Interest of Using Deep Learning Algorithm for Otosclerosis Detection on Temporal Bone High Resolution CT

Study Overview
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Brief Summary
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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
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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.

Official Title
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Interest of Using Deep Learning Algorithm for Otosclerosis Detection on Temporal Bone High Resolution CT

Conditions
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Otosclerosis

Intervention / Treatment
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* Combination Product: Radiologic diagnosis
* Diagnostic Test: Artificial intelligence diagnosis


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 110 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| CASE<br>Patients with surgically confirmed otosclerosis who initially consulted for conductive hearing loss with normal otoscopy, and with a high resolution computed tomography of temporal bone available | Combination Product: Radiologic diagnosis<br>* Each CT scan is interpreted by a radiologist and is assigned as positive or negative for the diagnosis of otosclerosis<br>Diagnostic Test: Artificial intelligence diagnosis<br>* Each CT scan is screened by the deep learning algorithm and is assigned as positive or negative for the diagnosis of otosclerosis<br>|
| CONTROL<br>Random patients with a high resolution computed tomography scan of temporal bone performed without suspicion of otosclerosis and considered normal | Combination Product: Radiologic diagnosis<br>* Each CT scan is interpreted by a radiologist and is assigned as positive or negative for the diagnosis of otosclerosis<br>Diagnostic Test: Artificial intelligence diagnosis<br>* Each CT scan is screened by the deep learning algorithm and is assigned as positive or negative for the diagnosis of otosclerosis<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 | 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 | through study completion, an average of 5 months | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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otosclerosis, IA, deep learning, temporal bone CT