Artificial Intelligence and Machine Learning in Ocular Oncology, Retinoblastoma (ArMOR): Experience with a Multiracial Cohort.

Abstract

Background: The color variation in fundus images from differences in melanin concentrations across races can affect the accuracy of artificial intelligence and machine learning (AI/ML) models. Hence, we studied the performance of our AI model (with proven efficacy in an Asian-Indian cohort) in a multiracial cohort for detecting and classifying intraocular RB (iRB). Methods: Retrospective observational study. Results: Of 210 eyes, 153 (73%) belonged to White, 37 (18%) to African American, 9 (4%) to Asian, 6 (3%) to Hispanic races, based on the U.S. Office of Management and Budget's Statistical Policy Directive No.15 and 5 (2%) had no reported race. Of the 2473 images in 210 eyes, 427 had no tumor, and 2046 had iRB. After training the AI model based on race, the sensitivity and specificity for detection of RB in 2473 images were 93% and 96%, respectively. The sensitivity and specificity of the AI model were 74% and 100% for group A; 88% and 96% for group B; 88% and 100% for group C; 73% and 98% for group D, and 100% and 92% for group E, respectively. Conclusions: The AI models built on a single race do not work well for other races. When retrained for different races, our model exhibited high sensitivity and specificity in detecting RB and classifying RB.