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Discriminating Between Papilledema and Optic Disc Drusen Using 3D Structural Analysis of the Optic Nerve Head
Michaël J A Girard1, Satish Panda2, Tin Aung Tun2
1From the Ophthalmic Engineering & Innovation Laboratory (M.J.A.G., S.P.), Singapore Eye Research Institute (T.A.T., R.P.N., T.A., D.M.), Singapore National Eye Centre; Duke-NUS Graduate Medical School (M.J.A.G., T.A.T., R.P.N., T.A., D.M.), Singapore; Institute for Molecular and Clinical Ophthalmology (M.J.A.G.), Basel, Switzerland; Department of Ophthalmology (E.A.W., S.H.), Rigshospitalet, University of Copenhagen, Denmark; Yong Loo Lin School of Medicine (R.P.N., T.A.), and Department of Statistics and Applied Probability (A.H.T.), National University of Singapore; and Save Sight Institute (C.F.), Faculty of Health and Medicine, The University of Sydney, New South Wales, Australia. mgirard@ophthalmic.engineering.
Background And Objectives:
The distinction of papilledema from other optic nerve head (ONH) lesions mimicking papilledema, such as optic disc drusen (ODD), can be difficult in clinical practice. We aimed the following: (1) to develop a deep learning algorithm to automatically identify major structures of the ONH in 3-dimensional (3D) optical coherence tomography (OCT) scans and (2) to exploit such information to robustly differentiate among ODD, papilledema, and healthy ONHs.
Methods:
This was a cross-sectional comparative study of patients from 3 sites (Singapore, Denmark, and Australia) with confirmed ODD, those with papilledema due to raised intracranial pressure, and healthy controls. Raster scans of the ONH were acquired using OCT imaging and then processed to improve deep-tissue visibility. First, a deep learning algorithm was developed to identify major ONH tissues and ODD regions. The performance of our algorithm was assessed using the Dice coefficient. Second, a classification algorithm (random forest) was designed to perform 3-class classifications (1: ODD, 2: papilledema, and 3: healthy ONHs) strictly from their drusen and prelamina swelling scores (calculated from the segmentations). To assess performance, we reported the area under the receiver operating characteristic curve for each class.
Results:
A total of 241 patients (256 imaged ONHs, including 105 ODD, 51 papilledema, and 100 healthy ONHs) were retrospectively included in this study. Using OCT images of the ONH, our segmentation algorithm was able to isolate neural and connective tissues and ODD regions/conglomerates whenever present. This was confirmed by an averaged Dice coefficient of 0.93 ± 0.03 on the test set, corresponding to good segmentation performance. Classification was achieved with high AUCs, that is, 0.99 ± 0.001 for the detection of ODD, 0.99 ± 0.005 for the detection of papilledema, and 0.98 ± 0.01 for the detection of healthy ONHs.
Discussion:
Our artificial intelligence approach can discriminate ODD from papilledema, strictly using a single OCT scan of the ONH. Our classification performance was very good in the studied population, with the caveat that validation in a much larger population is warranted. Our approach may have the potential to establish OCT imaging as one of the mainstays of diagnostic imaging for ONH disorders in neuro-ophthalmology, in addition to fundus photography.
