Clinical trial NCT05987215: Interest of Using Deep Learning Algorithm for Otosclerosis Detection on Temporal Bone High Resolution CT
Official title: Interest of Using Deep Learning Algorithm for Otosclerosis Detection on Temporal Bone High Resolution CT
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Otosclerosis
Keywords: otosclerosis
IA
deep learning
temporal bone CT
Lead sponsor: Hospices Civils de Lyon
Sponsor class: OTHER
First posted: 2023-08-14
Enrollment: 240
Sex: ALL
Minimum age (years): 18
Maximum age (years): 110
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: CASE_CONTROL
Time perspective: RETROSPECTIVE
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.

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.

Eligibility 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

Primary outcomes:
measure: 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
timeFrame: through study completion, an average of 5 months
Secondary outcomes:
Other outcomes:
Locations:
city: Pierre-Bénite
country: France
facility: Hospices Civils de Lyon, Centre Hospitalier Lyon sud, Service d'ORL, d'otoneurchirurgie et de chirurgie cervico-facaile
Study officials:
affiliation: Hospices Civils de Lyon
name: Maxime FIEUX, MD
role: PRINCIPAL_INVESTIGATOR
References: