Otosclerosis
Conditions
Keywords
otosclerosis, IA, deep learning, temporal bone CT
Brief summary
Otosclerosis is a relatively frequent pathology, of multifactorial origin with genetic and hormonal part, predominantly in women. This disease causes a disorder of the bone metabolism of the middle and inner ear, responsible for a progressive deafness, which can become severe. Several elements are necessary to make the diagnosis of otosclerosis: the clinical examination and questioning, the audiometric assessment, and finally the temporal bone CT. The CT scan allows to detect foci of otosclerosis within the bone of the middle or inner ear. This diagnosis is sometimes difficult and requires interpretation by a trained radiologist. The investigators would like to evaluate the ability of a deep learning algorithm to detect these foci of otosclerosis, and to compare its diagnostic performance with a trained radiologist.
Detailed description
Otosclerosis is a relatively frequent pathology, of multifactorial origin with genetic and hormonal part, predominantly in women. This disease causes a disorder of the bone metabolism of the middle and inner ear, responsible for a progressive deafness, which can become severe. Several elements are necessary to make the diagnosis of otosclerosis: the clinical examination and questioning, the audiometric assessment, and finally the temporal bone CT. The CT scan allows to detect foci of otosclerosis within the bone of the middle or inner ear. This diagnosis is sometimes difficult and requires interpretation by a trained radiologist. The investigators would like to evaluate the ability of a deep learning algorithm to detect these foci of otosclerosis, and to compare its diagnostic performance with a trained radiologist.
Interventions
COMBINATION_PRODUCTRadiologic diagnosis
Each CT scan is interpreted by a radiologist and is assigned as positive or negative for the diagnosis of otosclerosis
DIAGNOSTIC_TESTArtificial intelligence diagnosis
Each CT scan is screened by the deep learning algorithm and is assigned as positive or negative for the diagnosis of otosclerosis
Sponsors
Hospices Civils de Lyon
Study design
Observational model
CASE_CONTROL
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 110 Years
Healthy volunteers
Yes
Inclusion criteria
* Inclusion Criteria \* : * age over 18 * high resolution temporal bone CT scan available for analysis * for the case group : surgical confirmation of positive diagnosis for otosclerosis * for the control group : a first radiological analysis in favor of a normal temporal bone CT scanner and an initial radiologic report considered normal as well *
Exclusion criteria
\* : * age under 18 * no high resolution temporal bone CT scan available for analysis * unwillingness to participate in the study
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Diagnostic performance of the artificial intelligence algorithm compared to the diagnostic performance of the radiologist : sensitivity, specificity, positive and negative predictive value, area under the ROC curve | through study completion, an average of 5 months | These diagnostic performances will be established from the positive or negative diagnoses of the algorithm and the radiologist, compared to the case or control status of each patient included in the study |
Countries
France
Outcome results
None listed