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Neonatal endotracheal intubation (ETI) is an important, complex resuscitation skill, which requires a significant amount of practice to master. Current ETI practice is conducted on the physical manikin and relies on the expert instructors' assessment. Since the training opportunities are limited by the availability of expert instructors, an automatic assessment model is highly desirable. However, automating ETI assessment is challenging due to the complexity of identifying crucial features, providing accurate evaluations and offering valuable feedback to trainees. In this paper, we propose a dilated Convolutional Neural Network (CNN) based ETI assessment model, which can automatically provide an overall score and performance feedback to pediatric trainees. The proposed assessment model takes the captured kinematic multivariate time-series (MTS) data from the manikin-based augmented ETI system that we developed, automatically extracts the crucial features of captured data, and eventually provides an overall score as output. Furthermore, the visualization based on the class activation mapping (CAM) can automatically identify the motions that have significant impact on the overall score, thus providing useful feedback to trainees. Our model can achieve 92.2% average classification accuracy using the Leave-One-Out-Cross-Validation (LOOCV).

Objective: To investigate the prevalence of impaired glucose-insulin metabolism in obstructive sleep apnea hypopnea syndrome (OSAHS); to examine the relation between severity of OSAHS and impaired glucose metabolism; and to evaluate the effectiveness of continuous positive airway pressure (CPAP) on impaired glucose metabolism., Methods: A total of 214 patients who were free of diabetes at baseline underwent both nocturnal polysomnography (PSG), and 2-h oral glucose-tolerance test, insulin and hemoglobin A1c test. CPAP treatment for glucose-insulin metabolism (+) was given to OSAHS group after informed consent had been obtained., Results: Eighty-eight patients and 17 patients with impaired glucose-insulin metabolism were found in OSAHS group and the control group respectively. Impaired glucose-insulin metabolism was present in 54.3% of OSAHS group and 32.7% of control group. Logistic regression analysis showed a significant positive correlation with OSAHS (AHI ≥ 10 times/h) and impaired glucose-insulin metabolism in all patients (OR = 2.440, 95%CI 1.201 - 4.958). Plasma glucose level changes had no significant differences between before and after CPAP treatment (P > 0.05)., Conclusion: OSAHS is associated with a high frequency of impaired glucose metabolism. The relationship between OSAHS and impaired glucose metabolism is independent of obesity. Longest apnea time (LAT) and AHI are important contributors to impaired glucose metabolism in OSAHS patients. Short-term CPAP therapy has no significant improvement on glucose metabolism in patients with OSAHS.