TY - JOUR
T1 - Diabetic retinopathy screening using digital non-mydriatic fundus photography and automated image analysis
AU - Hansen, Anja Bech
AU - Hartvig, Niels V.
AU - Jensen, Maja S.
AU - Borch-Johnsen, Knut
AU - Lund-Andersen, Henrik
AU - Larsen, Michael
PY - 2004/12/1
Y1 - 2004/12/1
N2 - Purpose: To investigate the use of automated image analysis for the detection of diabetic retinopathy (DR) in fundus photographs captured with and without pharmacological pupil dilation using a digital non-mydriatic camera. Methods: A total of 83 patients (165 eyes) with type 1 or type 2 diabetes, representing the full spectrum of DR, were photographed with and without pharmacological pupil dilation using a digital non-mydriatic camera. Two sets of five overlapping, non-stereoscopic, 45-degree field images of each eye were obtained. All images were graded in a masked fashion by two readers according to ETDRS standards and disagreements were settled by an independent adjudicator. Automated detection of red lesions as well as image quality control was made: detection of a single red lesion or insufficient image quality was categorized as possible DR. Results: At patient level, the automated red lesion detection and image quality control combined demonstrated a sensitivity of 89.9% and specificity of 85.7% in detecting DR when used on images captured without pupil dilation, and a sensitivity of 97.0% and specificity of 75.0% when used on images captured with pupil dilation. For moderate non-proliferative or more severe DR the sensitivity was 100% for images captured both with and without pupil dilation. Conclusion: Our results demonstrate that the described automated image analysis system, which detects the presence or absence of DR, can be used as a first-step screening tool in DR screening with considerable effectiveness.
AB - Purpose: To investigate the use of automated image analysis for the detection of diabetic retinopathy (DR) in fundus photographs captured with and without pharmacological pupil dilation using a digital non-mydriatic camera. Methods: A total of 83 patients (165 eyes) with type 1 or type 2 diabetes, representing the full spectrum of DR, were photographed with and without pharmacological pupil dilation using a digital non-mydriatic camera. Two sets of five overlapping, non-stereoscopic, 45-degree field images of each eye were obtained. All images were graded in a masked fashion by two readers according to ETDRS standards and disagreements were settled by an independent adjudicator. Automated detection of red lesions as well as image quality control was made: detection of a single red lesion or insufficient image quality was categorized as possible DR. Results: At patient level, the automated red lesion detection and image quality control combined demonstrated a sensitivity of 89.9% and specificity of 85.7% in detecting DR when used on images captured without pupil dilation, and a sensitivity of 97.0% and specificity of 75.0% when used on images captured with pupil dilation. For moderate non-proliferative or more severe DR the sensitivity was 100% for images captured both with and without pupil dilation. Conclusion: Our results demonstrate that the described automated image analysis system, which detects the presence or absence of DR, can be used as a first-step screening tool in DR screening with considerable effectiveness.
KW - Automated image analysis
KW - Diabetic retinopathy
KW - Non-mydriatic
KW - Screening
UR - http://www.scopus.com/inward/record.url?scp=11244292362&partnerID=8YFLogxK
U2 - 10.1111/j.1600-0420.2004.00350.x
DO - 10.1111/j.1600-0420.2004.00350.x
M3 - Article
C2 - 15606461
AN - SCOPUS:11244292362
SN - 1395-3907
VL - 82
SP - 666
EP - 672
JO - Acta Ophthalmologica Scandinavica
JF - Acta Ophthalmologica Scandinavica
IS - 6
ER -