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Keyword-augmented and semi-automatic generation of FESS reports: a proof-of-concept study.

V Kunz1, V Wildfeuer2, R Bieck3

  • 1Department of Otolaryngology, Head and Neck Surgery, University Hospital Leipzig, Liebigstraße 10-14, 04103, Leipzig, Germany. Viktor.Kunz@medizin.uni-leipzig.de.

International Journal of Computer Assisted Radiology and Surgery
|November 17, 2022
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Summary

This study introduces an AI tool for semi-automatic surgical report generation in functional endoscopic sinus surgery (FESS), significantly reducing physician workload and improving efficiency. The AI model shows promise in optimizing clinical workflows and enhancing patient care quality.

Keywords:
Artificial intelligenceDeep learningFESSNatural language processingNeural language modelsSurgical reports

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Area of Science:

  • Artificial Intelligence in Medicine
  • Natural Language Processing for Clinical Documentation
  • Surgical Workflow Optimization

Background:

  • Surgical report generation is a time-consuming, error-prone task often performed from memory post-procedure.
  • This manual process increases physician workload and can impact efficiency.
  • Developing automated solutions is crucial for improving healthcare delivery.

Purpose of the Study:

  • To develop and evaluate an AI-powered software tool for semi-automatic intraoperative generation of surgical reports.
  • To assess the tool's impact on physician workload, time-saving, and report quality in functional endoscopic sinus surgery (FESS).
  • To determine physician acceptance and potential clinical benefits of AI-assisted surgical reporting.

Main Methods:

  • A neural language model was developed using an encoder-decoder architecture and a custom keyword vocabulary.
  • The model was trained on 48 conventional surgical reports.
  • Physicians evaluated generated reports for quality, time-saving, clinical benefits, and compared them to conventional reports, with corrections quantified.

Main Results:

  • Objective performance metrics improved significantly after model training (p < 0.001).
  • A majority of physicians reported significant time savings (16-30 min/day), clinical benefits, and workload reduction.
  • While content and form similarity to conventional reports varied, 66.67% expressed willingness to use the tool, with an average of 23.25 corrections needed per report.

Conclusions:

  • The AI tool demonstrates potential for reducing workload and optimizing clinical workflows in surgical reporting.
  • Physician acceptance is high, indicating the tool's value in improving patient care quality.
  • Further model training is necessary to address current limitations in text generation accuracy.