This study aims to construct a real-time quality monitoring system based on artificial intelligence technology.
Gastroscopy plays an important role in the detection and diagnosis of upper gastrointestinal diseases. It is necessary for endoscopists to operate gastroscope according to the standardized process, in order to avoid missing early lesions. However, with the rapid increase in the number of endoscopies, the workload of endoscopists increases further. High workload reduces the quality of endoscopy, resulting in incomplete observation of anatomical parts that are easy to be missed in the process of gastroscopy. There are significant differences in the operation level of different endoscopists. Therefore, carrying out artificial intelligence methods has good academic research and practical value for improving the quality of endoscopic diagnosis and treatment. Artificial intelligence devices need to use a large number of endoscopic images, based on this, we intends to collect endoscopic image data from our hospitals for training and validation of the model.
Study Type
OBSERVATIONAL
Enrollment
1,570
missed part during map the entire stomach through endoscopy
Beijing Cancer Hospital
Beijing, Haidian, China
Accuracy
Calculate the accuracy of AI's judgment on images
Time frame: 2020.2.22-2020.7.1
Sensitivity
number of images in which AI correctly diagnosed positive/all images with positive
Time frame: 2020.2.22-2020.7.1
Specificity
number of images in which AI correctly diagnosed negative/all images negative
Time frame: 2020.2.22-2020.7.1
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.