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

Background/Objectives: This study presents an artificial intelligence (AI)-supported incentive spirometry system designed to explore the feasibility of automating the monitoring of respiratory exercises, a critical nursing intervention for maintaining pulmonary function and reducing postoperative complications. Methods: This system uses a tablet's camera to track a standard spirometer's volume indicator in real-time, reducing the manual nursing workload, unlike traditional mechanical spirometers that lack feedback capabilities. 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". Results: The ensemble methods demonstrated exceptional performance, achieving 100% accuracy and R² = 1.0, with cross-validation mean accuracies exceeding 99.4%. This feasibility study demonstrates the technical viability of this AI-driven approach and lays the groundwork for future clinical validation. Conclusions: This system presents a potential 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. This pilot study demonstrates the technical feasibility and potential of this AI-driven approach, laying the groundwork for future clinical validation.