Artificial Intelligence, Gastroscopy, Training
Conditions
Brief summary
In this study, we proposed a prospective study about the effectiveness of artificial intelligence system for gastroscope training in novice endoscopists. The subjects would be divided into two groups. The experimental group would be trained in painless gastroscopy with the assistance of the artificial intelligence assistant system. The artificial intelligence assistant system can prompt abnormal lesions and the parts covered by the examination (the stomach is divided into 26 parts). The control group would receive routine painless gastroscopy training without special prompts. Then we compare the gastroscopy operation score, coverage rate of blind spots in gastroscopy,check the average test score before and after training, training satisfaction, detection rate of lesions and so on between the two group.
Detailed description
In this study, we proposed a prospective study about the effectiveness of artificial intelligence system for gastroscope training in novice endoscopists. The subjects would be divided into two groups. The experimental group would be trained in painless gastroscopy with the assistance of the artificial intelligence assistant system. The artificial intelligence assistant system can prompt abnormal lesions and the parts covered by the examination (the stomach is divided into 26 parts). The control group would receive routine painless gastroscopy training without special prompts. Then we compare the gastroscopy operation score, coverage rate of blind spots in gastroscopy,check the average test score before and after training, training satisfaction, detection rate of lesions and so on between the two group.
Interventions
The intervention is the use of the artificial intelligence assistant system in addition to the common training. The system is an non-invasive AI system which could help the endoscopists to diagnosis and monitor the blind spot during the gastroscope.
Sponsors
Renmin Hospital of Wuhan University
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
SCREENING
Masking
DOUBLE (Subject, Outcomes Assessor)
Eligibility
Sex/Gender
ALL
Age
18 Years to 70 Years
Healthy volunteers
Yes
Inclusion criteria
Novice endoscopists： Inclusion Criteria: 1. Males or females who are over 18 years old; 2. After qualified medical education and obtained the Certificate of Chinese medical practitioner;
Exclusion criteria
l: 1. A doctor who has already been trained in gastroenteroscopy; 2. Doctors without qualified medical education and didn't obtaine the Certificate of Chinese medical practitioner; 3. The researcher believes that the subjects are not suitable for participating in clinical trials.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Gastroscopy operation score | three month | Using a professional gastroscopy operation scoring scale, the full score is 100 points, and the score is divided into small items. In this experiment, the effect of training between the two groups was compared by comparing the scores of gastroscopy operation in the experimental group and the control group. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Coverage rate of blind spots in gastroscopy | three month | Evaluate the gastroscope operation videos retained by each physician during the examination, and calculate the coverage of 26 parts of the gastric mucosa in the experimental group and the control group during the examination. The calculation method is: the coverage rate of the blind area of the gastroscopy = the actual number of parts covered by the examination/26 parts of the stomach x 100%. |
| Check the average test score before and after training | three month | the difference between the theoretical test score after the training and the theoretical test score before the training, the calculation method: the theoretical test score after the training-the theoretical test score before the training. |
| Training satisfaction | three month | An AI assistant group fills out a questionnaire after training, and determines the satisfaction with AI assistant training through a grading method. |
| Detection rate of lesions | three month | the detection rate of lesions in the experimental group and the control group by gastroscopy. Calculation method = number of gastroscopes with detected lesions/total number of gastroscopes completed by beginner physicians x 100%. |
Countries
China
Outcome results
None listed