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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

Application of machine learning algorithm in the combined model. Learning phase (above): we randomly select a subpopulation of the total patient group, called the training group, and then use the known clinical diagnosis to split the training group into a DR group and a non-DR group. The clinical diagnosis, the number of MAs on the retina images, and the protein concentration values are the inputs of the machine learning algorithm. The algorithms are able to tell which data patterns are the most characteristic for the DR and non-DR groups. Assessment phase (below): in the following steps, we use the data from the validation group. The number of MAs and the protein concentration values constitute the input of the algorithm, but we do not use the information from clinical diagnosis. The learning algorithm compares the new data to the characteristic patterns that are known from the learning phase and will make its own decision (normal/DR) for each patient as the output of the model. For the assessment of the performance of the model, we compare the output with the known clinical diagnosis.
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fig2: Application of machine learning algorithm in the combined model. Learning phase (above): we randomly select a subpopulation of the total patient group, called the training group, and then use the known clinical diagnosis to split the training group into a DR group and a non-DR group. The clinical diagnosis, the number of MAs on the retina images, and the protein concentration values are the inputs of the machine learning algorithm. The algorithms are able to tell which data patterns are the most characteristic for the DR and non-DR groups. Assessment phase (below): in the following steps, we use the data from the validation group. The number of MAs and the protein concentration values constitute the input of the algorithm, but we do not use the information from clinical diagnosis. The learning algorithm compares the new data to the characteristic patterns that are known from the learning phase and will make its own decision (normal/DR) for each patient as the output of the model. For the assessment of the performance of the model, we compare the output with the known clinical diagnosis.

Mentions: As a next step, following the learning process, we intended to assess the performance of the screening method. At this phase only the protein levels and retinal images were entered into the system without diagnosis. Our goal was to show that the model based on both types of data has better performance than the models based on either the image data or the proteomics data alone. Figure 2 shows the learning and the assessment phases of the application of the machine learning algorithm.


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)

Application of machine learning algorithm in the combined model. Learning phase (above): we randomly select a subpopulation of the total patient group, called the training group, and then use the known clinical diagnosis to split the training group into a DR group and a non-DR group. The clinical diagnosis, the number of MAs on the retina images, and the protein concentration values are the inputs of the machine learning algorithm. The algorithms are able to tell which data patterns are the most characteristic for the DR and non-DR groups. Assessment phase (below): in the following steps, we use the data from the validation group. The number of MAs and the protein concentration values constitute the input of the algorithm, but we do not use the information from clinical diagnosis. The learning algorithm compares the new data to the characteristic patterns that are known from the learning phase and will make its own decision (normal/DR) for each patient as the output of the model. For the assessment of the performance of the model, we compare the output with the known clinical diagnosis.
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4499636&req=5

fig2: Application of machine learning algorithm in the combined model. Learning phase (above): we randomly select a subpopulation of the total patient group, called the training group, and then use the known clinical diagnosis to split the training group into a DR group and a non-DR group. The clinical diagnosis, the number of MAs on the retina images, and the protein concentration values are the inputs of the machine learning algorithm. The algorithms are able to tell which data patterns are the most characteristic for the DR and non-DR groups. Assessment phase (below): in the following steps, we use the data from the validation group. The number of MAs and the protein concentration values constitute the input of the algorithm, but we do not use the information from clinical diagnosis. The learning algorithm compares the new data to the characteristic patterns that are known from the learning phase and will make its own decision (normal/DR) for each patient as the output of the model. For the assessment of the performance of the model, we compare the output with the known clinical diagnosis.
Mentions: As a next step, following the learning process, we intended to assess the performance of the screening method. At this phase only the protein levels and retinal images were entered into the system without diagnosis. Our goal was to show that the model based on both types of data has better performance than the models based on either the image data or the proteomics data alone. Figure 2 shows the learning and the assessment phases of the application of the machine learning algorithm.

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