Objective Using the evidence-based management to manage the flexible endoscope based on the data collected by information means, to reduce the rate of serious faults and control maintenance costs. Methods From January 2017 to December 2018, we collected and analyzed the flexible endoscope data of the use, leak detection, washing and disinfection, and maintenance between 2015 and 2018 from the Gastroenterology Department of our hospital. Three main causes of flexible endoscope faults were found: delayed leak detection, irregular operation, and physical/chemical wastage. Management schemes (i.e., leak detection supervision, fault tracing, and reliability maintenance) were enacted according to these reasons. These schemes were improved continuously in the implementation. Finally, we calculated the changes of the fault rate of each grade and the maintenance cost. Results By two years management practice, compared with those from 2015 to 2016, the annual rates of grade A and grade C faults of flexible endoscope from 2017 to 2018 decreased by 10.3% and 16.7% respectively, and the annual average maintenance cost fell by 53.2%. Conclusions The maintenance costs of flexible endoscope could be effectively controlled by enacting and implementing a series of targeted management schemes based on the data from the root causes of faults applying the evidence-based management. Evidence-based management based on data has a broad application prospect in the management of medical equipment faults.
Objective To develop a neural network architecture based on deep learning to assist knee CT images automatic segmentation, and validate its accuracy. Methods A knee CT scans database was established, and the bony structure was manually annotated. A deep learning neural network architecture was developed independently, and the labeled database was used to train and test the neural network. Metrics of Dice coefficient, average surface distance (ASD), and Hausdorff distance (HD) were calculated to evaluate the accuracy of the neural network. The time of automatic segmentation and manual segmentation was compared. Five orthopedic experts were invited to score the automatic and manual segmentation results using Likert scale and the scores of the two methods were compared. Results The automatic segmentation achieved a high accuracy. The Dice coefficient, ASD, and HD of the femur were 0.953±0.037, (0.076±0.048) mm, and (3.101±0.726) mm, respectively; and those of the tibia were 0.950±0.092, (0.083±0.101) mm, and (2.984±0.740) mm, respectively. The time of automatic segmentation was significantly shorter than that of manual segmentation [(2.46±0.45) minutes vs. (64.73±17.07) minutes; t=36.474, P<0.001). The clinical scores of the femur were 4.3±0.3 in the automatic segmentation group and 4.4±0.2 in the manual segmentation group, and the scores of the tibia were 4.5±0.2 and 4.5±0.3, respectively. There was no significant difference between the two groups (t=1.753, P=0.085; t=0.318, P=0.752). Conclusion The automatic segmentation of knee CT images based on deep learning has high accuracy and can achieve rapid segmentation and three-dimensional reconstruction. This method will promote the development of new technology-assisted techniques in total knee arthroplasty.