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DOI 10.1097/wno.0000000000002223

Медицинская библиотека · Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society · 2024

A Deep Learning Approach for Accurate Discrimination Between Optic Disc Drusen and Papilledema on Fundus Photographs.

Sathianvichitr Kanchalika, Najjar Raymond P, Zhiqun Tang, Fraser J Alexander, Yau Christine W L, Girard Michael J A, Costello Fiona, Lin Mung Y, Lagrèze Wolf A, Vignal-Clermont Catherine, Fraser Clare L, Hamann Steffen, Newman Nancy J, Biousse Valérie, Milea Dan, BONSAI Group

СтатьяМногоцентровое исследованиеПоддержка исследования: вне правительства СШАПеревод пока не подготовлен

Аннотация

BACKGROUND

Optic disc drusen (ODD) represent an important differential diagnosis of papilledema caused by intracranial hypertension, but their distinction may be difficult in clinical practice. The aim of this study was to train, validate, and test a dedicated deep learning system (DLS) for binary classification of ODD vs papilledema (including various subgroups within each category), on conventional mydriatic digital ocular fundus photographs collected in a large international multiethnic population.

METHODS

This retrospective study included 4,508 color fundus images in 2,180 patients from 30 neuro-ophthalmology centers (19 countries) participating in the Brain and Optic Nerve Study with Artificial Intelligence (BONSAI) Group. For training and internal validation, we used 857 ODD images and 3,230 papilledema images, in 1,959 patients. External testing was performed on an independent data set (221 patients), including 207 images with ODD (96 visible and 111 buried), provided by 3 centers of the Optic Disc Drusen Studies Consortium, and 214 images of papilledema (92 mild-to-moderate and 122 severe) from a previously validated study.

RESULTS

The DLS could accurately distinguish between all ODD and papilledema (all severities included): area under the receiver operating characteristic curve (AUC) 0.97 (95% confidence interval [CI], 0.96-0.98), accuracy 90.5% (95% CI, 88.0%-92.9%), sensitivity 86.0% (95% CI, 82.1%-90.1%), and specificity 94.9% (95% CI, 92.3%-97.6%). The performance of the DLS remained high for discrimination of buried ODD from mild-to-moderate papilledema: AUC 0.93 (95% CI, 0.90-0.96), accuracy 84.2% (95% CI, 80.2%-88.6%), sensitivity 78.4% (95% CI, 72.2%-84.7%), and specificity 91.3% (95% CI, 87.0%-96.4%).

CONCLUSIONS

A dedicated DLS can accurately distinguish between ODD and papilledema caused by intracranial hypertension, even when considering buried ODD vs mild-to-moderate papilledema.

Для цитирования: Sathianvichitr Kanchalika, Najjar Raymond P, Zhiqun Tang, Fraser J Alexander, Yau Christine W L, Girard Michael J A, Costello Fiona, Lin Mung Y, Lagrèze Wolf A, Vignal-Clermont Catherine, Fraser Clare L, Hamann Steffen, Newman Nancy J, Biousse Valérie, Milea Dan, BONSAI Group. A Deep Learning Approach for Accurate Discrimination Between Optic Disc Drusen and Papilledema on Fundus Photographs.. Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society. 2024. DOI: 10.1097/wno.0000000000002223.

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