Prior optic neuritis detection on peripapillary ring scans using deep learning

• Published in: Annals of clinical and translational neurology Vol. 9; no. 11; pp. 1682 - 1691
• Main Authors: Motamedi, Seyedamirhosein, Yadav, Sunil Kumar, Kenney, Rachel C., Lin, Ting‐Yi, Kauer‐Bonin, Josef, Zimmermann, Hanna G., Galetta, Steven L., Balcer, Laura J., Paul, Friedemann, Brandt, Alexander U.
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.11.2022; John Wiley and Sons Inc; Wiley
• Subjects: Age; Artificial intelligence; Biomarkers; Datasets; Deep Learning; Ethnicity; Hispanic Americans; Humans; Longitudinal studies; Multiple sclerosis; Multiple Sclerosis - diagnostic imaging; Neural networks; Optic nerve; Optic Neuritis - diagnostic imaging; Optics; Pacific Islander people; Patients; Retina; Tomography; Tomography, Optical Coherence - methods; White people; New York; United States > US
• ISSN: 2328-9503; 2328-9503
• DOI: 10.1002/acn3.51632

Background The diagnosis of multiple sclerosis (MS) requires demyelinating events that are disseminated in time and space. Peripapillary retinal nerve fiber layer (pRNFL) thickness as measured by optical coherence tomography (OCT) distinguishes eyes with a prior history of acute optic neuritis (ON) and may provide evidence to support a demyelinating attack. Objective To investigate whether a deep learning (DL)‐based network can distinguish between eyes with prior ON and healthy control (HC) eyes using peripapillary ring scans. Methods We included 1033 OCT scans from 415 healthy eyes (213 HC subjects) and 510 peripapillary ring scans from 164 eyes with prior acute ON (140 patients with MS). Data were split into 70% training, 15% validation, and 15% test data. We included 102 OCT scans from 80 healthy eyes (40 HC) and 61 scans from 40 ON eyes (31 MS patients) from an independent second center. Receiver operating characteristic curve analyses with area under the curve (AUC) were used to investigate performance. Results We used a dilated residual convolutional neural network for the classification. The final network had an accuracy of 0.85 and an AUC of 0.86, whereas pRNFL only had an AUC of 0.77 in recognizing ON eyes. Using data from a second center, the network achieved an accuracy of 0.77 and an AUC of 0.90 compared to pRNFL, which had an AUC of 0.84. Interpretation DL‐based disease classification of prior ON is feasible and has the potential to outperform thickness‐based classification of eyes with and without history of prior ON.