Accuracy of a Deep Learning System for Classification of Papilledema Severity on Ocular Fundus Photographs.
Journal
Neurology
ISSN: 1526-632X
Titre abrégé: Neurology
Pays: United States
ID NLM: 0401060
Informations de publication
Date de publication:
27 07 2021
27 07 2021
Historique:
received:
09
11
2020
accepted:
19
04
2021
pubmed:
21
5
2021
medline:
10
8
2021
entrez:
20
5
2021
Statut:
ppublish
Résumé
To evaluate the performance of a deep learning system (DLS) in classifying the severity of papilledema associated with increased intracranial pressure on standard retinal fundus photographs. A DLS was trained to automatically classify papilledema severity in 965 patients (2,103 mydriatic fundus photographs), representing a multiethnic cohort of patients with confirmed elevated intracranial pressure. Training was performed on 1,052 photographs with mild/moderate papilledema (MP) and 1,051 photographs with severe papilledema (SP) classified by a panel of experts. The performance of the DLS and that of 3 independent neuro-ophthalmologists were tested in 111 patients (214 photographs, 92 with MP and 122 with SP) by calculating the area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, and specificity. Kappa agreement scores between the DLS and each of the 3 graders and among the 3 graders were calculated. The DLS successfully discriminated between photographs of MP and SP, with an AUC of 0.93 (95% confidence interval [CI] 0.89-0.96) and an accuracy, sensitivity, and specificity of 87.9%, 91.8%, and 86.2%, respectively. This performance was comparable with that of the 3 neuro-ophthalmologists (84.1%, 91.8%, and 73.9%, Our DLS accurately classified the severity of papilledema on an independent set of mydriatic fundus photographs, achieving a comparable performance with that of independent neuro-ophthalmologists. This study provides Class II evidence that a DLS using mydriatic retinal fundus photographs accurately classified the severity of papilledema associated in patients with a diagnosis of increased intracranial pressure.
Identifiants
pubmed: 34011570
pii: WNL.0000000000012226
doi: 10.1212/WNL.0000000000012226
pmc: PMC8362357
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e369-e377Investigateurs
Philippe Gohier
(P)
Neil Miller
(N)
Tanyatuth Padungkiatsagul
(T)
Anuchit Poonyathalang
(A)
Yanin Suwan
(Y)
Kavin Vanikieti
(K)
Giulia Amore
(G)
Piero Barboni
(P)
Michele Carbonelli
(M)
Valerio Carelli
(V)
Chiara La Morgia
(C)
Martina Romagnoli
(M)
Marie-Bénédicte Rougier
(MB)
Selvakumar Ambika
(S)
Komma Swetha
(K)
Pedro Fonseca
(P)
Miguel Raimundo
(M)
Steffen Hamann
(S)
Isabelle Karlesand
(I)
Lars Fuhrmann
(L)
Sebastian Küchlin
(S)
Wolf Alexander Lagrèze
(WA)
Nicolae Sanda
(N)
Gabriele Thumann
(G)
Florent Aptel
(F)
Christophe Chiquet
(C)
Kaiqun Liu
(K)
Hui Yang
(H)
Carmen Km Chan
(CK)
Noel Cy Chan
(NC)
Carol Y Cheung
(CY)
Tran Thi Ha Chau
(TT)
James Acheson
(J)
Maged S Habib
(MS)
Neringa Jurkute
(N)
Patrick Yu-Wai-Man
(P)
Richard Kho
(R)
Jost B Jonas
(JB)
John J Chen
(JJ)
Nouran Sabbagh
(N)
Catherine VignalClermont
(C)
Rabih Hage
(R)
Raoul Kanav Khanna
(RK)
Jeong-Min Hwang
(JM)
Dong Hyun Kim
(DH)
Hee Kyung Yang
(HK)
Tin Aung
(T)
Ching-Yu Cheng
(CY)
Ecosse Lamoureux
(E)
Leopold Schmetterer
(L)
Zhubo Jiang
(Z)
Clare L Fraser
(CL)
Luis J Mejico
(LJ)
Masoud Aghsaei Fard
(MA)
Informations de copyright
© 2021 American Academy of Neurology.
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