Clinical trial NCT05792280: Clinical Research on a Novel Deep-learning Based System in Mediastinal Endoscopic Ultrasound Scanning
Official title: Clinical Research on Navigation and Quality Control System of Mediastinal Ultrasound Endoscopy Based on Deep Learning
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: Mediastinum Disease
Keywords: EUS
AI
Quality Control
training
Lead sponsor: The Third Xiangya Hospital of Central South University
Sponsor class: OTHER
First posted: 2023-03-31
Enrollment: 180
Sex: ALL
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: OTHER

Brief summary: 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 integrity and 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: 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, the completeness of standard sites and scanning time of endoscopic-assisted and non-assisted AI systems were compared.

Eligibility criteria: Inclusion Criteria:

* 1. Age ≥18 years old, \<80 years old 2.Patients who need endoscopic ultrasonography; 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:

1. Poor physical condition, including hemoglobin ≤8.0g/dl, severe cardiopulmonary insufficiency, etc.
2. Anesthesia assessment failed
3. Pregnancy or breastfeeding
4. In the acute stage of chemical and corrosive injury, it is very easy to cause perforation
5. Recent acute coronary syndrome or clinically unstable ischemic heart attack
6. 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:

1. Mediastinal lesions have previously undergone surgery or radiotherapy and chemotherapy;
2. Mental illness, drug addiction, inability to express themselves or other diseases that may affect follow-up.

Primary outcomes:
measure: Accuracy
timeFrame: 1 year
measure: The completeness for standard station scanning
timeFrame: Until the end of the study
Secondary outcomes:
measure: Cohen's kappa coefficient
timeFrame: 1 year
measure: The completeness for standard stations and anatomical landmarks per individual
timeFrame: Until the end of the study
measure: The completeness of anatomical landmarks
timeFrame: Until the end of the study
measure: Operation time
timeFrame: Until the end of the study
measure: The incidence of adverse events
timeFrame: Until the end of the study
Other outcomes:
Locations:
city: Changsha
country: China
facility: The Third Xiangya Hospital of Central South University
state: Hunan
Study officials:
affiliation: The Third Xiangya Hospital of Central South University
name: Xiaoyan Wang, Doctor
role: PRINCIPAL_INVESTIGATOR
References: