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

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

Source: Am J Ophthalmol

Publié le

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.†.