The use of explainable artificial intelligence to explore types of fenestral otosclerosis misdiagnosed when using temporal bone high-resolution computed tomography.

ABSTRACT:

Background

The purpose of this study was to explore the common characteristics of fenestral otosclerosis (OS) which are misdiagnosed, and develop a deep learning model for the diagnosis of fenestral OS based on temporal bone high-resolution computed tomography scans.

Methods

We conducted a study to explicitly analyze the clinical performance of otolaryngologists in diagnosing fenestral OS and developed an explainable deep learning model using 134,574 temporal bone high-resolution computed tomography (HRCT) slices collected from 1,294 patients for the automatic diagnosis of fenestral OS. We prospectively created an external test set with 31,774 CT slices from 144 patients, which contained 86 fenestral OS ears and 202 normal ears and used it to evaluate the performance of our

SUBMITTER: Tan W 

PROVIDER: S-EPMC8267294 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature