Detecting Task Difficulty of Learners in Colonoscopy: Evidence from Eye-Tracking.

Eye-tracking can help decode the intricate control mechanism in human performance. In healthcare, physicians-in-training require extensive practice to improve their healthcare skills. When a trainee encounters any difficulty in the practice, they will need feedback from experts to improve their performance. Personal feedback is time-consuming and subjected to bias. In this study, we tracked the eye movements of trainees during their colonoscopic performance in simulation. We examined changes in eye movement behavior during the moments of navigation loss (MNL), a signature sign for task difficulty during colonoscopy, and tested whether deep learning algorithms can detect the MNL by feeding data from eye-tracking. Human eye gaze and pupil characteristics were learned and verified by the deep convolutional generative adversarial networks (DCGANs); the generated data were fed to the Long Short-Term Memory (LSTM) networks with three different data feeding strategies to classify MNLs from the entire colonoscopic procedure. Outputs from deep learning were compared to the expert's judgment on the MNLs based on colonoscopic videos. The best classification outcome was achieved when we fed human eye data with 1000 synthesized eye data, where accuracy (91.80%), sensitivity (90.91%), and specificity (94.12%) were optimized. This study built an important foundation for our work of developing an education system for training healthcare skills using simulation.

Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans

Coronavirus disease 2019 (COVID-19) has infected more than 1.3 million individuals all over the world and caused more than 106,000 deaths. One major hurdle in controlling the spreading of this disease is the inefficiency and shortage of medical tests. There have been increasing efforts on developing deep learning methods to diagnose COVID-19 based on CT scans. However, these works are difficult to reproduce and adopt since the CT data used in their studies are not publicly available. Besides, these works require a large number of CTs to train accurate diagnosis models, which are difficult to obtain. In this paper, we aim to address these two problems. We build a publicly-available dataset containing hundreds of CT scans positive for COVID-19 and develop sample-efficient deep learning methods that can achieve high diagnosis accuracy of COVID-19 from CT scans even when the number of training CT images are limited. Specifically, we propose a Self-Trans approach, which synergistically integrates contrastive self-supervised learning with transfer learning to learn powerful and unbiased feature representations for reducing the risk of overfitting. Extensive experiments demonstrate the superior performance of our proposed Self-Trans approach compared with several state-of-the-art baselines. Our approach achieves an F1 of 0.85 and an AUC of 0.94 in diagnosing COVID-19 from CT scans, even though the number of training CTs is just a few hundred.

Comparison of the SLIPA mask airway and endotracheal intubation in the cesarean section operation under general anesthesia / 中国医师进修杂志

Objective To observe the safety and efficacy of the SLIPA mask airway in the cesarean section operation under general anesthesia.Methods Fifty single birth pregnant women who underwent cesarean operation under general anesthesia were randomized into 2 groups.SLIPA mask group(group S,25 cases)and endotracheal intubation(ETT)group(group T,25 cases).Mean arterial pressure(MAP),heart rate(HR),partial pressure of carbon dioxide in end expired gas(P_(ET)CO_2),peak airway pressure(Paw)were measured before induction of anesthesia,just before intubation,2min after intubation,just before extubation,2 min after extubation.Bucking,style,reflow,vomiting,aspiration were observed at inmbation or SLIPA mask airway insertion,before and after extubation and intraoperative.Umbilical arteries and veins blood were collected at delivery for the blood gas analysis.All delivery times,operation time and Apgar scores at 1,5 min were recorded.Results The successful rate ofthe first time intubation was 100% in group S,there was 2 cases by the second time intubation in group T.There Was 3 cases with gently air leak,but no influence on respiratory management.After intubation and extubation,MAP and HR increased significantly in group T (P<0.05),while group S had no significant change.There was no occurrence of bucking,style,reflow,vomiting,aspiration intmoperative,but 17 cages occurred at extubation in group T.The incidence and degree of sore throat in group T(10 cases after 2 hours and 6 cases after 24 hours) were significantly higher than those in group S(P<0.05).The Apgar scores at 1,5 min and delivery times were not significant between the two groups(P>0.05).Conclusion There is less adverse effects of SLIPA mask airway than ETT under general anesthesia in the cesarean section operation,SLIPA mask airway is safe and efficacious.