Innovative Approach in Nursing Care: Artificial Intelligence-Assisted Incentive Spirometry

This study presents an artificial intelligence (AI)-supported incentive spirometry system designed to automate the monitoring of respiratory exercises, a critical nursing intervention for maintaining pulmonary function and reducing postoperative complications. Unlike traditional mechanical spirometers that lack feedback capabilities, this system uses a tablet’s camera to track a standard spirometer’s volume indicator in real-time, reducing the manual nursing workload. Image processing techniques analyze exercise performance, while the interface provides instant feedback, data recording, and graphical display. Machine learning models (Random Forest, XGBoost, Gradient Boosting, SVM, Logistic Regression, KNN) were trained on scripted patient data, including demographics, smoking status, and spirometry measurements, to classify respiratory performance as “poor”, “good”, or “excellent”. The ensemble methods demonstrated exceptional performance, achieving 100% accuracy and R² = 1.0, with cross-validation mean accuracies exceeding 99.4%. This AI-driven approach mitigates the monitoring burden in nursing care and aims to improve patient compliance. The system offers a cost-effective, accessible solution suitable for both clinical and home settings, potentially integrating into standard respiratory care protocols. This system not only reduces nursing workload but also has the potential to improve patient adherence and decrease postoperative pulmonary complications, aligning with global healthcare digitalization strategies.