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Artificial Intelligence for Otosclerosis Detection: A Pilot Study.

Antoine Emin1, Sophie Daubié1, Loïc Gaillandre2

  • 1Hospices Civils de Lyon, Service d'Imagerie Médicale, Centre Hospitalier Lyon Sud, 69310, Pierre Bénite Cedex, France.

Journal of Imaging Informatics in Medicine
|June 26, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) shows promise for diagnosing otosclerosis using temporal bone computed tomography (TBCT). AI performance was comparable to radiologists, offering a potential new tool for otosclerosis detection.

Keywords:
Artificial intelligenceCT scanOtosclerosisTemporal bone

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Area of Science:

  • Otolaryngology
  • Radiology
  • Medical Artificial Intelligence

Background:

  • Otosclerosis diagnosis relies on high-resolution temporal bone computed tomography (TBCT), but small lesions pose challenges.
  • Artificial intelligence (AI) algorithms are not yet standard for otosclerosis diagnosis in clinical practice.

Purpose of the Study:

  • To evaluate the diagnostic performance of an AI algorithm in detecting otosclerosis using TBCT.
  • To compare AI diagnostic accuracy against experienced radiologists.

Main Methods:

  • A case-control study included 174 surgically confirmed otosclerosis patients and 100 controls who underwent TBCT.
  • An AI algorithm interpreted TBCT scans for otosclerosis diagnosis.
  • Radiologists performed a double-blind reading; diagnostic performance was assessed using the Youden index.

Main Results:

  • The AI algorithm achieved 79% sensitivity and 98% specificity.
  • Radiological analysis showed 84% sensitivity and 98% specificity.
  • AI's diagnostic performance was comparable to radiologists, with similar specificity but lower sensitivity at the optimal threshold.

Conclusions:

  • AI demonstrates comparable diagnostic performance to radiologists for otosclerosis detection via TBCT.
  • AI holds potential as an adjunctive tool in otosclerosis diagnosis, though sensitivity requires further optimization.