Deep Learning in Retinoblastoma Detection and Monitoring.

Retinoblastoma Clinical Trial

Official title:

Deep Learning Computer-aided Detection System for Retinoblastoma Detection and Monitoring.

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05308043
• Other study ID #: AI in retinoblastoma
• Status: Recruiting
• 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
• 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.

Related Conditions & MeSH terms

• Retinoblastoma

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