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

NCT05987215 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 240

Last updated 2023-08-14

No results posted yet for this study

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

Interventions

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

Sponsors & Collaborators

  • Hospices Civils de Lyon

    lead OTHER

Principal Investigators

  • Maxime FIEUX, MD · Hospices Civils de Lyon

Eligibility

Min Age
18 Years
Max Age
110 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2022-07-01
Primary Completion
2023-05-01
Completion
2023-10-01

Countries

  • France

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05987215 on ClinicalTrials.gov