Application of Artificial Intelligence For Real-Time Anatomical Recognition During Endoscopic Ultrasound Evaluation: A Pilot Study

Aims We aimed to develop an artificial intelligence model that recognizes in real-time the anatomical structures during EUS evaluations.

Methods A single-center, pilot study. We developed two convolutional neuronal networks from linear and radial endoscopic ultrasound videos from patients without pathologies. The AI models were developed using an automated machine learning software (AI Works, MD Consulting group, Ecuador). Two expert endosonographers trained the two independent models. The linear and radial EUS algorithms metrics were calculated for recognizing anatomical structures during EUS evaluations.

Results We included eight anatomical structures from twelve endoscopic ultrasound videos for the development of the EUS-AI algorithms. A total of 8113 samples were captured from the EUS videos (6354 for radial and 1759 for linear). The anatomical structures were recognized and labeled for the training of the AI models by two experts endosonographers (≥300 EUS/year). The proposed EUS Radial model reached a mean average precision (mAP) of 69.67 %, F1-score (harmonic mean of sensitivity and precision) of 92 %, average IoU (overall between model prediction and expert marking) of 79.08 %, with a total loss of 0.13. The developed EUS Linear model reached an mAP of 83.43 %, F1-score of 89 %, average IoU of 73.48 %, with a total loss of 0.16. Two expert endosonographers evaluated the AI models for the recognition of anatomical structures in twenty-live cases that accurately recognized in real-time all the trained anatomical structures.

Conclusions The proposed artificial intelligence models for linear and radial EUS recognizes and identifies the trained anatomical structures during real-time EUS evaluations. The proposed model could be implemented for the training in EUS, probably reducing the time and number of cases required for achieving competency.

Citation: Robles-Medranda C, Oleas R, Del Valle R et al. OP174 APPLICATION OF ARTIFICIAL INTELLIGENCE FOR REAL-TIME ANATOMICAL RECOGNITION DURING ENDOSCOPIC ULTRASOUND EVALUATION: A PILOT STUDY. Endoscopy 2021; 53: S71.

Publication History

Article published online:
19 March 2021