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

Otosclerosis Clinical Trial

— OtoIA  

Official title:

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

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT05987215
• Other study ID #: 22-5019 / 69HCL22_1193
• Status: Active, not recruiting
• Start date: July 1, 2022
• Est. completion date: October 1, 2023

Study information

• Verified date: May 2023
• Source: Hospices Civils de Lyon
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 240
• Est. completion date: October 1, 2023
• Est. primary completion date: May 1, 2023
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years to 110 Years

Eligibility

• 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

Related Conditions & MeSH terms

• Otosclerosis

Intervention

Combination Product:
• Radiologic diagnosis
Each CT scan is interpreted by a radiologist and is assigned as positive or negative for the diagnosis of otosclerosis

Diagnostic Test:
• Artificial intelligence diagnosis
Each CT scan is screened by the deep learning algorithm and is assigned as positive or negative for the diagnosis of otosclerosis

Locations

Country	Name	City	State
France	Hospices Civils de Lyon, Centre Hospitalier Lyon sud, Service d'ORL, d'otoneurchirurgie et de chirurgie cervico-facaile	Pierre-Bénite

Sponsors (1)

Lead Sponsor	Collaborator
Hospices Civils de Lyon

Country where clinical trial is conducted

France, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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