Exactitud de tamizaje de retinopatía diabética: inteligencia artificial versus tecnólogos médicos entrenados

Background: The early detection of retinopathy among diabetics is of utmost importance. Aim: To estimate the diagnostic accuracy of two diabetic retinopathy (DR) screening strategies currently used in the Chilean public health system. Material and Methods: Cross-sectional observational study of 3...

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Autores principales: Ibáñez-bruron,María c., Cruzat,Andrea, Órdenes-Cavieres,Gonzalo, Coria,Marcelo
Lenguaje:Spanish / Castilian
Publicado: Sociedad Médica de Santiago 2021
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Acceso en línea:http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0034-98872021000400493
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spelling oai:scielo:S0034-988720210004004932021-08-26Exactitud de tamizaje de retinopatía diabética: inteligencia artificial versus tecnólogos médicos entrenadosIbáñez-bruron,María c.Cruzat,AndreaÓrdenes-Cavieres,GonzaloCoria,Marcelo Artificial Intelligence Diabetic Retinopathy Mass Screening Public Health Background: The early detection of retinopathy among diabetics is of utmost importance. Aim: To estimate the diagnostic accuracy of two diabetic retinopathy (DR) screening strategies currently used in the Chilean public health system. Material and Methods: Cross-sectional observational study of 371 diabetic patients aged 61 ± 14 years (61% women) who underwent DR screening at a public Hospital between July 1 and August 31, 2019. The mydriatic retinal photographs of all participants were classified using artificial intelligence software (DART) and trained medical technologists, independently. The precision of both strategies was compared with the reference standard, namely the evaluation of the fundus by an ophthalmologist with a slit lamp. Participants with severe non-proliferative DR or worse were considered as positive cases. The ophthalmologist was blind to the results of the screening tests. Results: Twenty four percent of participants had DR, including 34 (9.2%) who had sight threatening DR in at least one eye. The sensitivity and specificity of DART were 100% (95% confidence intervals (CI): 90-100%) and 55,4% (95% CI: 50-61%), respectively. Medical technologists had a sensitivity of 97,1% (95% CI: 85-100%) and a specificity of 91,7% (95% CI: 88-94%). The only case missed by medical technologists was a patient with unilateral panphotocoagulated DR. Conclusions: Both strategies had a similar sensitivity to detect cases of sight-threatening DR. However, the specificity of DART was significantly lower compared to medical technologists, which would greatly increase the burden on the health system, a very important aspect to consider in a screening strategy.info:eu-repo/semantics/openAccessSociedad Médica de SantiagoRevista médica de Chile v.149 n.4 20212021-04-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0034-98872021000400493es10.4067/s0034-98872021000400493
institution Scielo Chile
collection Scielo Chile
language Spanish / Castilian
topic Artificial Intelligence
Diabetic Retinopathy
Mass Screening
Public Health
spellingShingle Artificial Intelligence
Diabetic Retinopathy
Mass Screening
Public Health
Ibáñez-bruron,María c.
Cruzat,Andrea
Órdenes-Cavieres,Gonzalo
Coria,Marcelo
Exactitud de tamizaje de retinopatía diabética: inteligencia artificial versus tecnólogos médicos entrenados
description Background: The early detection of retinopathy among diabetics is of utmost importance. Aim: To estimate the diagnostic accuracy of two diabetic retinopathy (DR) screening strategies currently used in the Chilean public health system. Material and Methods: Cross-sectional observational study of 371 diabetic patients aged 61 ± 14 years (61% women) who underwent DR screening at a public Hospital between July 1 and August 31, 2019. The mydriatic retinal photographs of all participants were classified using artificial intelligence software (DART) and trained medical technologists, independently. The precision of both strategies was compared with the reference standard, namely the evaluation of the fundus by an ophthalmologist with a slit lamp. Participants with severe non-proliferative DR or worse were considered as positive cases. The ophthalmologist was blind to the results of the screening tests. Results: Twenty four percent of participants had DR, including 34 (9.2%) who had sight threatening DR in at least one eye. The sensitivity and specificity of DART were 100% (95% confidence intervals (CI): 90-100%) and 55,4% (95% CI: 50-61%), respectively. Medical technologists had a sensitivity of 97,1% (95% CI: 85-100%) and a specificity of 91,7% (95% CI: 88-94%). The only case missed by medical technologists was a patient with unilateral panphotocoagulated DR. Conclusions: Both strategies had a similar sensitivity to detect cases of sight-threatening DR. However, the specificity of DART was significantly lower compared to medical technologists, which would greatly increase the burden on the health system, a very important aspect to consider in a screening strategy.
author Ibáñez-bruron,María c.
Cruzat,Andrea
Órdenes-Cavieres,Gonzalo
Coria,Marcelo
author_facet Ibáñez-bruron,María c.
Cruzat,Andrea
Órdenes-Cavieres,Gonzalo
Coria,Marcelo
author_sort Ibáñez-bruron,María c.
title Exactitud de tamizaje de retinopatía diabética: inteligencia artificial versus tecnólogos médicos entrenados
title_short Exactitud de tamizaje de retinopatía diabética: inteligencia artificial versus tecnólogos médicos entrenados
title_full Exactitud de tamizaje de retinopatía diabética: inteligencia artificial versus tecnólogos médicos entrenados
title_fullStr Exactitud de tamizaje de retinopatía diabética: inteligencia artificial versus tecnólogos médicos entrenados
title_full_unstemmed Exactitud de tamizaje de retinopatía diabética: inteligencia artificial versus tecnólogos médicos entrenados
title_sort exactitud de tamizaje de retinopatía diabética: inteligencia artificial versus tecnólogos médicos entrenados
publisher Sociedad Médica de Santiago
publishDate 2021
url http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0034-98872021000400493
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