Deep Learning in Retinoblastoma Detection and Monitoring.

Capital Medical University

Status: Unknown

Conditions: Retinoblastoma

Treatments: Diagnostic Test: Deep learning algorism

Study type: Observational

Funder types: Other

Identifiers

NCT05308043

AI in retinoblastoma

Details and patient eligibility

About

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.

Full 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.

Enrollment: 200 estimated patients

Sex: All

Ages: Under 5 years old

Volunteers: No Healthy Volunteers

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.

Trial design

200 participants in 1 patient group

Retinoblastoma patients

Description:

Retinoblastoma patients who undergo standard medical care in Beijing Tongren Hospital. The anonymous image of these patients will be prospectively collected and labelled by senior ophthalmologists.

Treatment:

Diagnostic Test: Deep learning algorism