Clinical Research on a Novel Deep-learning Based System in Mediastinal Endoscopic Ultrasound Scanning

Study Overview
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Brief Summary
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The goal of this clinical trial is to develop and verify the auxiliary role of the artificial intelligence system in mediastinal ultrasound endoscopic scanning. The main questions it aims to answer are as follows: 1.The comparison of the image recognition accuracy between the artificial intelligence system and the ultrasound endoscopist; 2. Whether the artificial intelligence system can improve the efficiency of the mediastinum scanning for the ultrasound endoscopist. Participants will undergo mediastinal EUS with or without the assistance of the artificial intelligence system.

Detailed Description
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In this study, a total of 200 cases of mediastinal endoscopic ultrasound scanning videos will be collected. First of all, an artificial intelligence system based on deep learning for the navigation and quality control of mediastinal endoscopic ultrasonography will be established. Secondly, the artificial intelligence system will be used to identify the site and anatomical structure of the mediastinal ultrasound endoscope, and the results of the artificial intelligence system's station recognition will be compared with the results of the endoscopist's station recognition. Finally, compare the image recognition speed, image recognition accuracy and precision of endoscopists with and without the assistance of artificial intelligence system.

Official Title
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Clinical Research on Navigation and Quality Control System of Mediastinal Ultrasound Endoscopy Based on Deep Learning

Conditions
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Mediastinum Disease

Intervention / Treatment
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* Device: AI system


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: 1. Age ≥18 years old, <80 years old 2.Patients who need endoscopic ultrasonography of mediastinum; 3. Agree to participate in this study and sign the informed consent form. Exclusion Criteria: Subjects who meet any of the following criteria cannot be selected for this trial: First. The patient's physical condition does not meet the requirements of conventional endoscopic ultrasonography: Poor physical condition, including hemoglobin ≤8.0g/dl, severe cardiopulmonary insufficiency, etc. Anesthesia assessment failed Pregnancy or breastfeeding In the acute stage of chemical and corrosive injury, it is very easy to cause perforation Recent acute coronary syndrome or clinically unstable ischemic heart attack Heart disease patients with right-to-left shunt, patients with severe pulmonary hypertension (pulmonary artery pressure> 90mmHg),patients with uncontrolled systemic hypertension and patients with adult respiratory distress syndrome. Second. Disagree to participate in this study. Third. There are other problems that do not meet the requirements of this research or that affect the results of the research: Mediastinal lesions have previously undergone surgery or radiotherapy and chemotherapy; Mental illness, drug addiction, inability to express themselves or other diseases that may affect follow-up.

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 80 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Other
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Double


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: AI-assisted group<br>Subjects will undergo EUS examination with the assistance of AI system. | Device: AI system<br>* Patients will undergo EUS examination with the assistance of AI system.<br>|
| No Intervention: non-assisted group<br>Subjects will undergo EUS examination without the assistance of AI system | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Accuracy | The number of correctly classified images divided by the total number of images. | 1 year | 
| intersection over union (IoU) | It was defined as the relative area of overlap between the predicted bounding box(A) and the ground-truth(B) bounding box. | 1 year | 
| Precision | When the IoU was greater than the threshold, the prediction was true positive(TP); when the IoU was less than the threshold, the prediction was false positive(FP).When the model segmentation area was equal to 0, it was false negative(FN).Precision=TP/(TP+FP) | 1 year | 
| Recall | Recall=TP/(TP+FN) | 1 year | 
| Dice | Dice=2TP/(2TP+FP+FN) | 1 year | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Cohen's kappa coefficient | This data is to evaluate the agreement between the model and the endoscopists. | 1 year | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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EUS, AI, Quality Control, training