Retinoblastoma Clinical Trial
Official title:
Deep Learning Computer-aided Detection System for Retinoblastoma Detection and Monitoring.
Retinoblastoma is the most common eye cancer of childhood. Eye-preserving therapies require routine monitoring of retinoblastoma regression and recurrence to guide corresponding treatment. In the current study, we develop a deep learning algorism that can simultaneously identify retinoblastoma tumours on Retcam images and distinguish between active and inactive retinoblastoma tumours. This algorism will be validated through a prospectively collected dataset.
Status | Recruiting |
Enrollment | 200 |
Est. completion date | October 1, 2022 |
Est. primary completion date | May 1, 2022 |
Accepts healthy volunteers | No |
Gender | All |
Age group | N/A to 5 Years |
Eligibility | Inclusion Criteria: - Retinoblastoma patients undergo standard medical management. Exclusion Criteria: - The operators identified images non-assessable for a correct diagnosis, due to reasons such as blur and defocus, and excluded them from further analysis. |
Country | Name | City | State |
---|---|---|---|
China | Wen-Bin Wei | Beijing | Beijing |
Lead Sponsor | Collaborator |
---|---|
Beijing Tongren Hospital |
China,
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Diagnosis accurcy of deep learning algorism | The diagnosic accurcy of this deep learning algorism is the proportion of true positive and true negative in all evaluated cases | 1 week |
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