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Technical Approach for Infrared Tracking for Soft Tissue Navigation with a Holographic Head-Mounted Display and Preclinical Validation

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Artificial intelligence system for EUS navigation and anatomical landmark recognition.

Gianenrico Rizzatti¹, Giulia Tripodi¹, Sara Sofia De Lucia¹

¹Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.

Videogie : an Official Video Journal of the American Society for Gastrointestinal Endoscopy

|July 11, 2025

Summary

This summary is machine-generated.

Artificial intelligence (AI) systems can now identify anatomical landmarks during endoscopic ultrasound (EUS) examinations. This deep learning technology shows promise for improving EUS training and quality control.

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Area of Science:

Medical technology
Artificial intelligence in medicine
Endoscopic Ultrasound (EUS)

Background:

Artificial intelligence (AI) demonstrates potential in various medical fields, including endoscopy.
AI applications in EUS are emerging, with initial studies focusing on pancreatic mass identification.
Recent advancements include AI systems designed for real-time anatomical landmark identification during diagnostic EUS.

Purpose of the Study:

To evaluate an AI system for real-time anatomical landmark identification during diagnostic EUS.
To assess the system's accuracy in recognizing key anatomical structures in EUS procedures.

Main Methods:

The Endoangel system, utilizing deep convolutional neural networks (DCNNs), was employed.
The DCNN was trained on over 550 EUS procedures for feature extraction and prediction.
The system was tested in three patients undergoing diagnostic EUS.

Main Results:

The AI EUS system successfully identified anatomical landmarks in real-time during the tested procedures.
An expert performing the EUS judged the accuracy of the landmark recognition.
The system did not identify pathological findings like pancreatic masses or cystic lesions.

Conclusions:

The DCNN-based AI EUS system accurately identifies anatomical landmarks.
This technology has potential applications in EUS training and quality assurance.
Future developments may include the identification of pathological alterations.