Automatic Retinoblastoma Screening and Surveillance Using Deep Learning

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Abstract
Retinoblastoma is the most common intraocular malignancy in childhood. With the advanced management strategy, the global salvage and overall survival have significantly improved, which proposes subsequent challenges regarding long-term surveillance and offspring screening. Here, we developed deep learning algorithm, called Deep Learning Assistant for Retinoblastoma (DLA-RB) training on A total of 36623 images from 713 patients. We validated it in the prospectively collected dataset, comprised of 1366 images form 139 eyes of 103 patients. In identifying active retinoblastoma tumors (treatment required) from all clinical-suspected patients, the area under the receiver operating characteristic curve (AUC) of DLA-RB reached 0.991 (95% CI 0.970-1.000). In identifying active retinoblastoma from stable retinoblastoma patients (treatment is not required), AUC of DLA-RB reached 0.962 (95% CI 0.915-1.000), respectively. Cost-utility analysis revealed that DLA-RB based diagnosis mode is more cost-effective in both retinoblastoma diagnosis and retinoblastoma activity surveillance. The DLA-RB achieved high accuracy and sensitivity in identifying active retinoblastoma from the normal and stable retinoblastoma fundus. It can be incorporated within telemedicine programs in the future.
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