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Development of automatic generation system for lung nodule finding descriptions.

Yohei Momoki1, Akimichi Ichinose1, Keigo Nakamura1

  • 1Medical Systems Research & Development Center, FUJIFILM Corporation, Minato, Tokyo, Japan.

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|March 21, 2024
PubMed
Summary

An artificial intelligence system was developed to automatically generate descriptions of lung nodules from computed tomography images. This AI tool improved the consistency and completeness of radiology reports, aiding in lung cancer management.

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Area of Science:

  • Artificial Intelligence in Radiology
  • Medical Imaging Analysis
  • Natural Language Processing for Clinical Reports

Background:

  • Lung cancer is a leading cause of cancer mortality globally.
  • Accurate and consistent radiology reports are crucial for lung nodule management.
  • Variability in radiologist reporting can impact patient care.

Purpose of the Study:

  • To develop an AI system for automatic generation of lung nodule descriptions from CT images.
  • To enhance the quality, uniformity, and comprehensiveness of radiology reports.
  • To assess the clinical utility of the AI system in a real-world setting.

Main Methods:

  • An AI system combining image recognition and natural language processing was developed.
  • Image recognition extracts bronchopulmonary segments and nodule characteristics from CT scans.
  • Natural language processing generates fluent and descriptive text for radiology reports.

Main Results:

  • The AI system significantly improved the similarity of described contents between radiologists (p = 0.001).
  • Comprehensiveness of the reported findings was also significantly enhanced (p = 0.025).
  • The accuracy of the generated descriptions did not significantly decrease (p = 0.484).

Conclusions:

  • The proposed AI system effectively generates consistent and comprehensive descriptions of lung nodules.
  • This technology has the potential to improve the quality and standardization of radiology reporting for lung cancer.
  • AI-assisted reporting can aid radiologists in managing lung nodules and improving patient outcomes.