Glaucoma Precognition Based on Confocal Scanning Laser Ophthalmoscopy Images of the Optic Disc Using Convolutional Neural Network
We develop an Artificial Intelligence (AI) framework for glaucoma precognition from baseline confocal scanning laser ophthalmoscopy imaging data, using a convolutional neural network (CNN) model. The proposed framework extracts 'deep features' from convolutional layers of the CNN model, which are used as input to the ensemble learning classifier in order to identify patients that will likely convert to glaucoma after few years. The prediction model achieved area under the receiver operating characteristic curve (AUC) of 0.83 using the data from baseline visit. The model predicted the onset of glaucoma more accurately than known glaucoma risk factors, Glaucoma Probability Score (GPS) and Moorfields Regression Analysis (MRA) parameters of the Heidelberg Retinal Tomograph (HRT) software. The proposed AI construct provides a highly specific and sensitive model that can predict the onset of glaucoma from baseline HRT parameters and has the potential to provide clinicians valuable information regarding the onset of glaucoma.