Assessment of automated disease detection in diabetic retinopathy screening using two-field photography.

<h4>Aim</h4>To assess the performance of automated disease detection in diabetic retinopathy screening using two field mydriatic photography.<h4>Methods</h4>Images from 8,271 sequential patient screening episodes from a South London diabetic retinopathy screening service were...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Keith Goatman, Amanda Charnley, Laura Webster, Stephen Nussey
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a249b448014a432a8b9449a60a98591a
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:a249b448014a432a8b9449a60a98591a
record_format	dspace
spelling	oai:doaj.org-article:a249b448014a432a8b9449a60a98591a2021-11-18T07:32:47ZAssessment of automated disease detection in diabetic retinopathy screening using two-field photography.1932-620310.1371/journal.pone.0027524https://doaj.org/article/a249b448014a432a8b9449a60a98591a2011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22174741/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Aim</h4>To assess the performance of automated disease detection in diabetic retinopathy screening using two field mydriatic photography.<h4>Methods</h4>Images from 8,271 sequential patient screening episodes from a South London diabetic retinopathy screening service were processed by the Medalytix iGrading™ automated grading system. For each screening episode macular-centred and disc-centred images of both eyes were acquired and independently graded according to the English national grading scheme. Where discrepancies were found between the automated result and original manual grade, internal and external arbitration was used to determine the final study grades. Two versions of the software were used: one that detected microaneurysms alone, and one that detected blot haemorrhages and exudates in addition to microaneurysms. Results for each version were calculated once using both fields and once using the macula-centred field alone.<h4>Results</h4>Of the 8,271 episodes, 346 (4.2%) were considered unassessable. Referable disease was detected in 587 episodes (7.1%). The sensitivity of the automated system for detecting unassessable images ranged from 97.4% to 99.1% depending on configuration. The sensitivity of the automated system for referable episodes ranged from 98.3% to 99.3%. All the episodes that included proliferative or pre-proliferative retinopathy were detected by the automated system regardless of configuration (192/192, 95% confidence interval 98.0% to 100%). If implemented as the first step in grading, the automated system would have reduced the manual grading effort by between 2,183 and 3,147 patient episodes (26.4% to 38.1%).<h4>Conclusion</h4>Automated grading can safely reduce the workload of manual grading using two field, mydriatic photography in a routine screening service.Keith GoatmanAmanda CharnleyLaura WebsterStephen NusseyPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 12, p e27524 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Keith Goatman Amanda Charnley Laura Webster Stephen Nussey Assessment of automated disease detection in diabetic retinopathy screening using two-field photography.
description	<h4>Aim</h4>To assess the performance of automated disease detection in diabetic retinopathy screening using two field mydriatic photography.<h4>Methods</h4>Images from 8,271 sequential patient screening episodes from a South London diabetic retinopathy screening service were processed by the Medalytix iGrading™ automated grading system. For each screening episode macular-centred and disc-centred images of both eyes were acquired and independently graded according to the English national grading scheme. Where discrepancies were found between the automated result and original manual grade, internal and external arbitration was used to determine the final study grades. Two versions of the software were used: one that detected microaneurysms alone, and one that detected blot haemorrhages and exudates in addition to microaneurysms. Results for each version were calculated once using both fields and once using the macula-centred field alone.<h4>Results</h4>Of the 8,271 episodes, 346 (4.2%) were considered unassessable. Referable disease was detected in 587 episodes (7.1%). The sensitivity of the automated system for detecting unassessable images ranged from 97.4% to 99.1% depending on configuration. The sensitivity of the automated system for referable episodes ranged from 98.3% to 99.3%. All the episodes that included proliferative or pre-proliferative retinopathy were detected by the automated system regardless of configuration (192/192, 95% confidence interval 98.0% to 100%). If implemented as the first step in grading, the automated system would have reduced the manual grading effort by between 2,183 and 3,147 patient episodes (26.4% to 38.1%).<h4>Conclusion</h4>Automated grading can safely reduce the workload of manual grading using two field, mydriatic photography in a routine screening service.
format	article
author	Keith Goatman Amanda Charnley Laura Webster Stephen Nussey
author_facet	Keith Goatman Amanda Charnley Laura Webster Stephen Nussey
author_sort	Keith Goatman
title	Assessment of automated disease detection in diabetic retinopathy screening using two-field photography.
title_short	Assessment of automated disease detection in diabetic retinopathy screening using two-field photography.
title_full	Assessment of automated disease detection in diabetic retinopathy screening using two-field photography.
title_fullStr	Assessment of automated disease detection in diabetic retinopathy screening using two-field photography.
title_full_unstemmed	Assessment of automated disease detection in diabetic retinopathy screening using two-field photography.
title_sort	assessment of automated disease detection in diabetic retinopathy screening using two-field photography.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/a249b448014a432a8b9449a60a98591a
work_keys_str_mv	AT keithgoatman assessmentofautomateddiseasedetectionindiabeticretinopathyscreeningusingtwofieldphotography AT amandacharnley assessmentofautomateddiseasedetectionindiabeticretinopathyscreeningusingtwofieldphotography AT laurawebster assessmentofautomateddiseasedetectionindiabeticretinopathyscreeningusingtwofieldphotography AT stephennussey assessmentofautomateddiseasedetectionindiabeticretinopathyscreeningusingtwofieldphotography
_version_	1718423304394506240

Assessment of automated disease detection in diabetic retinopathy screening using two-field photography.

Ejemplares similares