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Combined Methods for Diabetic Retinopathy Screening, Using Retina Photographs and Tear Fluid Proteomics Biomarkers.

Torok Z, Peto T, Csosz E, Tukacs E, Molnar AM, Berta A, Tozser J, Hajdu A, Nagy V, Domokos B, Csutak A - J Diabetes Res (2015)

Bottom Line: MA detection method alone resulted in 0.84 sensitivity and 0.81 specificity.Conclusions.As the two different types of data represent independent and complementary information on the outcome, the combined model resulted in a reliable screening method that is comparable to the requirements of DR screening programs applied in clinical routine.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Graphics and Image Processing, Bioinformatics Research Group, Faculty of Informatics, University of Debrecen, Egyetem tér 1, Debrecen 4032, Hungary ; Department of Ophthalmology, Faculty of Medicine, University of Debrecen, Egyetem tér 1, Debrecen 4032, Hungary ; Astridbio Technologies Inc., 439 University Avenue, Toronto, ON, Canada M5G 1Y8.

ABSTRACT
Background. It is estimated that 347 million people suffer from diabetes mellitus (DM), and almost 5 million are blind due to diabetic retinopathy (DR). The progression of DR can be slowed down with early diagnosis and treatment. Therefore our aim was to develop a novel automated method for DR screening. Methods. 52 patients with diabetes mellitus were enrolled into the project. Of all patients, 39 had signs of DR. Digital retina images and tear fluid samples were taken from each eye. The results from the tear fluid proteomics analysis and from digital microaneurysm (MA) detection on fundus images were used as the input of a machine learning system. Results. MA detection method alone resulted in 0.84 sensitivity and 0.81 specificity. Using the proteomics data for analysis 0.87 sensitivity and 0.68 specificity values were achieved. The combined data analysis integrated the features of the proteomics data along with the number of detected MAs in the associated image and achieved sensitivity/specificity values of 0.93/0.78. Conclusions. As the two different types of data represent independent and complementary information on the outcome, the combined model resulted in a reliable screening method that is comparable to the requirements of DR screening programs applied in clinical routine.

No MeSH data available.


Related in: MedlinePlus

Microaneurysm detection. (a) Original retina image; (b) CLAHE contrast enhancement; (c) median filtering; (d) top-hat transform; (e) raw MA candidates.
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fig1: Microaneurysm detection. (a) Original retina image; (b) CLAHE contrast enhancement; (c) median filtering; (d) top-hat transform; (e) raw MA candidates.

Mentions: The image data was processed using an MA detector, based on the image processing techniques (Figure 1) [25, 26]. In short, the green plane of the image is shade corrected, by subtracting the median filtered version of the image (using a 35 × 35 rectangle) from the green component of each pixel value. On the shade corrected image, a contrast limited histogram equalization (CLAHE) is performed, which is used to enhance the contrast of the image.


Combined Methods for Diabetic Retinopathy Screening, Using Retina Photographs and Tear Fluid Proteomics Biomarkers.

Torok Z, Peto T, Csosz E, Tukacs E, Molnar AM, Berta A, Tozser J, Hajdu A, Nagy V, Domokos B, Csutak A - J Diabetes Res (2015)

Microaneurysm detection. (a) Original retina image; (b) CLAHE contrast enhancement; (c) median filtering; (d) top-hat transform; (e) raw MA candidates.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4499636&req=5

fig1: Microaneurysm detection. (a) Original retina image; (b) CLAHE contrast enhancement; (c) median filtering; (d) top-hat transform; (e) raw MA candidates.
Mentions: The image data was processed using an MA detector, based on the image processing techniques (Figure 1) [25, 26]. In short, the green plane of the image is shade corrected, by subtracting the median filtered version of the image (using a 35 × 35 rectangle) from the green component of each pixel value. On the shade corrected image, a contrast limited histogram equalization (CLAHE) is performed, which is used to enhance the contrast of the image.

Bottom Line: MA detection method alone resulted in 0.84 sensitivity and 0.81 specificity.Conclusions.As the two different types of data represent independent and complementary information on the outcome, the combined model resulted in a reliable screening method that is comparable to the requirements of DR screening programs applied in clinical routine.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Graphics and Image Processing, Bioinformatics Research Group, Faculty of Informatics, University of Debrecen, Egyetem tér 1, Debrecen 4032, Hungary ; Department of Ophthalmology, Faculty of Medicine, University of Debrecen, Egyetem tér 1, Debrecen 4032, Hungary ; Astridbio Technologies Inc., 439 University Avenue, Toronto, ON, Canada M5G 1Y8.

ABSTRACT
Background. It is estimated that 347 million people suffer from diabetes mellitus (DM), and almost 5 million are blind due to diabetic retinopathy (DR). The progression of DR can be slowed down with early diagnosis and treatment. Therefore our aim was to develop a novel automated method for DR screening. Methods. 52 patients with diabetes mellitus were enrolled into the project. Of all patients, 39 had signs of DR. Digital retina images and tear fluid samples were taken from each eye. The results from the tear fluid proteomics analysis and from digital microaneurysm (MA) detection on fundus images were used as the input of a machine learning system. Results. MA detection method alone resulted in 0.84 sensitivity and 0.81 specificity. Using the proteomics data for analysis 0.87 sensitivity and 0.68 specificity values were achieved. The combined data analysis integrated the features of the proteomics data along with the number of detected MAs in the associated image and achieved sensitivity/specificity values of 0.93/0.78. Conclusions. As the two different types of data represent independent and complementary information on the outcome, the combined model resulted in a reliable screening method that is comparable to the requirements of DR screening programs applied in clinical routine.

No MeSH data available.


Related in: MedlinePlus