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Discriminating Between Papilledema and Optic Disc Drusen Using 3D Structural Analysis of the Optic Nerve Head.

Neurology 2022 September 30
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: (1) To develop a deep learning algorithm to automatically identify major structures of the optic nerve head (ONH) in three dimensional (3D) optical coherence tomography (OCT) scans; (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 three sites (Singapore, Denmark, Australia) with confirmed ODD, papilledema due to raised intracranial pressure, and healthy controls. Raster scans of the ONH were acquired using OCT imaging, 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, 3: healthy) 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 (AUC) 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, i.e. 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 AI 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.

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