Screening Diabetic Retinopathy Using an Automated Retinal Image Analysis System in Independent and Assistive Use Cases in Mexico: Randomized Controlled Trial
Por:
Noriega, Alejandro, Meizner, Daniela, Camacho, Dalia, Enciso, Jennifer, Quiroz-Mercado, Hugo, Morales-Canton, Virgilio, Almaatouq, Abdullah, Pentland, Alex
Publicada:
1 ago 2021
Resumen:
Background: The automated screening of patients at risk of developing
diabetic retinopathy represents an opportunity to improve their midterm
outcome and lower the public expenditure associated with direct and
indirect costs of common sight-threatening complications of diabetes.
Objective: This study aimed to develop and evaluate the performance of
an automated deep learning-based system to classify retinal fundus
images as referable and nonreferable diabetic retinopathy cases, from
international and Mexican patients. In particular, we aimed to evaluate
the performance of the automated retina image analysis (ARIA) system
under an independent scheme (ie, only ARIA screening) and 2 assistive
schemes (ie, hybrid ARIA plus ophthalmologist screening), using a
web-based platform for remote image analysis to determine and compare
the sensibility and specificity of the 3 schemes.
Methods: A randomized controlled experiment was performed where 17
ophthalmologists were asked to classify a series of retinal fundus
images under 3 different conditions. The conditions were to (1) screen
the fundus image by themselves (solo); (2) screen the fundus image after
exposure to the retina image classification of the ARIA system (ARIA
answer); and (3) screen the fundus image after exposure to the
classification of the ARIA system, as well as its level of confidence
and an attention map highlighting the most important areas of interest
in the image according to the ARIA system (ARIA explanation). The
ophthalmologists' classification in each condition and the result from
the ARIA system were compared against a gold standard generated by
consulting and aggregating the opinion of 3 retina specialists for each
fundus image.
Results: The ARIA system was able to classify referable vs nonreferable
cases with an area under the receiver operating characteristic curve of
98%, a sensitivity of 95.1%, and a specificity of 91.5% for
international patient cases. There was an area under the receiver
operating characteristic curve of 98.3%, a sensitivity of 95.2%, and a
specificity of 90% for Mexican patient cases. The ARIA system
performance was more successful than the average performance of the 17
ophthalmologists enrolled in the study. Additionally, the results
suggest that the ARIA system can be useful as an assistive tool, as
sensitivity was significantly higher in the experimental condition where
ophthalmologists were exposed to the ARIA system's answer prior to their
own classification (93.3%), compared with the sensitivity of the
condition where participants assessed the images independently (87.3%;
P=.05).
Conclusions: These results demonstrate that both independent and
assistive use cases of the ARIA system present, for Latin American
countries such as Mexico, a substantial opportunity toward expanding the
monitoring capacity for the early detection of diabetes-related
blindness.
Filiaciones:
Noriega, Alejandro:
MIT Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
Prosperia Salud, Mexico City, Mexico
Meizner, Daniela:
Retina Department, Asociación Para Evitar La Ceguera En México, Mexico City, Mexico
Camacho, Dalia:
Prosperia Salud, Mexico City, Mexico
Engineering Academic Division, Instituto Tecnológico Autónomo de México, Mexico City, Mexico
Enciso, Jennifer:
Prosperia Salud, Mexico City, Mexico
Posgrado de Ciencias Bioquímicas, Universidad Nacional Autónoma de México, Mexico City, Mexico
Quiroz-Mercado, Hugo:
Retina Department, Asociación Para Evitar La Ceguera En México, Mexico City, Mexico
Morales-Canton, Virgilio:
Retina Department, Asociación Para Evitar La Ceguera En México, Mexico City, Mexico
Almaatouq, Abdullah:
Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, United States
Pentland, Alex:
MIT Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
Green Published, gold, Green Submitted, Gold, Green
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