Artificial Intelligence for Otosclerosis Detection: A Pilot Study.
Artificial intelligence
CT scan
Otosclerosis
Temporal bone
Journal
Journal of imaging informatics in medicine
ISSN: 2948-2933
Titre abrégé: J Imaging Inform Med
Pays: Switzerland
ID NLM: 9918663679206676
Informations de publication
Date de publication:
26 Jun 2024
26 Jun 2024
Historique:
received:
02
02
2024
accepted:
06
03
2024
revised:
05
03
2024
medline:
27
6
2024
pubmed:
27
6
2024
entrez:
26
6
2024
Statut:
aheadofprint
Résumé
The gold standard for otosclerosis diagnosis, aside from surgery, is high-resolution temporal bone computed tomography (TBCT), but it can be compromised by the small size of the lesions. Many artificial intelligence (AI) algorithms exist, but they are not yet used in daily practice for otosclerosis diagnosis. The aim was to evaluate the diagnostic performance of AI in the detection of otosclerosis. This case-control study included patients with otosclerosis surgically confirmed (2010-2020) and control patients who underwent TBCT and for whom radiological data were available. The AI algorithm interpreted the TBCT to assign a positive or negative diagnosis of otosclerosis. A double-blind reading was then performed by two trained radiologists, and the diagnostic performances were compared according to the best combination of sensitivity and specificity (Youden index). A total of 274 TBCT were included (174 TBCT cases and 100 TBCT controls). For the AI algorithm, the best combination of sensitivity and specificity was 79% and 98%, with an ideal diagnostic probability value estimated by the Youden index at 59%. For radiological analysis, sensitivity was 84% and specificity 98%. The diagnostic performance of the AI algorithm was comparable to that of a trained radiologist, although the sensitivity at the estimated ideal threshold was lower.
Identifiants
pubmed: 38926265
doi: 10.1007/s10278-024-01079-w
pii: 10.1007/s10278-024-01079-w
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.
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