Research Article: Application of an artificial-intelligence–based transesophageal echocardiography simulation system in residency training
Abstract:
Transesophageal echocardiography (TEE) is a key perioperative monitoring modality in anesthesiology, but traditional training is constrained by limited case exposure, patient safety concerns, and heterogeneous learning curves. Artificial-intelligence (AI)–based TEE simulators that integrate 3-dimensional (3D) anatomy visualization, interactive image interpretation, and virtual probe manipulation may improve learning efficiency and confidence in residents.
In this single-center randomized controlled study, sixty anesthesiology residents in standardized training at Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital (December 2024–December 2025) were randomly allocated to an AI group ( n =?30) or a control group ( n =?30). Both groups received the same instructor-led TEE curriculum. The control group underwent traditional teaching (lectures, static images, and video demonstrations), whereas the AI group additionally trained on an AI-based TEE simulation system that provided 3D anatomy visualization, interactive image-reading exercises, and virtual probe operation with real-time feedback. Outcomes included a standardized image-interpretation test (0–100), Objective Structured Assessment of Technical Skills for TEE (OSATS-TEE; 0–150), key-view acquisition rate, procedure time, self-efficacy score, and course satisfaction (all 0–100).
Baseline demographic characteristics did not differ between groups (all p >?0.05). After the course, the AI group achieved higher image-interpretation test scores than the control group (84.3?±?6.9 vs. 78.1?±?7.8, p =?0.002) and higher scores on the training-context-specific OSATS-TEE assessment (121.5?±?9.2 vs. 112.4?±?10.1, p <?0.001). The key-view acquisition rate was significantly higher in the AI group (88.2%?±?8.1% vs. 76.5%?±?9.7%, p <?0.001), while mean operation time was shorter (27.1?±?3.9?min vs. 32.4?±?4.6?min, p <?0.001). Self-efficacy (87.2?±?7.5 vs. 78.3?±?8.4, p <?0.001) and course satisfaction (91.0?±?6.8 vs. 80.5?±?7.6, p <?0.001) were also higher in the AI group.
An AI-based TEE simulation training system was associated with improved residents' simulation-based TEE learning outcomes, including image-interpretation performance, procedural assessment scores, efficiency, and perceived self-efficacy, compared with traditional teaching. However, further studies are needed to determine whether these gains translate into clinical performance in perioperative practice.
Introduction:
Transesophageal echocardiography (TEE) is a key perioperative monitoring modality in anesthesiology, but traditional training is constrained by limited case exposure, patient safety concerns, and heterogeneous learning curves. Artificial-intelligence (AI)–based TEE simulators that integrate 3-dimensional (3D) anatomy visualization, interactive image interpretation, and virtual probe manipulation may improve learning efficiency and confidence in residents.
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