
Learning models for endoscopic ultrasonography in gastrointestinal endoscopy
Author(s) -
Gwang Ha Kim,
Sung Jo Bang,
Joo Ha Hwang
Publication year - 2015
Publication title -
world journal of gastroenterology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.427
H-Index - 155
eISSN - 2219-2840
pISSN - 1007-9327
DOI - 10.3748/wjg.v21.i17.5176
Subject(s) - endoscopic ultrasonography , endoscopy , medicine , radiology , medical physics , modality (human–computer interaction) , ultrasonography , computer science , artificial intelligence
Endoscopic ultrasonography (EUS) has become a useful diagnostic and therapeutic modality in gastrointestinal endoscopy. However, EUS requires additional training since it requires simultaneous endoscopic manipulation and ultrasonographic interpretation. Obtaining adequate EUS training can be challenging since EUS is highly operator-dependent and training on actual patients can be associated with an increased risk of complications including inaccurate diagnosis. Therefore, several models have been developed to help facilitate training of EUS. The models currently available for EUS training include computer-based simulators, phantoms, ex vivo models, and live animal models. Although each model has its own merits and limitations, the value of these different models is rather complementary than competitive. However, there is a lack of objective data regarding the efficacy of each model with recommendations on the use of various training models based on expert opinion only. Therefore, objective studies evaluating the efficacy of various EUS training models on technical and clinical outcomes are still needed.