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

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 coh...

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Bibliographic Details
Published in:Cancers 2024-10, Vol.16 (20), p.3516
Main Authors: Vempuluru, Vijitha S, Viriyala, Rajiv, Ayyagari, Virinchi, Bakal, Komal, Bhamidipati, Patanjali, Dhara, Krishna Kishore, Ferenczy, Sandor R, Shields, Carol L, Kaliki, Swathi
Format: Article
Language:English
Subjects:
Accountability
Accuracy
African Americans
Algorithms
Artificial intelligence
Blood vessels
Cancer in children
Care and treatment
Children
Classification
Computer vision
Deep learning
Diagnosis
Ethnicity
Eye
Hemorrhage
Hispanic Americans
Image processing
Learning algorithms
Machine learning
Melanin
Oncology
Patients
Pediatric research
Retinoblastoma
Technology application
Tumors
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Summary: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). Retrospective observational study. 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. 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.
ISSN:2072-6694
2072-6694
DOI:10.3390/cancers16203516