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

Trial statusRecruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
Age18-80
SponsorThe Third Xiangya Hospital of Central South University

About this trial

The goal of this clinical trial is to develop and verify the auxiliary role of the artificial intelligence system in pancreatic 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 pancreatic scanning for the ultrasound endoscopist. Participants will undergo pancreatic EUS with or without the assistance of the artificial intelligence system.

Eligibility criteria

This trial accepts healthy volunteers

Qualifiers

1. Age ≥18 years old, <80 years old 2.Patients who need endoscopic ultrasonography of pancreas; 3. Agree to participate in this study and sign the informed consent form.

Disqualifiers

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

Trial design

Design model

Parallel

Treatments tested in this trial

  • artificial intelligence system

    Device

    Patients will undergo EUS examination with the assistance of artificial intelligence(AI) system.

Treatment groups

200 Participants
are divided into 2 treatment groups
Group A: AI-assisted groupExperimental treatment 1 intervention
Group B: Non-assisted groupNo intervention 0 interventions

Trial outcomes

Primary outcomes

1

Accuracy

The number of correctly classified images divided by the total number of images.

Time frame
2 year
2

The completeness for standard station scanning

This was calculated as the number of stations successfully scanned divided by the total number of stations that should have been scanned.

Time frame
2 year

Secondary outcomes

1

Cohen's kappa coefficient.

This data is to evaluate the agreement between the model and the endoscopists.

Time frame
2 year
2

The completeness of anatomical landmarks

It calculated as the number of anatomical structures successfully scanned divided by the total number of structures that should have been scanned.

Time frame
2 year
3

The completeness for standard stations and anatomical landmarks per individual

The completeness of stations and anatomic landmarks of biliopancreatic endoscopic ultrasonography by different endoscopists in the AI system assisted group and the control group were compared.

Time frame
2 year
4

Operation time

In addition to puncture, elastography, and enhanced ultrasound to observe the lesion or treatment, it is also used to observe the time of biliopancreatic system.

Time frame
2 year

Other outcomes

Sponsors and contacts

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