Deep Learning in Retinoblastoma Detection and Monitoring.

Retinoblastoma Clinical Trial

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

Status Recruiting

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.

Clinical Trial 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. ;

Related Conditions & MeSH terms

• Retinoblastoma

• NCT number: NCT05308043
• Study type: Observational
• Source: Beijing Tongren Hospital
• Contact: Wenbin Wei, MD
• Phone: 010-58269523
• Email: weiwenbintr@163.com
• Status: Recruiting
• Start date: March 1, 2020
• Completion date: October 1, 2022