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A Feasibility Study of Diabetic Retinopathy Detection in Type II Diabetic Patients Based on Explainable Artificial Intelligence

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Abstract

Diabetic retinopathy (DR) is vision impairment and a life-threatening condition for diabetic patients. Especially type II diabetic people have higher chances of getting retinal problems. Hence, early prediction of DR is necessary for preventing the diabetic patients from vision impairment. The main aim of this feasibility study is to identify the most critical risk features that could lead to diabetic retinopathy. This study investigated type II diabetic patients’ socio-analytical, diabetes, behavioral, and clinical risk factors. We conducted a self-individual questionnaire session for all participants. Our questionnaire asked about the reliability of results, feeling comfortable during the screening test, willingness to participate in future screenings, overall perspective, and satisfaction with the DR screening test. We proposed a random forest model for predicting the prevalence of DR risk among diabetics. Further explanations of the model were conducted using more robust SHAP eXplainable Artificial Intelligence (XAI) tools. The SHAP method makes it possible to understand how input variables interact with their representative output records, as well as how input variables are ranked. In addition, various descriptive and inferential statistical analyses were performed on the data and evaluated the significant relationship between the factors discussed above via hypothesis testing. This feasibility study involved 172 type II diabetic patients (73 males and 99 females). Therefore, we found that 81 (47.09%) out of 172 participants had referable DR. The average age of the patients was determined as 55.08, with a standard deviation of ± 9.770 (ranging from 40 to 79). Type II patients were affected by mild, moderate, severe, and advanced proliferative diabetic retinopathy (PDR) stages with 23.83%, 13.95%, 5.81%, and 3.48%, respectively, of the total samples. The developed RF model obtained high accuracy of 94.9% using clinical dataset. Our results showed that the formation of tiny microminiature lesions was noticeable in type II diabetic patients with aged people, abnormal blood glucose levels, and prolonged diabetes duration.

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Data availability

The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

Anti VEGF:

Anti vascular endothelial growth factor

ANOVA:

Analysis of variance

BMI:

Body mass index

DBP:

Diastolic blood pressure

DR:

Diabetic retinopathy

EOS:

Electro optical system

ETDRS:

Early Treatment Diabetic Retinopathy Study

EURODIB:

Epidemiology and prevention of diabetes study

FOV:

Field of view

HbA1c:

Haemoglobin A1c

HSD:

Honestly significant difference

KMO:

Kaiser-Meyer-Olkin method

KNN:

K-nearest neighbour

LCD:

Liquid crystal display

LR:

Logistic Regression

NB:

Naïve Bayes

NHS:

National health service

NPDR:

Non proliferative diabetic retinopathy

PDR:

Proliferative diabetic retinopathy

RF:

Random Forest

SBP:

Systolic blood pressure

SHAP:

Shapley Additive Explanation

SPSS:

Software package for social science

SVM:

Support Vector Machine

WESDR:

Wisconsin epidemiologic study of diabetic retinopathy

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Acknowledgements

The authors would like to thank all participants and our institution SRMIST in this study. And we thank the doctors and clinical supporting faculties for their support in fundus image acquisition and disease severity detection.

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Authors and Affiliations

  1. Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur, Chennai, TN, India

    B. Lalithadevi & S. Krishnaveni

  2. Department of Ophthalmology, SRM Medical College Hospital and Research Centre, Kattankulathur, Chennai, TN, India

    J. Samuel Cornelius Gnanadurai

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  1. B. Lalithadevi
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  2. S. Krishnaveni
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  3. J. Samuel Cornelius Gnanadurai
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Contributions

All authors contributed to the study conception and design. B.Lalithadevi performed data collection, statistical analysis, preparation of manuscript, model development with shap analysis and interpretation of the results. S.Krishnaveni involved in study design, reviewing, editing the manuscript and participated in the result interpretation. Fundus images acquisition and disease severity grading were done by J.Samuel Cornelius Gnanadurai. All authors read and approved the final manuscript.

Corresponding author

Correspondence to B. Lalithadevi.

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Ethics approval

The procedures used in this study adhere to the tenets of the Declaration of Helsinki. This study was approved by the Institutional Ethics Committee (IEC) and was conducted at SRM Medical College Hospital and Research Centre, Kattankulathur according to the guidelines(Approval no/8377).

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Informed consent was obtained from all individual participants included in the study.

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Patients signed informed consent regarding publishing their data and photographs.

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The authors declare that they have no competing interests.

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Lalithadevi, B., Krishnaveni, S. & Gnanadurai, J.S.C. A Feasibility Study of Diabetic Retinopathy Detection in Type II Diabetic Patients Based on Explainable Artificial Intelligence. J Med Syst 47, 85 (2023). https://doi.org/10.1007/s10916-023-01976-7

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