NCT05308043

Deep Learning in Retinoblastoma Detection and Monitoring.

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

• Status: Unknown
• Phase: —
• Study type: Observational
• Enrollment: 200 (estimated)
• Sponsor: Beijing Tongren Hospital · Academic / Other
• Sex: All
• Age: 0 Years – 5 Years
• Healthy volunteers: Not accepted

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.

Conditions

• Retinoblastoma

Interventions

Type	Name	Description
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.

Timeline

• Start date: 2020-03-01
• Primary completion: 2022-05-01
• Completion: 2022-10-01
• First posted: 2022-04-01
• Last updated: 2022-04-01

Locations

1 site across 1 country: China