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.
背景：不同种族间黑色素浓度的差异导致眼底图像颜色变化，可能影响人工智能与机器学习（AI/ML）模型的准确性。因此，我们研究了在亚洲印度人群中已验证有效的AI模型，在多种族队列中检测和分类眼内视网膜母细胞瘤（iRB）的性能。方法：回顾性观察研究。结果：根据美国行政管理和预算局第15号统计政策指令，210只眼中153只（73%）属于白人，37只（18%）属于非裔美国人，9只（4%）属于亚洲人，6只（3%）属于西班牙裔，5只（2%）未报告种族。210只眼的2473张图像中，427张无肿瘤，2046张存在iRB。基于种族对AI模型进行训练后，在2473张图像中检测RB的敏感性和特异性分别为93%和96%。AI模型对各分组的敏感性和特异性分别为：A组74%和100%；B组88%和96%；C组88%和100%；D组73%和98%；E组100%和92%。结论：基于单一种族构建的AI模型在其他种族中表现不佳。经过针对不同种族的重新训练后，我们的模型在检测和分类RB方面展现出高敏感性和特异性。
肿瘤(癌症)患者之家