Artificial Intelligence vs Endoscopist Identification in EUS Normal Anatomy
- Conditions
- Gastrointestinal Diseases
- Interventions
- Diagnostic Test: Detection of structures
- Registration Number
- NCT06279546
- Lead Sponsor
- Instituto Ecuatoriano de Enfermedades Digestivas
- Brief Summary
Endoscopic ultrasound (EUS) visual impression is operator-dependant and can hinder diagnostic accuracy, especially in less experienced endoscopists. The implementation of artificial intelligence can potentially mitigate operator dependency and interpretation variability, helping or improving the overall accuracy.
The investigators therefore aim to compare diagnostic accuracy between artificial intelligence (AI)-based model and the endoscopists when identifying normal anatomical structures in EUS-procedures.
- Detailed Description
EUS is an operator dependent procedure where accuracy depends on experience and skills. Nowadays, EUS-training can be achieved by a formal fellowship training in a center for 6-24 months or an informal training through didactic sessions with a short hands-on experience. However, parameters for a correct and complete learning experience measurement are yet to be defined. The implementation of artificial intelligence on EUS can potentially mitigate the operator-dependent variable and improve diagnostic accuracy.
Therefore, detection of normal anatomical structures on a separate basis using an AI-based model, expert and non-expert endoscopists to determine where the AI would be most helpful.
The investigators aim to compare the diagnostic accuracy of the AI-based model with the endoscopists identification of normal anatomical structures in EUS procedures.
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 30
- Expert gastrointestinal EUS-endoscopists.
- Non-expert gastrointestinal endoscopists training for EUS.
- Patients with chronic dyspepsia without other findings.
- Patients with previous CT images or upper digestive endoscopy reporting no other findings.
- Patients requiring EUS for surveillance due to family history of pancreatic cancer without findings on MRI.
- Internet connection less than 100 MBs per second.
- Patients with abnormal structures or with visible lesions.
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description Expert endoscopists Detection of structures Endoscopists with \>190 EUS procedures per year, including 75 pancreatobiliary and mucosal cancer staging procedures each, 40 subepithelial cases; and 50 cases of EUS-fine needle aspiration (FNA) (25 of them being pancreatic cases); following the American Society for Gastrointestinal Endoscopy (ASGE) recommendations. Non-expert endoscopists Detection of structures Endoscopists with \<190 EUS procedures per year, including 75 pancreatobiliary and mucosal cancer staging procedures each, 40 subepithelial cases; and 50 cases of EUS-FNA (25 of them being pancreatic cases); following the American Society for Gastrointestinal Endoscopy (ASGE) recommendations. AI-based model Detection of structures AIWorks-EUS Convolutional Neural Network version 2 (CNNv2) (mdconsgroup, Guayaquil, Ecuador) applied on pre-recorded videos for the detection of normal anatomical structures.
- Primary Outcome Measures
Name Time Method Diagnostic accuracy 5 months The true positive, true negative, false positive and false negative based on detection of anatomical structures according to the an external expert endoscopist as gold-standard.
- Secondary Outcome Measures
Name Time Method Interobserver agreement 5 months Comparison of diagnostic accuracies between Artificial intelligence (AI)-based model and both groups (expert and non-expert endoscopists) using Fleiss Kappa.
Trial Locations
- Locations (1)
IECED
🇪🇨Guayaquil, Guayas, Ecuador