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Related Concept Videos

Imaging Studies for Cardiovascular System I:Echocardiography01:17

Imaging Studies for Cardiovascular System I:Echocardiography

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Cardiac imaging studies encompass a wide range of noninvasive and minimally invasive techniques designed to visualize the heart's structure and function in detail. One such technique is echocardiography, which uses high-frequency ultrasound waves to produce detailed images of the heart, known as echocardiograms.
Indications: Echocardiography is utilized to diagnose heart failure, valve disorders, and myocardial infarction. It also assesses cardiac structures' size, shape, and motion,...
280

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Automated structured data extraction from intraoperative echocardiography reports using large language models.

Emily J MacKay1, Shir Goldfinger2, Trevor J Chan3

  • 1Department of Anaesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn Center for Perioperative Outcomes Research and Transformation (CPORT), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn's Cardiovascular Outcomes, Quality and Evaluative Research Center (CAVOQER), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

British Journal of Anaesthesia
|March 4, 2025
PubMed
Summary
This summary is machine-generated.

Consensus-based large language model (LLM) ensembles can automate structured data extraction from echocardiography reports. The unanimous LLM ensemble demonstrated high accuracy and low error rates in analyzing intraoperative transesophageal reports.

Keywords:
artificial intelligence (AI)cardiac surgeryechocardiographylarge language modelsperioperative medicine

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Area of Science:

  • Artificial Intelligence in Medicine
  • Natural Language Processing in Healthcare
  • Cardiovascular Imaging Informatics

Background:

  • Echocardiography reports contain valuable data in unstructured text format.
  • Automated structured data extraction is needed to improve efficiency and data utilization.
  • Large Language Models (LLMs) show promise for this task.

Purpose of the Study:

  • To evaluate the effectiveness of consensus-based LLM ensembles for extracting structured echocardiographic data.
  • To compare different LLM ensemble voting strategies (unanimous, supermajority, majority, plurality).
  • To assess accuracy, error rates, and data yield in intraoperative transesophageal reports.

Main Methods:

  • A cross-sectional study used 600 intraoperative transesophageal reports.
  • Three key echocardiographic parameters were extracted: LVEF, RV systolic function, and TR.
  • Five open-source LLMs and four voting strategies were employed to create ensembles.

Main Results:

  • The unanimous LLM ensemble achieved the highest consensus accuracy (99.4% presurgical, 97.9% postsurgical) and lowest error rates.
  • The plurality LLM ensemble yielded the highest raw accuracy (96.1% presurgical, 93.7% postsurgical) and data extraction yield.
  • Performance varied significantly across different voting strategies and report sections.

Conclusions:

  • Consensus-based LLM ensembles can successfully generate structured data from unstructured echocardiography reports.
  • The choice of voting strategy impacts the trade-off between accuracy, yield, and error rates.
  • LLM ensembles offer a viable automated solution for echocardiographic data extraction.