Abstract
Purpose :
The goal of this study is to develop an artificial intelligence (AI) algorithm based on fluorescein angiography (FA) and color fundus images to predict the need for treatment in infants with retinopathy of prematurity (ROP).
Methods :
All available color fundus photos, FA images and clinical data of the babies with ROP at the University of Florida (UF) Shands Hospital between 2012 and 2022 will be reviewed. The AI model was developed using convolutional neural network (CNN - based on VGG16 architecture) for feature extraction of images. The results were concatenated with the corresponding medical data, e.g., gestational age and birth weight. Finally, a dense neural network was utilized to predict the patient's ROP zone, stage and plus status. See Image 1.
Results :
The AI model was trained with images and data from eight infants (16 eyes) and then tested on 3 infants (6 eyes).
The model has high accuracy to predict the zone, stage and plus status. The prediction accuracy is best when using fundus photographs and fluorescein angiographies together compared to only using one type of image input. The model can predict the zone and stage of ROP with 83.33% accuracy and the plus status with 100% accuracy. See Table 1.
Next, study staff will include color fundus photos, FA images and clinical data from 62 additional infants into the AI prediction model for further training. The AI prediction model will then undergo validation noting statistical outcomes for the prediction of ROP zone, stage and plus status.
Conclusions :
Our results have demonstrated that we have developed an AI algorithm based on fundus imaging, fluorescein angiography and demographic data which has high diagnostic performance for detection of infants who require treatment for ROP. The accuracy of the model will continue to improve as trains and learns from more subjects. This AI model can be used to help improve diagnostic accuracy in neonatal intensive care units around the world. Further versions of the algorithm will be able to detect progression or regression of disease following ROP treatment.
This abstract was presented at the 2023 ARVO Annual Meeting, held in New Orleans, LA, April 23-27, 2023.