Background: The prevention of ventilator-induced lung injury (VILI) is an important topic in criticalcare. Lung overdistention is a great attributor to VILI. Regional evaluation of the pulmonary mechanicsis a relatively new method and is currently being studied using electrical impedance tomography (EIT)and computed tomography (CT). However, ultrasonography may be an effective alternative tomeasure lung overdistention by quantifying lung sliding. Objective: The primary aim of this study was to explore if lung sliding can be quantified using in-housemade speckle tracking algorithms. Second, the usability of lung sliding quantification to detect lungoverdistention was investigated. Methods: Two speckle tracking algorithms were built and validated to quantify lung sliding. Adultpatients admitted to the ICU were prospectively examined using ultrasound. Ultrasound images wereanalyzed offline using the in-house developed Fourier-Based Speckle Tracking algorithm (FBST) andthe Intensity-Based Speckle Tracking algorithm (IBST). The performance of the best algorithm wasvalidated against the manual evaluation of lung sliding by experts and validated against lungcompliance. Besides, the performance of the best algorithm was investigated by reproducibilitytesting. Finally, we tested the algorithm for its ability to detect lung overdistention. Results: The FBST algorithm could not reliably quantify lung sliding and was not further evaluated.However, the IBST algorithm showed an overall success rate of 88%. Moreover, the IBST algorithmdifferentiated normal and moderate lung sliding with a mean IBST score of 12.1 and 29, respectively(p<0.001). A positive correlation was measured between IBST score and lung compliance (β 0.15, SE0.025, 95% CI 0.101-0.208, p <0.001). The reproducibility test calculated a relative standard error of2.7. Finally, a difference was found in IBST scores between patients with a PEEP value <12 and ≥12with a mean ± SD of 20.9 ± 7 and 12.6 ± 6, p < 0.05, respectively. Conclusion: The IBST algorithm enables a robust lung sliding quantification. The IBST scores correlatewell with expert opinion and lung compliance, but the reliability needs improvement. Our results showthat detection of lung overdistention using ultrasound is feasible. Further clinical studies should assessif ultrasonography can measure lung overdistention.

Background: The diaphragm is poorly monitored in the Intensive care unit (ICU), despite its evident importance in respiration. Ultrasound (US) is frequently employed to evaluate diaphragm thickness (DT) and diaphragm thickening factor (DTF) but requires expertise and only covers a small diaphragm area. Therefore, advanced US (post-)processing techniques are being investigated for diaphragm applications, including speckle tracking methods. Objective: The primary aim of this pilot study was to develop an algorithm enabling DT quantification. Second, both an existing Fourier-based (FBST) and intensity-based speckle tracking (IBST) algorithm were modified to determine their feasibility in diaphragm strain quantification. Results: Minimal (DTmin, mm) and maximal DT (DTmax, mm), and DTF (%) values were 1.9 ± 0.4, 2.3 ± 0.5, and 22.6 ± 10.9 in the MV patient group, and 2.2 ± 0.4, 3.7 ± 1.5 and 66.5 ± 45.0 in the volunteer group (all p > 0.05). Correlation of DT algorithm variables and manual expert grading were allsignificant, with a good correlation in DTmax (ICC 0.9, r or ρ 0.7, p < 0.001) and a moderate correlation in DTmin and DTF (ICC 0.7, r or ρ 0.7, p < 0.001 and p = 0.001, respectively). Inter- and intra-rater reproducibility of manual DT assessment was poor (ICC < 0.4 and r or ρ < 0.2). GLS (%) and GLSR (%/s) values were -32.0 ± 19.8 and -6.6 ± 3.8 in the patient group and -40.3 ± 17.3 and -10.4 ± 6.8 in the volunteer group, respectively (all p > 0.05). IBST scores were 26.2 ± 17.6 in the patient group and 68.1 ± 32.5 in the volunteer group (p = 0.009). IBST score values showed a good correlation with DTF (r or ρ 0.7, p = 0.003), and a moderate, negative correlation with DTmin (r or ρ -0.5, p = 0.043). No significant correlations were seen between the remaining manual expert DT assessment and algorithm-derived FBST and IBST variables.Conclusion: DT quantification using the algorithm developed during this study correlated to conventional US expert assessment. Poor reproducibility of current diaphragm function quantification supports the need for such an automated assessment. The clinical value of diaphragm strain assessment using the modified FBST and IBST algorithms remains unclear. Further research involving a widely defined gold standard technique in diaphragm function quantification is warranted.

Introduction Patient-ventilator asynchrony (PVA) poses a significant challenge in the management of mechanically ventilated patients, contributing to adverse clinical outcomes. Current methods of detecting PVA rely on visual assessment by clinicians, leading to subjectivity and inconsistency. Therefore, there is a need for automated techniques to identify PVA accurately and efficiently. In this study, we explore the application of supervised and unsupervised machine learning algorithms to develop an automatic detection system for PVA. Methods This study was conducted at the ICU of the LUMC in Leiden, the Netherlands. Patients eligible for inclusion were mechanically ventilated with an esophageal balloon inserted. Data collected included flow, Paw, and Pes curves, which were labelled using an open-source data labeling platform and processed in Python. Supervised CNN models were trained for different ventilation modes, while unsupervised techniques, utilizing Mahalanobis distance, were explored for data pre-labeling. The discriminative capability of the models was assessed using AUROC values. Results 25 patients were included in this study and labelled by clinicians. Using an unsupervised machine learning technique based on the Mahalanobis distance for data pre-labeling, a threshold of 3.5 was selected, resulting in a 95% accuracy in correctly identifying normal breaths. Creating different CNN models for automating the detection of PVA the results demonstrate the discriminative capability of the various models across all ventilation modes, PSV and PCV ventilation. They can differentiate between normal and abnormal breaths, as indicated by the AUROC values of 0.85(±0.08), 0.83 (±0.12), and 0.80 (±0.28) respectively. Discussion This study investigated the application of machine learning techniques to analyse ventilation data in critical care settings. Through a combination of unsupervised and supervised learning methods, we have explored the automation of the labelling process and the development of predictive models for identifying patient-ventilator asynchronies (PVAs).