Diabetic retinopathy screening using artificial intelligence and its predictors among people with type 2 diabetes mellitus in an urban area of Durgapur.

INTRODUCTION

Diabetes mellitus (DM) is a metabolic disorder characterized by chronic hyperglycaemia either due to insulin resistance or due to relative or absolute insulin deficiency. Poorly controlled DM may result in both macrovascular and/or microvascular complications like diabetic retinopathy [DR]. Dilated eye examination is the most commonly employed method to diagnose DR. Nonmydriatic artificial intelligence [AI]-based technologies are the now available to screen DR.

METHODS

A cross-sectional observational study was conducted in urban field practice area of our medical college for 2 months duration. A total of 95 patients with type 2 DM were interviewed using predesigned, pretested semistructured schedule to collect data. Medical records were reviewed to collect relevant information. DR was screened using AI-based DR screening instrument, and venous blood sample was collected for glycated hemoglobin (HbA1C) testing. Data were analyzed using IBM SPSS [version 16]. Univariate and multivariate logistic regression tests were used, and value ≤ 0.05 was taken as statistically significant.

RESULTS

The prevalence of DR was 17.9% in our study. Around 76.9% respondents had high fasting blood glucose [FBG: ≥126 mg/dl], and majority of the respondents [73.7%] had HbA1C value >7%. DR was significantly associated with FBG level, longer duration of diabetes, presence of hypertension, dyslipidemia, and kidney disease in univariate logistic regression and, in multivariable logistic regression, FBG level, presence of dyslipidemia and kidney disease retained their significance.

CONCLUSION

This study had used AI-based DR screening instrument, to screen DR among T2DM patients. AI-based DR screening system can be encouraged in mass screening camps, especially in areas with inadequate number of ophthalmologists. This study also evaluated some important modifiable predictors of DR. Appropriate and early identification of such predictors may prevent DR-related blindness.