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Artificial Intelligence in Temporal Bone Imaging: A Systematic Review.

Dimitrios Spinos1,2, Anastasios Martinos3, Dioni-Pinelopi Petsiou3

  • 1South Warwickshire NHS Foundation Trust, Warwick, UK.

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|October 1, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) shows promise in temporal bone imaging, improving diagnostic accuracy and speed. However, research quality varies, necessitating standardized methods for reliable clinical application.

Keywords:
artificial intelligencelateral skull basemachine learningotologyreview

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • The human temporal bone is anatomically complex, requiring precise image interpretation.
  • Artificial intelligence (AI) applications are increasingly used in medical imaging.
  • This review focuses on the role of AI in temporal bone imaging.

Purpose of the Study:

  • To systematically review and highlight the current applications of AI in temporal bone imaging.
  • To assess the role of AI in interpreting complex anatomical structures of the temporal bone.

Main Methods:

  • Systematic review of English publications from MEDLINE, COCHRANE Library, and EMBASE.
  • Search terms included 'artificial intelligence,' 'machine learning,' 'deep learning,' 'neural network,' 'temporal bone,' and 'vestibular schwannoma.'
  • 72 studies were included, with screening based on predefined inclusion and exclusion criteria.

Main Results:

  • Most studies (95.8%) were retrospective and used internal databases (88.9%).
  • Computed tomography (CT) was the primary imaging modality (54.2%), with vestibular schwannoma (VS) being the most studied condition (37.5%).
  • Neural networks, particularly convolutional neural networks (CNNs), were employed in 58 studies; research quality averaged 13.6/20.

Conclusions:

  • AI demonstrates potential to enhance diagnostic accuracy, speed, and reduce errors in temporal bone imaging compared to clinicians.
  • Existing research exhibits heterogeneity and variable quality, indicating a need for standardized methodologies.
  • Further standardized research is crucial for ensuring the consistency and reliability of AI in clinical practice.