AI-Enabled Framework for Automated Pendelluft Detection in Critically Ill Patients Using Electrical Impedance Tomography

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Abstract

Importance : While electrical impedance tomography (EIT) provides real-time monitoring of regional lung ventilation, its clinical implementation is hindered by complex data interpretation. We developed and validated a ChatGPT-generated system for automated pendelluft detection. Objective :To develop and validate an automated system for pendelluft detection using ChatGPT-generated architecture, with focus on real-time processing capability and clinical applicability. Design and Setting:A retrospective multicenter study conducted at three tertiary hospitals in China between January 2020 and December 2024.Participants:Among 458 screened patients, 278 mechanically ventilated patients met the inclusion criteria and were included for the final analysis. Main Outcomes and Measures : The proposed ChatGPT- generated system was developed using a three-component architecture: a modified ResNet-50 backbone for feature extraction, a bidirectional LSTM network (256 hidden units) with an 8-head self-attention layer for temporal analysis, and optimization for 32×32 EIT image processing. Primary outcomes included diagnostic accuracy (sensitivity, specificity, and AUC-ROC) and time efficiency. Secondary outcomes included correlation between pendelluft severity and clinical outcomes. Results : TheChatGPT-generated system achieved superior diagnostic performance (AUC 0.91, 95% CI: 0.87–0.95) compared to conventional machine learning approaches (CNN: 0.85, Random Forest: 0.82, SVM: 0.79). Analysis time decreased from 12.5 minutes (expert review) to 2.8 minutes (ChatGPT). Higher pendelluft grades correlated significantly with increased 28-day mortality (25.4% vs 9.0%, adjusted HR 2.8, 95% CI: 1.6–4.9, P = 0.001). Conclusions and Relevance : OurChatGPT-generated system provides an accurate, efficient solution for automated EIT analysis, potentially enabling real-time clinical decision support in mechanical ventilation management.

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