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Deep Learning in Retinoblastoma Detection and Monitoring.

NCT05308043 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 200

Last updated 2022-04-01

No results posted yet for this study

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.

Conditions

  • Retinoblastoma

Interventions

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.

Sponsors & Collaborators

  • Beijing Tongren Hospital

    lead OTHER

Eligibility

Min Age
0 Years
Max Age
5 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2020-03-01
Primary Completion
2022-05-01
Completion
2022-10-01

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05308043 on ClinicalTrials.gov