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

NCT05987215 | Unknown

• 1 other identifier: 22-5019 / 69HCL22_1193
• Study Type: observational
• Target: 240
• Locations: 1 country
• Sites: 1

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.

Conditions

Otosclerosis

Keywords

otosclerosis; IA; deep learning; temporal bone CT

Outcome Measures

Primary Outcomes (1)

• 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

Study Arms (2)

CASE

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; Diagnostic Test: Artificial intelligence diagnosis

CONTROL

Random patients with a high resolution computed tomography scan of temporal bone performed without suspicion of otosclerosis and considered normal

Combination Product: Radiologic diagnosis; Diagnostic Test: Artificial intelligence diagnosis

Interventions

Radiologic diagnosis COMBINATION_PRODUCT

Each CT scan is interpreted by a radiologist and is assigned as positive or negative for the diagnosis of otosclerosis

CASE; CONTROL

Artificial intelligence diagnosis DIAGNOSTIC_TEST

Each CT scan is screened by the deep learning algorithm and is assigned as positive or negative for the diagnosis of otosclerosis

CASE; CONTROL

Eligibility Criteria

• Age: 18 Years - 110 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study population will include cases from a group of patients who have undergone surgery for otosclerosis at the Lyon Sud hospital in France, and controls who have had a high resolution temporal bone CT scan at the Lyon Sud hospital with no clinical suspicion for otosclerosis and considered normal.

You may qualify if:

• 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

You may not qualify if:

• age under 18
• no high resolution temporal bone CT scan available for analysis
• unwillingness to participate in the study

Sponsors & Collaborators

• Hospices Civils de Lyon: lead

Study Sites (1)

Hospices Civils de Lyon, Centre Hospitalier Lyon sud, Service d'ORL, d'otoneurchirurgie et de chirurgie cervico-facaile

Pierre-Bénite, 69310, France

Location

MeSH Terms

Conditions

Otosclerosis

Condition Hierarchy (Ancestors)

Ear Diseases; Otorhinolaryngologic Diseases

Study Officials

• Maxime FIEUX, MD

  Hospices Civils de Lyon

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: May 17, 2023
• First Posted: August 14, 2023
• Study Start: July 1, 2022
• Primary Completion: May 1, 2023
• Study Completion: October 1, 2023
• Last Updated: August 14, 2023

Record last verified: 2023-05

Locations

FR (1)