Deep Learning to Discriminate Arteritic From Nonarteritic Ischemic Optic Neuropathy on Color Images
Authors: Gungor Ayse , Najjar Raymond P. , Hamann Steffen , Tang Zhiqun , Lagrèze Wolf A. , Sadun Riccardo , Sathianvichitr Kanchalika , Dinkin Marc J. , Oliveira Cristiano , Li Anfei , Sadun Federico , Carey Andrew R. , Bouthour Walid , Lin Mung Yan , Loo Jing-Liang , Miller Neil R. , Newman Nancy J. , Biousse Valérie , Milea Dan ,   BONSAI Group, Petzold Axel , Gohier Philippe , Patil Ajay , Padungkiatsagul Tanyatuth , Suwan Yanin , Vanikieti Kavin , Barboni Piero , Carelli Valerio , La Morgia Chiara , Rougier Marie-Bénédicte , Costello Fiona , Bénard-Séguin Étienne , Milea Leonard B. , Ambika Selvakumar , Fonseca Pedro , Hansen Michael S. , Küchlin Sebastian , Farassat Navid , Sanda Nicolae , Thumann Gabriele , Chiquet Christophe , Yang Hui , Cheung Carol Y. , Chan Carmen KM. , Nakamura Makoto , Fumio Takano , Tran Thi HC. , Jurkute Neringa , Yu-Wai-Man Patrick , Kho Richard , Jonas Jost B. , Mejico Luis J. , Clermont-Vignal Catherine , Khanna Raoul K. , Lamirel Cedric , Touitou Valérie , Chen John J. , Hwang Jeong-Min , Yang Hee K. , Kim Dong H. , Aung Tin , Lamoureux Ecosse , Schmetterer Leopold , Leng Christine YW. , Girard Michael JA. , Fraser Clare L. , Fard Masoud A. , Micieli Jonathan A. 
Published: October 2024

Importance

Prompt and accurate diagnosis of arteritic anterior ischemic optic neuropathy (AAION) from giant cell arteritis and other systemic vasculitis can contribute to preventing irreversible vision loss from these conditions. Its clinical distinction from nonarteritic anterior ischemic optic neuropathy (NAION) can be challenging, especially when systemic symptoms are lacking or laboratory markers of the disease are not reliable.

Objective

To develop, train, and test a deep learning system (DLS) to discriminate AAION from NAION on color fundus images during the acute phase.

Design, Setting, and Participants

This was an international study including color fundus images of 961 eyes of 802 patients with confirmed AAION and NAION. Training was performed using images from 21 expert neuro-ophthalmology centers in 16 countries, while external testing was performed in a cohort from 5 expert neuro-ophthalmology centers in the US and Europe. Data for training and external testing were collected from August 2018 to January 2023. A mix of deidentified images of 2 fields of view (optic disc centered and macula centered) were used. For training and internal validation, images were from 16 fundus camera models with fields of 30° to 55°. For external testing, images were from 5 fundus cameras with fields of 30° to 50°. Data were analyzed from January 2023 to January 2024.

Main Outcomes and Measures

The performance of the DLS was measured using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy.

Results

In the training and validation sets, 374 (54.9%) of patients were female, 301 (44.2%) were male, and 6 (0.9%) were of unknown sex; the median (range) age was 66 (23-96) years. When tested on the external dataset including 121 patients (35 [28.9%] female, 44 [36.4%] male, and 42 [34.7%] of unknown sex; median [range] age, 69 [37-89] years), the DLS achieved an AUC of 0.97 (95% CI, 0.95-0.99), a sensitivity of 91.1% (95% CI, 85.2-96.9), a specificity of 93.4% (95% CI, 91.1-98.2), and an accuracy of 92.6% (95% CI, 90.5-96.6). The accuracy of the 2 experts for classification of the same dataset was 74.3% (95% CI, 66.7-81.9) and 81.6% (95% CI, 74.8-88.4), respectively.

Conclusions and Relevance

A DLS showing disease-specific averaged class-activation maps had greater than 90% accuracy at discriminating between acute AAION from NAION on color fundus images, at the eye level, without any clinical or biomarker information. A DLS that identifies AAION could improve clinical decision-making, potentially reducing the risk of misdiagnosis and improving patient outcomes.