## 4 Discussion
For instance, combining respiratory mechanic parameters with continuous monitoring data from EQV sensors could provide additional information to clinicians with respect to changes in lung function, potentially indicating the onset or progression of respiratory conditions. Based on this information, clinicians can decide if a FOT measurement is also needed to further investigate and confirm the observed changes in lung mechanics or to adjust treatment plans accordingly. The results of this study suggest the advantages of using a multi-faceted approach to assess respiratory mechanics. By integrating FOT measurements with continuous EQV monitoring, a more comprehensive picture of lung function can be obtained. This approach not only improves the accuracy of respiratory assessments but also provides valuable insights into the dynamic behavior of the respiratory system under various conditions. Hence, the integration of low-frequency FOT with continuous EQV sensor monitoring, coupled with the application of AI tools, offers a promising framework for enhancing respiratory mechanic assessment. Future research should focus on validating these findings in larger and more diverse populations, exploring the potential of AI-driven predictive models, and refining the integration of these technologies for broader clinical applications. This comprehensive approach has the potential to significantly improve the diagnosis, monitoring, and management of lung diseases, ultimately leading to better patient outcomes. The protocol outlined in this paper involves rigorous and repetitive measurements. However, taking measurements over a two-hour period could be physically demanding and potentially exhausting for subjects with lung disease. AI analysis presents a promising solution to meet these challenges and can enhance the efficiency and accuracy of data collection, processing, and analysis, potentially reducing the need for prolonged measurement sessions. For instance, AI algorithms can predict respiratory mechanics by analyzing smaller sets of data and extrapolating results with high accuracy, which minimizes the physical strain on subjects. Recent studies have demonstrated the application of AI in predicting and forecasting respiratory conditions. For example, a study used AI algorithms to predict the optimal timing for weaning subjects from mechanical ventilation in respiratory care. This study employed various AI algorithms, including random forests, support vector machines (SVMs), and neural networks, to analyze 26 feature variables from 670 intubated subjects. The models achieved high accuracy, with the area under the receiver operating characteristic curve (AUC) ranging from 0.792 to 0.868. The AI intervention reduced the mean number of ventilator days by 0.5 [33]. Another study applied AI methods, specifically long short-term memory (LSTM) networks, to predict emergency room visits for respiratory diseases based on environmental and physiological data. The inputs included air quality indices, weather data, and patient historical health records. The LSTM model effectively forecasted respiratory health crises, demonstrating AI's ability to handle temporal data and provide early warnings for health interventions [34]. Furthermore, the use of deep learning techniques, such as convolutional neural networks (CNNs), to predict respiratory rates from biosignals has shown high accuracy. One study used CNNs to analyze photoplethysmography (PPG) signals, achieving an accuracy of over 90% in predicting respiratory rates. This approach underscores AI's potential in continuous respiratory monitoring, providing real-time insights into respiratory mechanics and enabling timely responses to any abnormalities [35]. The spectrograms herein produced offer image-based time-varying information for AI analysis at various time intervals of evaluations.