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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT05308043
Other study ID # AI in retinoblastoma
Secondary ID
Status Recruiting
Phase
First received
Last updated
Start date March 1, 2020
Est. completion date October 1, 2022

Study information

Verified date March 2022
Source Beijing Tongren Hospital
Contact Wenbin Wei, MD
Phone 010-58269523
Email weiwenbintr@163.com
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial 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.


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.


Recruitment information / eligibility

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.

Study Design


Related Conditions & MeSH terms


Intervention

Diagnostic Test:
Deep learning algorism
A deep learning algorism that was developed previous would be applied to identify retinoblastoma tumours on Retcam images and distinguish between active and inactive retinoblastoma tumours. The decision of two different senior ophthalmologists would be the gold standard.

Locations

Country Name City State
China Wen-Bin Wei Beijing Beijing

Sponsors (1)

Lead Sponsor Collaborator
Beijing Tongren Hospital

Country where clinical trial is conducted

China, 

Outcome

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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