An Artificial Intelligence System for ROSE of EUS-FNA Sample: a Prospective, Multicenter, Diagnostic Study.

Qilu Hospital of Shandong University236 enrolled

Overview

This is an observational study with a prospective, multicenter, disgnostic design. An artificial intelligence system named ROSE-AI system was developed using cytopathological slide images taken by microscope camera or smartphone of pancreas, bile duct, liver and lymph node, collected retrospectively from patients who underwent EUS-FNA and ROSE, and the performance of ROSE-AI system was validated in the datasets collected prospectively.This study aims to assist endoscopists in conducting rapid on-site cytopathology evaluations during EUS-FNA without the presence of cytopathologists. In addition, the diagnostic field was compared between the cytopathologists and ROSE-AI system, endoscopists with or without ROSE-AI system.

Study Type

OBSERVATIONAL

Enrollment

236

Conditions

The Malignant Lesions and Non-malignant Lesions of Pancreas, Bile Duct, Liver and Lymph Node

Interventions

ROSE-AI systemDIAGNOSTIC_TEST

The cytopathological slide images of the patients' ROSE samples will be identified by the ROSE-AI system.

Eligibility

Sex: ALLMin age: 18 YearsHealthy volunteers:

Medical Language ↔ Plain English

Inclusion Criteria: 1. the patient age ≥18 years accepted EUS-FNA+ROSE. 2. agree to participate in the research and be able to sign written informed consent. Exclusion Criteria: 1. uncorrectable coagulopathy (PTT \>50 seconds or INR \>1.5) and/or uncorrectable thrombocytopenia (platelet count \<50 × 109 /L). 2. patients who were too clinically ill to undergo an EUS examination. 3. lesions that were deemed inaccessible for EUS-guided sampling. 4. unsuccessful EUS-FNA (e.g., failure to obtain an adequate specimen, patient intolerance, intraoperative accidents, etc.). 5. Patients with unqualified ROSE smear. 6. Patients who underwent biopsy during EUS-FNA but did not receive a definitive pathological diagnosis or pathological report. 7. pregnancy.

Outcomes

Primary Outcomes

the accuracy, sensitivity and specificity of the ROSE-AI system in identifying malignant/non-malignant ROSE samples

The primary outcome of the study is to evaluate the performance of the ROSE-AI system in identifying the malignant/non-malignant ROSE samples of pancreatic, bile duct, hepatic and lymph node based on both images taken by microscope camera and smartphone, and comparing the performance between the ROSE-AI system and endoscopists, cytopathologists.

Time frame: During procedure

Secondary Outcomes

comparing the diagnostic performance between endoscopists with ROSE-AI system and without ROSE-AI system

A cross-over human-AI contest using images of the prospective testing dataset will be performed. The diagnostic performance of endoscopists with ROSE-AI system and without ROSE-AI system will be evaluated.