Eye-tracking-based Artificial Intelligence Detects Abnormalities of the Oculomotor System in Type 1 Diabetes

Study Overview
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Brief Summary
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Abnormalities of the oculomotor system may represent an early sign of diabetic neuropathy and are currently poorly studied. The investigators designed an eye-tracking-based test to evaluate the oculomotor function in patients with type 1 diabetes. The investigators used the SRLab -Tobii TX300 Eye tracker®, an eye-tracking device, coupled with a software that we developed to test abnormalities of the oculomotor system. The software consists in a series of screens divided in 5 classes of parameters (Resistance, Wideness, Pursuit, Velocity and Optokinetic Nystagmus [OKN]) to evaluate both smooth and saccadic movement in different directions. 40 healthy volunteers and 40 patients with long-standing type 1 diabetes will be enrolled to analyze the alterations in the oculomotor system and function.

Detailed Description
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The study will enroll 40 healthy volunteers and 40 patients with long-standing type 1 diabetes. The aim of the study is to analyze alterations in the oculomotor system function as an early sign of diabetic neuropathy. A novel eye-tracking-based test will be employed and 5 parameters will be analyzed through the use of a computer-based test which will record timeframes of eye movements towards an object, type of eye movements, time between the first eye movement, number of visualization required to recognize the object across the screen. Each of these parameter will be employed to assess whether Resistance, Wideness, Pursuit, Velocity of eye movements and movements related to the Optokinetic Nystagmus are altered in type 1 diabetes as compared to non diabetic subjects.

Official Title
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Eye-tracking-based Artificial Intelligence Detects Abnormalities of the Oculomotor System in Type 1 Diabetes

Conditions
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Diabetic Neuropathies

Intervention / Treatment
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* Diagnostic Test: Eye-tracking video test


Participation Criteria
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Eligibility Criteria
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Patients: Inclusion Criteria: Type 1 diabetes willing to give consent Exclusion Criteria: Diabetic retinopathy and other ocular diseases other endocrine disease malignancy Healthy volunteers: Inclusion Criteria: willing to give consent Exclusion Criteria: Other ocular diseases Any endocrine disease Malignancy

Ages Eligible for Study
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Minimum Age: 16 Years
Maximum Age: 60 Years

Sexes Eligible for Study
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All

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Long-standing T1D patients<br>Patients with long-standing type 1 diabetes | Diagnostic Test: Eye-tracking video test<br>* An eye-tracking-based test will be administered to patients. It lasts 10 minutes. It consists in several screens passing by to identify objects at a certain time by a mouse clic or touchpad.<br>|
| Healthy volunteers<br>Healthy volunteers | Diagnostic Test: Eye-tracking video test<br>* An eye-tracking-based test will be administered to patients. It lasts 10 minutes. It consists in several screens passing by to identify objects at a certain time by a mouse clic or touchpad.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Percentage of patients with a change in eye movement test in each class Resistance, Velocity, Pursuit, Wideness and OKN measured by the eye-tracking based test | Percentage of all parameters tested in each class changed in patients with type 1 diabetes as compared to healthy subjects. Parameters evaluated include number of visualization required to recognize a target and time (seconds), to recognize a target moving on the screen in different directions. | End of the study (when all patients have completed all sessions of the test), an average of 6 months | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Percentage of patients with a change in eye movement test in the Resistance class | Percentage of parameters tested in the Resistance class changed in patients with type 1 diabetes as compared to healthy subjects. Resistance will be measured by using number of visualization required to recognize a target and time (seconds) required to recognize a target moving on the screen in different directions. An algorithm will attribute those measurements to the specific class of Resistance. | End of the study (when all patients have completed all sessions of the test), an average of 6 months | 
| Percentage of patients with a change in eye movement test in the Wideness class | Percentage of parameters tested in the Wideness class changed in patients with type 1 diabetes as compared to healthy subjects. Wideness will be measured by using number of visualization required to recognize a target and time (seconds) required to recognize a target moving on the screen in different directions. An algorithm will attribute those measurements to the specific class of Wideness. | End of the study (when all patients have completed all sessions of the test), an average of 6 months | 
| Percentage of patients with a change in eye movement test in the Velocity class | Percentage of parameters tested in the Velocity class changed in patients with type 1 diabetes as compared to healthy subjects. Velocity will be measured by using number of visualization required to recognize a target and time (seconds) required to recognize a target moving on the screen in different directions. An algorithm will attribute those measurements to the specific class of Velocity. | End of the study (when all patients have completed all sessions of the test), an average of 6 months |