Artificial intelligence and machine learning in ocular oncology: Retinoblastoma

Le résumé fourni par la source

Purpose: This study was done to explore the utility of artificial intelligence (AI) and machine learning in the diagnosis and grouping of intraocular retinoblastoma (iRB). Methods: It was a retrospective observational study using AI and Machine learning, Computer Vision (OpenCV). Results: Of 771 fundus images of 109 eyes, 181 images had no tumor and 590 images displayed iRB based on review by two independent ocular oncologists (with an interobserver variability of <1%). The sensitivity, specificity, positive predictive value, and negative predictive value of the trained AI model were 85%, 99%, 99.6%, and 67%, respectively. Of 109 eyes, the sensitivity, specificity, positive predictive value, and negative predictive value for detection of RB by AI model were 96%, 94%, 97%, and 91%, respectively. Of these, the eyes were normal (n = 31) or belonged to groupA (n=1), B (n=22), C (n=8), D (n=23),and E (n=24) RB based on review by two independent ocular oncologists (with an interobserver variability of 0%). The sensitivity, specificity, positive predictive value, and negative predictive value of the trained AI model were 100%, 100%, 100%, and 100% for group A; 82%, 20 21 98%, 90%, and 96% for group B; 63%, 99%, 83%, and 97% for group C; 78%, 98%, 90%, and 94% for group D, and 92%, 91%, 73%, and 98% for group E, respectively. Conclusion: Based on our study, we conclude that the AI model for iRB is highly sensitive in the detection of RB with high specificity for the classification of iRB.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.