Evaluation of AI Models in Determining the Optimal PEEP

Patients receiving mechanical ventilation support in the intensive care unit (ICU), who are hemodynamically stable and over 18 years old, will be considered eligible for the study. These criteria have been set to ensure that the patients require adequate respiratory support while being suitable for individualized ventilator management optimization.

During the patient selection process, the medical records of ICU patients will be reviewed, and those meeting the criteria will be included in the study. To ensure the accuracy of clinical data, the patient selection process will be carried out by intensive care specialists.

The mechanical ventilation parameters of the included patients will be systematically recorded. In this context, inspiratory hold and expiratory hold maneuvers will be applied to evaluate the respiratory mechanics of the patients. The recorded measurements will include compliance (lung elasticity), elastance (lung stiffness), auto-PEEP (presence of air trapping), and time constant (gas exchange duration).

In addition to these fundamental ventilation parameters, the pressure-volume (P-V) curve will be analyzed to collect critical data for ventilation optimization. These procedures will be performed using advanced analysis modules integrated into ICU ventilators and patient monitoring systems. The collected data will be analyzed both manually by experts and by artificial intelligence (AI) models throughout the study.

In the study, PEEP titration will be performed manually by intensive care specialists. This process will be applied immediately after intubation for intubated patients and will continue daily. The procedure will be standardized according to a predefined protocol and applied consistently across all patients.

The lower inflection point (LIP) will be identified, and PEEP will be applied at the point where alveolar recruitment begins. This step is critical to preventing lung collapse and ensuring optimal oxygenation. Additionally, the upper inflection point (UIP) will be determined to prevent overdistension, ensuring that tidal volume remains below this pressure threshold. If the lower inflection point is not clearly identified, a PEEP level of approximately 10 cmH₂O will be applied as a standard approach.

The pressure-volume curve of the ventilator will be analyzed to assess whether the optimal PEEP level has been achieved. If the curve shifts upward, it will be considered an indicator of successful alveolar recruitment. If the curve shifts to the right, it will indicate a risk of overdistension, prompting a reduction in PEEP. By this approach, the most suitable PEEP level for each patient will be determined, and ventilator settings will be optimized accordingly. These procedures are routinely applied in our hospital and globally and are among the most commonly preferred methods for PEEP titration.

All collected ventilation parameters will be input into three different artificial intelligence models: ChatGPT, DeepSeek, and Gemini. These systems will be asked to provide:

An optimal PEEP level recommendation for the patient The ability to detect abnormal ventilation parameters Three possible diagnostic suggestions Three proposed treatment approaches The data provided by the AI models will be recorded and compared with manual assessments performed by experienced ICU specialists. This comparison aims to determine the extent to which AI models can serve as clinical decision support tools. The reliability, accuracy, and clinical applicability of AI-generated recommendations will be thoroughly analyzed.

In the final phase of the study, the PEEP levels determined by AI models will be compared with those identified by ICU specialists with at least five years of experience. The following criteria will be evaluated:

Agreement between AI models and expert opinions on PEEP levels (primary outcome measure) Sensitivity and specificity of AI in detecting abnormal ventilation parameters Accuracy of AI-generated diagnoses Clinical applicability of AI-recommended treatment approaches

• Sensitivity and Specificity of AI Models in Detecting Abnormal Ventilation Parameters [Срок оценки: 7 days]
• Accuracy of AI-Generated Diagnostic Predictions [Срок оценки: 7 days]
• Clinical Applicability of AI-Generated Treatment Recommendations [Срок оценки: Within the first 24 hours of mechanical ventilation.]