Purpose: Glaucoma is characterised by progressive irreversible vision loss which can be prevented or delayed through timely
interventions aimed at lowering intraocular pressure (IOP). Visual field (VF) testing, optic nerve assessment and IOP are the most
important indicators used for determining the time of interventions. We investigated how VF, IOP and RNFL thickness  can be used with a deep
learning algorithm to predict the timing of a glaucoma intervention.
Study Design: Retrospective database study
Methods: Patients with primary open-angle, pseudoexfoliation, pigmentary and normal pressure glaucoma seen at the Glaucoma
Clinic at Toronto Western Hospital with reliable VFs and a minimum of 10 reliable VFs were included. Data collected for each visit
included age, VFs (Humphrey SITA-Standard 24-2) ,RNFL thickness and IOP. In addition laser trabeculoplasty and glaucoma surgical interventions
were recorded. IOP ,VF and OCT data from four consecutive visits were used to train a convolutional neural network (CNN) in a 3-fold
cross validation scheme to generate the probability of an intervention following the 4th visit(the most recent visit).Three different
CNN 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 a
total of 115 interventions;29.6% laser trabeculoplasties,21.7% trabeculectomies,17.4% phacoemulsification with trabeculectomy and
6.9% Ahmed glaucoma valve surgeries. The CNN-IOP network predicted the timing of intervention with high sensitivity (0.96) and
low specificity (0.36), as it suffers from a high false positive rate. The CNN-VF network predicted the timing of intervention with low
sensitivity (0.45) and relatively high specificity (0.79), as it suffers from a high false negative rate. When the CNN-all was used, the
timing of intervention could be predicted with relatively high sensitivity (0.83) and specificity (0.75).The area under the curve of the
predictor 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 include
parameters from optic nerve assessments.
Conclusions: To our knowledge, this study is the first attempt to apply deep learning to predict the timing of glaucoma procedural
interventions. This approach can provide e!ective supplementary information to guide glaucoma specialists in complex cases.

Predicting glaucoma interventions with deep learning networks

[Poster Abstract Booklet](https://drive.google.com/file/d/1AGitRGyG9W0FiZ2Xjz6RNO2h7C0XglOL/view)

[Surgical Video Abstract Booklet](https://drive.google.com/file/d/1B-IkojrZhXk4AgXzsEQyuE_FwPG1Lvpe/view?usp=sharing)

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