Register
- Sponsor press releases (Google)
- Trial protocol + status: ClinicalTrials.gov NCT06844916 (US National Library of Medicine, public domain)
- Drug + disease cross-links: matched in real time against Drug Landscape's normalised drug + company + condition tables
- Sponsor: as reported to ClinicalTrials.gov by Kanuni Sultan Suleyman Training and Research Hospital
- Last refreshed: 4 June 2025
Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT06844916.
Primary sources · FDA · ClinicalTrials.gov · EMA · SEC EDGAR · ChEMBL · Wikidata · full sourcing