Deep Learning in Retinoblastoma Detection and Monitoring.

• Conditions: Retinoblastoma

• Registration Number: NCT05308043
• Lead Sponsor: Beijing Tongren Hospital

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 Timeline

• Study Start: 2020-03-01
• Status Verified: 2022-03
• Study First Submitted: 2022-03-24
• Study First Submitted QC: 2022-03-24
• Last Update Submitted: 2022-03-24
• Study First Posted: 2022-04-01
• Last Update Posted: 2022-04-01
• Primary Completion: 2022-05-01
• Study Completion: 2022-10-01

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 200

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

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Diagnosis accurcy of deep learning algorism	1 week	The diagnosic accurcy of this deep learning algorism is the proportion of true positive and true negative in all evaluated cases

Trial Locations

Locations (1)

Wen-Bin Wei; 🇨🇳 Beijing, Beijing, China