Details

Autor(en) / Beteiligte

• Biousse, Valérie
• Najjar, Raymond P.
• Tang, Zhiqun
• Lin, Mung Yan
• Wright, David W.
• Keadey, Matthew T.
• Wong, Tien Y.
• Bruce, Beau B.
• Milea, Dan
• Newman, Nancy J.
• Fraser, Clare L.
• Micieli, Jonathan A.
• Costello, Fiona
• Bénard-Séguin, Étienne
• Yang, Hui
• Chan, Carmen Kar Mun
• Cheung, Carol Y
• Chan, Noel CY
• Hamann, Steffen
• Gohier, Philippe
• Vautier, Anaïs
• Rougier, Marie-Bénédicte
• Chiquet, Christophe
• Vignal-Clermont, Catherine
• Hage, Rabih
• Khanna, Raoul Kanav
• Tran, Thi Ha Chau
• Lagrèze, Wolf Alexander
• Jonas, Jost B
• Ambika, Selvakumar
• Fard, Masoud Aghsaei
• La Morgia, Chiara
• Carbonelli, Michele
• Barboni, Piero
• Carelli, Valerio
• Romagnoli, Martina
• Amore, Giulia
• Nakamura, Makoto
• Fumio, Takano
• Petzold, Axel
• Wenniger lj, Maillette de Buy
• Kho, Richard
• Fonseca, Pedro L.
• Bikbov, Mukharram M.
• Najjar, Raymond P
• Ting, Daniel
• Loo, Jing Liang
• Tow, Sharon
• Singhal, Shweta
• Vasseneix, Caroline
• Wong, Tien Yin
• Lamoureux, Ecosse
• Yu Chen, Ching
• Aung, Tin
• Schmetterer, Leopold
• Sanda, Nicolae
• Thuman, Gabriele
• Hwang, Jeong-Min
• Vanikieti, Kavin
• Suwan, Yanin
• Padungkiatsagul, Tanyatuth
• Yu-Wai-Man, Patrick
• Jurkute, Neringa
• Hong, Eun Hee
• Biousse, Valerie
• Peragallo, Jason H.
• Datillo, Michael
• Kedar, Sachin
• Patil, Ajay
• Aung, Andre
• Boyko, Matthew
• Alsakran, Wael Abdulraman
• Zayani, Amani
• Bouthour, Walid
• Banc, Ana
• Mosley, Rasha
• Labella, Fernando
• Miller, Neil R.
• Chen, John J.
• Mejico, Luis J.
• Kilangalanga, Janvier Ngoy

Titel

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

Ist Teil von

• American journal of ophthalmology, 2024-05, Vol.261, p.199-207

Ort / Verlag

United States: Elsevier Inc

Erscheinungsjahr

2024

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

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