Application of an artificial-intelligence-based transesophageal echocardiography simulation system in residency training.

Meng Zhang, Si-Yuan Song, Huixian Zhou, Guangmin Xu, Xianxiang Tong, Dan Fan, Qian Lei

Abstract read

In one paragraph

Article in Frontiers in surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Meng Zhang *Department of Anesthesiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.

Si-Yuan Song *Department of Anesthesiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, United States.

Huixian ZhouDepartment of Internal Medicine, Montefiore Medical Center Wakefield Campus, Bronx, NY, United States.

Guangmin XuDepartment of Anesthesiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.

Xianxiang TongDepartment of Anesthesiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.

Dan FanDepartment of Anesthesiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.

Qian LeiDepartment of Anesthesiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: 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. Methods: 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 ( Results: Baseline demographic characteristics did not differ between groups (all Conclusions: 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.

Indexed as

anesthesiologyartificial intelligenceresidency educationsimulation trainingtransesophageal echocardiography

Identifiers

PMID42266218

DOI10.3389/fsurg.2026.1762740

PMCPMC13243387

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.