Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
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.
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.
Not provided
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| with Artificial intelligence assistant system | Experimental | The experiment group would receive the training with the help of 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. |
|
| without Artificial intelligence assistant system | No Intervention | The control group would receive the training without the help of artificial intelligence assistant system. That is, they would receive the common training process. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Artificial intelligence assistant system | Other | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| Gastroscopy operation score | 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. | three month |
| Measure | Description | Time Frame |
|---|---|---|
| Coverage rate of blind spots in gastroscopy | 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%. |
Not provided
Novice endoscopists：
Inclusion Criteria:
Exclusion Criterial:
Not provided
Not provided
Not provided
Not provided
Not provided
| Name | Affiliation | Role |
|---|---|---|
| Honggang W Yu, Doctor | Renmin Hospital of Wuhan University | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Renmin Hospital of Wuhan University | Wuhan | 430060 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 34970565 | Derived | Huang L, Liu J, Wu L, Xu M, Yao L, Zhang L, Shang R, Zhang M, Xiong Q, Wang D, Dong Z, Xu Y, Li J, Zhu Y, Gong D, Wu H, Yu H. Impact of Computer-Assisted System on the Learning Curve and Quality in Esophagogastroduodenoscopy: Randomized Controlled Trial. Front Med (Lausanne). 2021 Dec 14;8:781256. doi: 10.3389/fmed.2021.781256. eCollection 2021. |
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
|
| three month |
| Check the average test score before and after training | 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. | three month |
| Training satisfaction | An AI assistant group fills out a questionnaire after training, and determines the satisfaction with AI assistant training through a grading method. | three month |
| Detection rate of lesions | 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%. | three month |