Simple excision for the management of limbal dermoids in young children

Purpose: Glaucoma is characterised by progressive irreversible vision loss which can be prevented or delayed through timelyinterventions aimed at lowering intraocular pressure (IOP). Visual field (VF) testing, optic nerve assessment and IOP are the mostimportant indicators used for determining the time of interventions. We investigated how VF, IOP and RNFL thickness can be used with a deeplearning algorithm to predict the timing of a glaucoma intervention.Study Design: Retrospective database studyMethods: Patients with primary open-angle, pseudoexfoliation, pigmentary and normal pressure glaucoma seen at the GlaucomaClinic at Toronto Western Hospital with reliable VFs and a minimum of 10 reliable VFs were included. Data collected for each visitincluded age, VFs (Humphrey SITA-Standard 24-2) ,RNFL thickness and IOP. In addition laser trabeculoplasty and glaucoma surgical interventionswere recorded. IOP ,VF and OCT data from four consecutive visits were used to train a convolutional neural network (CNN) in a 3-foldcross validation scheme to generate the probability of an intervention following the 4th visit(the most recent visit).Three differentCNN networks were evaluated: a) CNN-IOP - using only IOP, b) CNN-VF - using only VF, c) CNN-all using IOP+VF+age.Results: Data from 2743 visits of 84 patients were collected with a mean follow-up of 14 years (range 5-30 years).There were atotal of 115 interventions;29.6% laser trabeculoplasties,21.7% trabeculectomies,17.4% phacoemulsification with trabeculectomy and6.9% Ahmed glaucoma valve surgeries. The CNN-IOP network predicted the timing of intervention with high sensitivity (0.96) andlow specificity (0.36), as it suffers from a high false positive rate. The CNN-VF network predicted the timing of intervention with lowsensitivity (0.45) and relatively high specificity (0.79), as it suffers from a high false negative rate. When the CNN-all was used, thetiming of intervention could be predicted with relatively high sensitivity (0.83) and specificity (0.75).The area under the curve of thepredictor was 0.86. Our study suggests that age, VF assessments,IOP and OCT measurements are essential for the model predictions.The performance of the predictor might be improved with more training examples and by expanding the training data to includeparameters from optic nerve assessments.Conclusions: To our knowledge, this study is the first attempt to apply deep learning to predict the timing of glaucoma proceduralinterventions. This approach can provide e!ective supplementary information to guide glaucoma specialists in complex cases.

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