Deep Learning in Retinoblastoma Detection and Monitoring.

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 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

0 Years - 5 Years

Accepts Healthy Volunteers

No

Contacts

, 010-58269523, [email protected]

Location Countries

China

Location Countries

China

NCT ID

NCT05308043

Organization ID

AI in retinoblastoma

Responsible Party

Principal Investigator

Study Sponsor

Beijing Tongren Hospital

Study Sponsor

, , 

Verification Date

March 2022