Interest of Using Deep Learning Algorithm for Otosclerosis Detection on Temporal Bone High Resolution CT
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 入组人数
- 240
- 试验地点
- 1
- 主要终点
- 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
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Control
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 110 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- 未提供
排除标准
- 未提供
结局指标
主要结局
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
时间窗: through study completion, an average of 5 months
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
次要结局
未报告次要终点