Application of a Deep Learning System to Detect Papilledema on Nonmydriatic Ocular Fundus Photographs in an Emergency Department

Application of a Deep Learning System to Detect Papilledema on Nonmydriatic Ocular Fundus Photographs in an Emergency Department Publisher Pubmed

Biousse V^{1, 2, 39} ; Najjar RP^{6, 7, 8, 9, 33} ; Tang Z^{6, 33, 34} ; Lin MY^{1, 39} ; Wright DW³ ; Keadey MT³ ; Wong TY^{6, 7, 10, 33} ; Bruce BB^{1, 2, 5} ; Milea D^{6, 7, 33} ; Newman NJ^{1, 2, 4, 39} ; Fraser CL¹¹ ; Micieli JA¹² ; Costello F¹³ ; Benardseguin E¹³ Show All Authors

Biousse V^{1, 2, 39}
Najjar RP^{6, 7, 8, 9, 33}
Tang Z^{6, 33, 34}
Lin MY^{1, 39}
Wright DW³
Keadey MT³
Wong TY^{6, 7, 10, 33}
Bruce BB^{1, 2, 5}
Milea D^{6, 7, 33}
Newman NJ^{1, 2, 4, 39}
Fraser CL¹¹
Micieli JA¹²
Costello F¹³
Benardseguin E¹³
Yang H¹⁴
Chan CKM¹⁵
Cheung CY¹⁵
Chan NC¹⁵
Hamann S¹⁶
Gohier P¹⁷
Vautier A¹⁷
Rougier MB¹⁸
Chiquet C¹⁹
Vignalclermont C²⁰
Hage R²⁰
Khanna RK²⁰
Tran THC²¹
Lagreze WA²²
Jonas JB²³
Ambika S²⁴
Fard MA²⁵
La Morgia C²⁶
Carbonelli M²⁶
Barboni P²⁶
Carelli V²⁶
Romagnoli M²⁶
Amore G²⁶
Nakamura M²⁷
Fumio T²⁷
Petzold A²⁸
Wenniger Lj MDB²⁸
Kho R²⁹
Fonseca PL³⁰
Bikbov MM³¹
Ting D^{32, 33, 34}
Loo JL^{32, 33, 34}
Tow S^{32, 33, 34}
Singhal S^{32, 33, 34}
Vasseneix C^{32, 33, 34}
Lamoureux E^{32, 33, 34}
Yu Chen C^{32, 33, 34}
Aung T^{32, 33, 34}
Schmetterer L^{32, 33, 34}
Sanda N³⁵
Thuman G³⁵
Hwang JM³⁶
Vanikieti K³⁷
Suwan Y³⁷
Padungkiatsagul T³⁷
Yuwaiman P³⁸
Jurkute N³⁸
Hong EH³⁸
Peragallo JH³⁹
Datillo M³⁹
Kedar S³⁹
Patil A³⁹
Aung A³⁹
Boyko M³⁹
Alsakran WA³⁹
Zayani A³⁹
Bouthour W³⁹
Banc A³⁹
Mosley R³⁹
Labella F³⁹
Miller NR⁴⁰
Chen JJ⁴¹
Mejico LJ^{42, 43}
Kilangalanga JN⁴⁴

Show Affiliations

1. From the Department of Ophthalmology (V.B., M.Y.L., B.B.B., N.J.N.), Emory University School of Medicine, Atlanta, GA, United States
2. Department of Neurology (V.B., B.B.B., N.J.N.), Emory University School of Medicine, Atlanta, GA, United States
3. Department of Emergency Medicine (D.W.W., M.T.K.), Emory University School of Medicine, Atlanta, GA, United States
4. Department of Neurological Surgery (N.J.N.), Emory University School of Medicine, Atlanta, GA, United States
5. Rollins School of Public Health (B.B.B.), Emory University School of Medicine, Atlanta, GA, United States
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 Nâ€™ 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
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, 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, Hopital 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 Universitario de Coimbra, Coimbra, Portugal
31. Ufa Eye Research Institute, Ufa, Russian Federation
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, United States
40. Departments of Ophthalmology, Neurology and Neurosurgery. Johns Hopkins University School of Medicine, Baltimore, MD, United States
41. Department of Ophthalmology and Neurology, Mayo Clinic, Rochester, MI, United States
42. Department of Neurology, SUNY Upstate Medical University, Syracuse, NY, United States
43. Democratic Republic of Congo
44. Dept of Ophthalmology, Saint Joseph Hospital, Boulevard Lumumba, 15eme rue, Kinshasa, Limete

Source: American Journal of Ophthalmology Published:2024

Abstract

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. © 2023 Elsevier Inc.

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Style	Citing Format
MLA	Biousse V, et al.. "Application of a Deep Learning System to Detect Papilledema on Nonmydriatic Ocular Fundus Photographs in an Emergency Department." American Journal of Ophthalmology, vol. 261, no. , 2024, pp. 199-207.
APA	Biousse V, Najjar RP, Tang Z, Lin MY, Wright DW, Keadey MT, Wong TY, Bruce BB, Milea D, Newman NJ, Fraser CL, Micieli JA, Costello F, Benardseguin E, Yang H, Chan CKM, Cheung CY, Chan NC, Hamann S, ... Kilangalanga JN (2024). Application of a Deep Learning System to Detect Papilledema on Nonmydriatic Ocular Fundus Photographs in an Emergency Department. American Journal of Ophthalmology, 261(), 199-207.
Chicago	Biousse V, Najjar RP, Tang Z, Lin MY, Wright DW, Keadey MT, Wong TY, et al.. "Application of a Deep Learning System to Detect Papilledema on Nonmydriatic Ocular Fundus Photographs in an Emergency Department." American Journal of Ophthalmology 261, no. (2024): 199-207.
Harvard	Biousse V et al. (2024) 'Application of a Deep Learning System to Detect Papilledema on Nonmydriatic Ocular Fundus Photographs in an Emergency Department', American Journal of Ophthalmology, 261(), pp. 199-207.
Vancouver	Biousse V, Najjar RP, Tang Z, Lin MY, Wright DW, Keadey MT, et al.. Application of a Deep Learning System to Detect Papilledema on Nonmydriatic Ocular Fundus Photographs in an Emergency Department. American Journal of Ophthalmology. 2024;261():199-207.
BibTex	@article{ author = {Biousse V and Najjar RP and Tang Z and Lin MY and Wright DW and Keadey MT and Wong TY and Bruce BB and Milea D and Newman NJ and Fraser CL and Micieli JA and Costello F and Benardseguin E and Yang H and Chan CKM and Cheung CY and Chan NC and Hamann S and Gohier P and Vautier A and Rougier MB and Chiquet C and Vignalclermont C and Hage R and Khanna RK and Tran THC and Lagreze WA and Jonas JB and Ambika S and Fard MA and La Morgia C and Carbonelli M and Barboni P and Carelli V and Romagnoli M and Amore G and Nakamura M and Fumio T and Petzold A and Wenniger Lj MDB and Kho R and Fonseca PL and Bikbov MM and Ting D and Loo JL and Tow S and Singhal S and Vasseneix C and Lamoureux E and Yu Chen C and Aung T and Schmetterer L and Sanda N and Thuman G and Hwang JM and Vanikieti K and Suwan Y and Padungkiatsagul T and Yuwaiman P and Jurkute N and Hong EH and Peragallo JH and Datillo M and Kedar S and Patil A and Aung A and Boyko M and Alsakran WA and Zayani A and Bouthour W and Banc A and Mosley R and Labella F and Miller NR and Chen JJ and Mejico LJ and Kilangalanga JN}, title = {Application of a Deep Learning System to Detect Papilledema on Nonmydriatic Ocular Fundus Photographs in an Emergency Department}, journal = {American Journal of Ophthalmology}, volume = {261}, number = {}, pages = {199-207}, year = {2024} }
RIS	TY - JOUR AU - Biousse V AU - Najjar RP AU - Tang Z AU - Lin MY AU - Wright DW AU - Keadey MT AU - Wong TY AU - Bruce BB AU - Milea D AU - Newman NJ AU - Fraser CL AU - Micieli JA AU - Costello F AU - Benardseguin E AU - Yang H AU - Chan CKM AU - Cheung CY AU - Chan NC AU - Hamann S AU - Gohier P AU - Vautier A AU - Rougier MB AU - Chiquet C AU - Vignalclermont C AU - Hage R AU - Khanna RK AU - Tran THC AU - Lagreze WA AU - Jonas JB AU - Ambika S AU - Fard MA AU - La Morgia C AU - Carbonelli M AU - Barboni P AU - Carelli V AU - Romagnoli M AU - Amore G AU - Nakamura M AU - Fumio T AU - Petzold A AU - Wenniger Lj MDB AU - Kho R AU - Fonseca PL AU - Bikbov MM AU - Ting D AU - Loo JL AU - Tow S AU - Singhal S AU - Vasseneix C AU - Lamoureux E AU - Yu Chen C AU - Aung T AU - Schmetterer L AU - Sanda N AU - Thuman G AU - Hwang JM AU - Vanikieti K AU - Suwan Y AU - Padungkiatsagul T AU - Yuwaiman P AU - Jurkute N AU - Hong EH AU - Peragallo JH AU - Datillo M AU - Kedar S AU - Patil A AU - Aung A AU - Boyko M AU - Alsakran WA AU - Zayani A AU - Bouthour W AU - Banc A AU - Mosley R AU - Labella F AU - Miller NR AU - Chen JJ AU - Mejico LJ AU - Kilangalanga JN TI - Application of a Deep Learning System to Detect Papilledema on Nonmydriatic Ocular Fundus Photographs in an Emergency Department JO - American Journal of Ophthalmology VL - 261 IS - SP - 199 EP - 207 PY - 2024 ER -

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