Deep Learning in Retinoblastoma Detection and Monitoring.
Study Details
Study Description
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.
Condition or Disease | Intervention/Treatment | Phase |
---|---|---|
|
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 Design
Arms and Interventions
Arm | Intervention/Treatment |
---|---|
Retinoblastoma patients 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. |
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.
|
Outcome Measures
Primary Outcome Measures
- 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
Eligibility Criteria
Criteria
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.
Contacts and Locations
Locations
Site | City | State | Country | Postal Code | |
---|---|---|---|---|---|
1 | Wen-Bin Wei | Beijing | Beijing | China | 100730 |
Sponsors and Collaborators
- Beijing Tongren Hospital
Investigators
None specified.Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- AI in retinoblastoma