Artificial intelligence and machine learning in ocular oncology: Retinoblastoma
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wolters Kluwer Medknow Publications
2023-01-01
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| Series: | Indian Journal of Ophthalmology |
| Subjects: | |
| Online Access: | http://www.ijo.in/article.asp?issn=0301-4738;year=2023;volume=71;issue=2;spage=424;epage=430;aulast=Kaliki |
| Summary: | 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. |
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| ISSN: | 0301-4738 1998-3689 |