| Full text | |
| Author(s): |
Pires, Ramon
[1]
;
Carvalho, Tiago
[1]
;
Spurling, Geoffrey
[2, 3]
;
Goldenstein, Siome
[1]
;
Wainer, Jacques
[1]
;
Luckie, Alan
[4]
;
Jelinek, Herbert F.
[5, 6, 7]
;
Rocha, Anderson
[1]
Total Authors: 8
|
| Affiliation: | [1] Univ Estadual Campinas, Inst Comp, Campinas, SP - Brazil
[2] Queensland Hlth, Southern Queensland Ctr Excellence Aboriginal & T, Brisbane, Qld - Australia
[3] Sch Med, Discipline Gen Practice, Brisbane, Qld - Australia
[4] Albury Eye Clin, Retinal Div, Albury, NSW - Australia
[5] Macquarie Univ, Australian Sch Adv Med, Sydney, NSW 2109 - Australia
[6] Charles Sturt Univ, Ctr Res Complex Syst, Albury, NSW - Australia
[7] Charles Sturt Univ, Sch Community Hlth, Albury, NSW - Australia
Total Affiliations: 7
|
| Document type: | Journal article |
| Source: | PLoS One; v. 10, n. 6 JUN 2 2015. |
| Web of Science Citations: | 3 |
| Abstract | |
Diabetic Retinopathy (DR) is a complication of diabetes mellitus that affects more than one-quarter of the population with diabetes, and can lead to blindness if not discovered in time. An automated screening enables the identification of patients who need further medical attention. This study aimed to classify retinal images of Aboriginal and Torres Strait Islander peoples utilizing an automated computer-based multi-lesion eye screening program for diabetic retinopathy. The multi-lesion classifier was trained on 1,014 images from the Sao Paulo Eye Hospital and tested on retinal images containing no DR-related lesion, single lesions, or multiple types of lesions from the Inala Aboriginal and Torres Strait Islander health care centre. The automated multi-lesion classifier has the potential to enhance the efficiency of clinical practice delivering diabetic retinopathy screening. Our program does not necessitate image samples for training from any specific ethnic group or population being assessed and is independent of image pre- or post-processing to identify retinal lesions. In this Aboriginal and Torres Strait Islander population, the program achieved 100% sensitivity and 88.9% specificity in identifying bright lesions, while detection of red lesions achieved a sensitivity of 67% and specificity of 95%. When both bright and red lesions were present, 100% sensitivity with 88.9% specificity was obtained. All results obtained with this automated screening program meet WHO standards for diabetic retinopathy screening. (AU) | |
| FAPESP's process: | 08/54443-2 - Automated screening for diabetic retinopathies: IT in the fight against preventable blindness |
| Grantee: | Jacques Wainer |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 10/05647-4 - Digital forensics: collection, organization, classification and analysis of digital evidences |
| Grantee: | Anderson de Rezende Rocha |
| Support Opportunities: | Research Grants - Young Investigators Grants |