Retina (Philadelphia, Pa.) · 2026
ARTIFICIAL INTELLIGENCE FOR THE DETECTION OF MACULOPATHY IN PEDIATRIC PATIENTS WITH SICKLE CELL DISEASE.
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Hoyek Sandra, Chaaya Celine, Abidi Muhammad, Lamarque Francisco Altamirano, Meshkin Ryan S, Giridharan Varsha, Shah Kavach, Gonzalez Efren, Pinsky Eugene, Patel Nimesh
Аннотация
PURPOSE: To determine the feasibility of developing an artificial intelligence (AI) algorithm based on optical coherence tomography (OCT) images as an automated screening tool for diagnosing retinal thinning in children with sickle cell disease (SCD).
METHODS: This retrospective consecutive series included children with SCD who had an ophthalmic examination at a Pediatric Tertiary Care Hospital, including OCT imaging between January 1998 and August 2022. Three different machine learning algorithms were evaluated: logistic regression, K-Nearest Neighbors (KNN), and random forest.
RESULTS: A total of 348 OCT scans from 174 eyes of 87 patients (54% males) were included. Using the original data set, KNN algorithm outperformed both the random forest and logistic regression algorithms when using two OCT scans per patient. However, with cross-validation, this model's accuracy dropped to 77.11%. When duplicating the data set's values, the random forest algorithm performed best, demonstrating the highest accuracy after cross-validation of 96.0%, AUC, sensitivity, specificity, and a F1 score all reaching 1, when using one OCT scan per patient.
CONCLUSION: AI-based analysis of OCT imaging is a promising tool in the early detection of sickle cell maculopathy in the pediatric population.
METHODS: This retrospective consecutive series included children with SCD who had an ophthalmic examination at a Pediatric Tertiary Care Hospital, including OCT imaging between January 1998 and August 2022. Three different machine learning algorithms were evaluated: logistic regression, K-Nearest Neighbors (KNN), and random forest.
RESULTS: A total of 348 OCT scans from 174 eyes of 87 patients (54% males) were included. Using the original data set, KNN algorithm outperformed both the random forest and logistic regression algorithms when using two OCT scans per patient. However, with cross-validation, this model's accuracy dropped to 77.11%. When duplicating the data set's values, the random forest algorithm performed best, demonstrating the highest accuracy after cross-validation of 96.0%, AUC, sensitivity, specificity, and a F1 score all reaching 1, when using one OCT scan per patient.
CONCLUSION: AI-based analysis of OCT imaging is a promising tool in the early detection of sickle cell maculopathy in the pediatric population.