Brief Title
Deep Learning in Retinoblastoma Detection and Monitoring.
Official Title
Deep Learning Computer-aided Detection System for Retinoblastoma Detection and Monitoring.
Brief Summary
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.
Detailed Description
Retinoblastoma, the most common eye cancer of childhood, affects 1 in 15 000 to 1 in 18 000 live births. China has the second-largest number of patients with retinoblastoma in the world. Eye-preserving therapies have been used widely in China for approximately 15 years. Eye-preserving therapies require routine monitoring of retinoblastoma regression and recurrence to guide corresponding treatment. However, the major amount of qualified ophthalmologists are concentrated in several medical centres. Deep learning based on Retcam examination that can identify retinoblastoma will reduce screening accuracy of the local hospitals and reduce monitoring wordload. In the current study, a deep learning algorism was developed 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.
Study Type
Observational
Primary Outcome
Diagnosis accurcy of deep learning algorism
Condition
Retinoblastoma
Intervention
Deep learning algorism
Study Arms / Comparison Groups
Retinoblastoma patients
Description: Retinoblastoma patients who undergo standard medical care in Beijing Tongren Hospital. The anonymous image of these patients will be prospectively collected and labelled by senior ophthalmologists.
Publications
* Includes publications given by the data provider as well as publications identified by National Clinical Trials Identifier (NCT ID) in Medline.
Recruitment Information
Recruitment Status
Diagnostic Test
Estimated Enrollment
200
Start Date
March 1, 2020
Completion Date
October 1, 2022
Primary Completion Date
May 1, 2022
Eligibility Criteria
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.
Gender
All
Ages
N/A - 5 Years
Accepts Healthy Volunteers
No
Contacts
, 010-58269523, [email protected]
Location Countries
China
Location Countries
China
Administrative Informations
NCT ID
NCT05308043
Organization ID
AI in retinoblastoma
Responsible Party
Principal Investigator
Study Sponsor
Beijing Tongren Hospital
Study Sponsor
, ,
Verification Date
March 2022