Title

CHROMATIC PUPILLARY REFLEX AS A SCREENING TOOL FOR TYPE 2 DIABETES MELLITUS DIAGNOSIS

Introduction

Approximately 50% of people with diabetes remain undiagnosed, especially in developing countries, until the onset of clinical complications. To develop an automated pupilometry system (SAP) to diagnosis of type 2 diabetes based on the pupillary reflex

Methods

A pupilometer with a lighting system was used with external lighting sealing. While the RGB LED lighting system offers a solution for a pupil response, an infrared camera captures it as images. To evaluate the direct pupil reflex, the pupilometer was used to record videos during stimuli with a luminance of 250 cd / m2 and 1 second of duration after the patient was adapted to the dark for 10 minutes. The interval between stimuli was of 59 seconds. After a data capture, a data processing phase, data return declaration and data normalization were applied. In the last phase, a learning machine algorithm, called Random Forest, was applied to create the classification model of patients. The patients were classified in groups: Group 1 – Healthy, Group 2 – Type 2 Diabetes. All patients underwent complete ophthalmologic consultation and macular OCT. Thus, the patients were according to the diagnosis of the type2 diabetes based on the American Diabetes Association. The study was approved by the Institutional Review Board CAAE: 23723213.0.0000.5083.

Results

Automated pupilometry system was able to record, induce, and extract 96 pupil features. 31 volunteers were analyzed (16 in Group 1, 15 in Group 2), of which 22 were female volunteers (70.97%) and 9 were male volunteers (29.03%). A mean age of 60 year. As a result of the automated classification, Random Forest presented a result of 94.0% accuracy in the identification of diabetics type II was obtained.

Discussion

The present results are consistent with previously published studies, showing that diabetes is associated with dysregulation of the autonomic system. The proposal proved to be promising, noninvasive, objective and portable method of identifying the Type 2 Diabetes.

Keywords

Chromatic pupillary reflex; Diabetes Mellitus, Type 2; Artificial Intelligence

Area

CLINICAL CASE

Authors

Eduardo Nery Rossi Camilo, Cleyton Rafael Gomes Silva, Cristhiane Goncalves, Ronaldo Matins Costa, Celso Goncalves Camilo Junior, Nelson Rassi, Augusto Paranhos Junior