Evaluation of AI Models in Determining the Optimal PEEP

Condition(s) studied

Investigational drug(s) / intervention(s)

AI-Assisted PEEP Optimization: In this study, three artificial intelligence (AI) models (ChatGPT, DeepSeek, and Gemini) will analyze mechanical ventilation data, including compliance, elastance, auto-PEEP, time constant, and pressure-volume (P-V) curves, to generate patient-specific PEEP recommendations. These AI-generated recommendations will be compared with manual PEEP titration performed by experienced ICU specialists. The AI models will also provide abnormal ventilation parameter detection, diagnostic suggestions, and treatment recommendations. The study aims to evaluate the reliability, accuracy, and clinical applicability of AI-generated outputs in optimizing PEEP settings for mechanically ventilated ICU patients.

Study summary

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.

Eligibility

Primary outcome measure(s)

• Agreement Between AI-Generated and Expert-Determined PEEP Levels — 7 day
  The primary outcome will be the agreement between the PEEP levels recommended by AI models (ChatGPT, DeepSeek, Gemini) and those determined manually by ICU specialists. Agreement will be assessed using Bland-Altman analysis and intraclass correlation coefficient (ICC) to measure consistency

Trial sites (1)

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