Application of a Deep Learning System to Detect Papilledema on Nonmydriatic Ocular Fundus Photographs in an Emergency Department - 16/04/24

Doi : 10.1016/j.ajo.2023.10.025

Valérie Biousse ^1,^2,^⁎ , Raymond P. Najjar ^6,^7,^8,⁹, Zhiqun Tang ⁶, Mung Yan Lin ¹, David W. Wright ³, Matthew T. Keadey ³, Tien Y. Wong ^6,^7,¹⁰, Beau B. Bruce ^1,^2,⁵, Dan Milea ^6,^7,^{^{#
Nancy J. Newman and Dan Milea contributed equally as co-senior authors. Members of the BONSAI Study Group can be found in Supplemental Material 1.}}, Nancy J. Newman ^1,^2,^4,^{^{#
Nancy J. Newman and Dan Milea contributed equally as co-senior authors. Members of the BONSAI Study Group can be found in Supplemental Material 1.}}
for the
BONSAI Study Group^{^#}
Nancy J. Newman and Dan Milea contributed equally as co-senior authors. Members of the BONSAI Study Group can be found in Supplemental Material 1.
Clare L. Fraser ¹¹, Jonathan A. Micieli ¹², Fiona Costello ¹³, Étienne Bénard-Séguin ¹³, Hui Yang ¹⁴, Carmen Kar Mun Chan ¹⁵, Carol Y Cheung ¹⁵, Noel CY Chan ¹⁵, Steffen Hamann ¹⁶, Philippe Gohier ¹⁷, Anaïs Vautier ¹⁷, Marie-Bénédicte Rougier ¹⁸, Christophe Chiquet ¹⁹, Catherine Vignal-Clermont ²⁰, Rabih Hage ²⁰, Raoul Kanav Khanna ²⁰, Thi Ha Chau Tran ²¹, Wolf Alexander Lagrèze ²², Jost B Jonas ²³, Selvakumar Ambika ²⁴, Masoud Aghsaei Fard ²⁵, Chiara La Morgia ²⁶, Michele Carbonelli ²⁶, Piero Barboni ²⁶, Valerio Carelli ²⁶, Martina Romagnoli ²⁶, Giulia Amore ²⁶, Makoto Nakamura ²⁷, Takano Fumio ²⁷, Axel Petzold ²⁸, Maillette de Buy Wenniger lj ²⁸, Richard Kho ²⁹, Pedro L. Fonseca ³⁰, Mukharram M. Bikbov ³¹, Dan Milea ^32,^33,³⁴, Raymond P Najjar ^32,^33,³⁴, Daniel Ting ^32,^33,³⁴, Zhiqun Tang ^32,^33,³⁴, Jing Liang Loo ^32,^33,³⁴, Sharon Tow ^32,^33,³⁴, Shweta Singhal ^32,^33,³⁴, Caroline Vasseneix ^32,^33,³⁴, Tien Yin Wong ^32,^33,³⁴, Ecosse Lamoureux ^32,^33,³⁴, Ching Yu Chen ^32,^33,³⁴, Tin Aung ^32,^33,³⁴, Leopold Schmetterer ^32,^33,³⁴, Nicolae Sanda ³⁵, Gabriele Thuman ³⁵, Jeong-Min Hwang ³⁶, Kavin Vanikieti ³⁷, Yanin Suwan ³⁷, Tanyatuth Padungkiatsagul ³⁷, Patrick Yu-Wai-Man ³⁸, Neringa Jurkute ³⁸, Eun Hee Hong ³⁸, Valerie Biousse ³⁹, Nancy J. Newman ³⁹, Jason H. Peragallo ³⁹, Michael Datillo ³⁹, Sachin Kedar ³⁹, Mung Yan Lin ³⁹, Ajay Patil ³⁹, Andre Aung ³⁹, Matthew Boyko ³⁹, Wael Abdulraman Alsakran ³⁹, Amani Zayani ³⁹, Walid Bouthour ³⁹, Ana Banc ³⁹, Rasha Mosley ³⁹, Fernando Labella ³⁹, Neil R. Miller ⁴⁰, John J. Chen ⁴¹, Luis J. Mejico ^42,⁴³, Janvier Ngoy Kilangalanga ⁴⁴
¹¹ Save Sight Institute, Faculty of Health and Medicine, The University of Sydney, NSW Australia
¹² Kensington Eye Institute, St. Michael's Hospital, Toronto Western Hospital, Toronto, Canada
¹³ Departments of Clinical Neurosciences and Surgery, University of Calgary, Canada
¹⁴ Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, P.R. China
¹⁵ Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
¹⁶ Department of Ophthalmology, Rigshospitalet, University of Copenhagen, Glostrup, Denmark
¹⁷ Department of Ophthalmology, University Hospital Angers, Angers, France
¹⁸ Department of Ophthalmology, Hôpital Pellegrin, CHU de Bordeaux, Bordeaux, France
¹⁹ Department of Ophthalmology, Grenoble Alpes University Hospital, Grenoble, France
²⁰ Department of Ophthalmology, Fondation Adolphe de Rothschild, Paris, France
²¹ Department of Ophthalmology, Lille Catholic Hospital, Lille Catholic University and Inserm U1171, Lille, France
²² Eye Center, Medical Center, University of Freiburg, Freiburg, Germany
²³ Department of Ophthalmology, Medical Faculty Mannheim of the Ruprecht-Karls-University of Heidelberg, Mannheim, Germany
²⁴ Department of Neuro-ophthalmology, Sankara Nethralaya, Medical Research Foundation, Chennai, India
²⁵ Farabi Eye Hospital, Tehran University of Medical Science, Tehran, Iran
²⁶ IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
²⁷ Department of Surgery, Division of Ophthalmology. Kobe University Graduate School of Medicine, Kobe, Japan
²⁸ Neuro-ophthalmology Expert Centre, Amsterdam University Medical Center, Amsterdam, Netherlands
²⁹ American Eye Center, Mandaluyong City, Philippines
³⁰ Department of Ophthalmology, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
³¹ Ufa Eye Research Institute, Ufa, Russia
³² Singapore National Eye Centre
³³ Singapore Eye Research Institute, Singapore
³⁴ National University of Singapore
³⁵ The Department of Clinical Neuroscience, Geneva University Hospital, Geneva, Switzerland
³⁶ Department of Ophthalmology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seoul, South Korea
³⁷ Department of Ophthalmology, Faculty of Medicine Ramathibodi Hospital, Mahidol University. Bangkok, Thailand
³⁸ Moorfields Eye Hospital NHS Foundation Trust, United Kingdom
³⁹ Department of Ophthalmology, Emory University School of Medicine, Atlanta GA, USA
⁴⁰ Departments of Ophthalmology, Neurology and Neurosurgery. Johns Hopkins University School of Medicine, Baltimore, MD, USA
⁴¹ Department of Ophthalmology and Neurology, Mayo Clinic, Rochester, MI, USA
⁴² Department of Neurology, SUNY Upstate Medical University, Syracuse, NY, USA
⁴³ Democratic Republic of Congo
⁴⁴ Dept of Ophthalmology, Saint Joseph Hospital, Boulevard Lumumba, 15eme rue, Kinshasa, Limete

¹ From the Department of Ophthalmology (V.B., M.Y.L., B.B.B., N.J.N.), Emory University School of Medicine, Atlanta, Georgia, USA
² Department of Neurology (V.B., B.B.B., N.J.N.), Emory University School of Medicine, Atlanta, Georgia, USA
³ Department of Emergency Medicine (D.W.W., M.T.K.), Emory University School of Medicine, Atlanta, Georgia, USA
⁴ Department of Neurological Surgery (N.J.N.), Emory University School of Medicine, Atlanta, Georgia, USA
⁵ Rollins School of Public Health (B.B.B.), Emory University School of Medicine, Atlanta, Georgia, USA
⁶ Singapore Eye Research Institute and Singapore National Eye Centre (R.P.N., Z.T., T.Y.W., D.M.), Singapore
⁷ Duke-NUS Medical School (R.P.N., T.Y.W., D.M.), National University of Singapore, Singapore
⁸ Eye N’ Brain Research Group (R.P.N.), Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
⁹ Center for Innovation and Precision Eye Health (R.P.N.), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
¹⁰ Tsinghua Medicine (T.Y.W.), Tsinghua University, China

^⁎Inquiries to Valérie Biousse, Department of Ophthalmology, Neuro-Ophthalmology Service, 1365B Clifton Rd NE, Atlanta, GA 30322, USADepartment of OphthalmologyNeuro-Ophthalmology Service1365B Clifton Rd NEAtlantaGA30322USA

Highlights

•	The Brain and Optic Nerve Study Artificial Intelligence (BONSAI) deep learning system was able to reliably identify papilledema and normal optic discs on photographs obtained in the Fundus photography vs Ophthalmoscopy Trial Outcomes in the Emergency Department (FOTO-ED) studies.
•	This deep learning system previously trained on high-quality mydriatic fundus photographs performed very well on nonmydriatic ocular fundus photographs.
•	Our deep learning system has excellent potential as a diagnostic aid in emergency departments and nonophthalmology clinics equipped with nonmydriatic fundus cameras.

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Résumé

Purpose

The Fundus photography vs Ophthalmoscopy Trial Outcomes in the Emergency Department (FOTO-ED) studies showed that ED providers poorly recognized funduscopic findings in patients in the ED. We tested a modified version of the Brain and Optic Nerve Study Artificial Intelligence (BONSAI) deep learning system on nonmydriatic fundus photographs from the FOTO-ED studies to determine if the deep learning system could have improved the detection of papilledema had it been available to ED providers as a real-time diagnostic aid.

Design

Retrospective secondary analysis of a cohort of patients included in the FOTO-ED studies.

Methods

The testing data set included 1608 photographs obtained from 828 patients in the FOTO-ED studies. Photographs were reclassified according to the optic disc classification system used by the deep learning system (“normal optic discs,” “papilledema,” and “other optic disc abnormalities”). The system's performance was evaluated by calculating the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 1-vs-rest strategy, with reference to expert neuro-ophthalmologists.

Results

The BONSAI deep learning system successfully distinguished normal from abnormal optic discs (AUC 0.92 [95% confidence interval {CI} 0.90-0.93]; sensitivity 75.6% [73.7%-77.5%] and specificity 89.6% [86.3%-92.8%]), and papilledema from normal and others (AUC 0.97 [0.95-0.99]; sensitivity 84.0% [75.0%-92.6%] and specificity 98.9% [98.5%-99.4%]). Six patients with missed papilledema in 1 eye were correctly identified by the deep learning system as having papilledema in the other eye.

Conclusions

The BONSAI deep learning system was able to reliably identify papilledema and normal optic discs on nonmydriatic photographs obtained in the FOTO-ED studies. Our deep learning system has excellent potential as a diagnostic aid in EDs and non-ophthalmology clinics equipped with nonmydriatic fundus cameras. NOTE: Publication of this article is sponsored by the American Ophthalmological Society.

Le texte complet de cet article est disponible en PDF.

Plan

Methods

Study Design

Overview of the FOTO-ED Study

Overview of the BONSAI-DLS

FOTO-ED Testing Data Set

Statistical Analysis

Results

Testing Data Set

Classification Performance in the FOTO-ED Data Set

Review of Classification Errors

Discussion

	Supplemental Material available at AJO.com.
	Presented at the North American Neuro-Ophthalmology Society Annual Meeting (Orlando, Florida, USA, March 2023).

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Application of a Deep Learning System to Detect Papilledema on Nonmydriatic Ocular Fundus Photographs in an Emergency Department - 16/04/24

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Purpose

Design

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Results

Conclusions

Plan

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