2021/12/18 by Michaël J. A. Girard, Satish K. Panda, Girard, Michaël J. A. +17
Medicine · #Cerebral Venous Sinus Thrombosis #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Glaucoma and retinal disorders #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Traumatic Brain Injury and Neurovascular Disturbances #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2112.09970
openalex publication_date 2021/12/18 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/28
Purpose: (1) To develop a deep learning algorithm to identify major tissue structures of the optic nerve head (ONH) in 3D optical coherence tomography (OCT) scans; (2) to exploit such information to robustly differentiate among healthy, optic disc drusen (ODD), and papilledema ONHs. It was a cross-sectional comparative study with confirmed ODD (105 eyes), papilledema due to high intracranial pressure (51 eyes), and healthy controls (100 eyes). 3D scans of the ONHs were acquired using OCT, then processed to improve deep-tissue visibility. At first, a deep learning algorithm was developed using 984 B-scans (from 130 eyes) in order to identify: major neural/connective tissues, and ODD regions. The performance of our algorithm was assessed using the Dice coefficient (DC). In a 2nd step, a classification algorithm (random forest) was designed using 150 OCT volumes to perform 3-class classifications (1: ODD, 2: papilledema, 3: healthy) strictly from their drusen and prelamina swelling scores (derived from the segmentations). To assess performance, we reported the area under the receiver operating characteristic curves (AUCs) for each class. Our segmentation algorithm was able to isolate neural and connective tissues, and ODD regions whenever present. This was confirmed by an average DC of 0.93±0.03 on the test set, corresponding to good performance. Classification was achieved with high AUCs, i.e. 0.99±0.01 for the detection of ODD, 0.99 ± 0.01 for the detection of papilledema, and 0.98±0.02 for the detection of healthy ONHs. Our AI approach accurately discriminated ODD from papilledema, using a single OCT scan. Our classification performance was excellent, with the caveat that validation in a much larger population is warranted. Our approach may have the potential to establish OCT as the mainstay of diagnostic imaging in neuro-ophthalmology.