EyekonT1D: Eye-tracking-based Artificial Intelligence Detects Abnormalities of the Oculomotor System in Type 1 Diabetes

Sponsor
University of Milan (Other)
Overall Status
Completed
CT.gov ID
NCT04608890
Collaborator
ASST Fatebenefratelli Sacco (Other)
40
1
13
3.1

Study Details

Study Description

Brief Summary

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 trackerĀ®, 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.

Condition or Disease Intervention/Treatment Phase
  • Diagnostic Test: Eye-tracking video test

Detailed Description

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.

Study Design

Study Type:
Observational
Actual Enrollment :
40 participants
Observational Model:
Case-Only
Time Perspective:
Cross-Sectional
Official Title:
Eye-tracking-based Artificial Intelligence Detects Abnormalities of the Oculomotor System in Type 1 Diabetes
Actual Study Start Date :
Dec 1, 2020
Actual Primary Completion Date :
Nov 1, 2021
Actual Study Completion Date :
Dec 31, 2021

Arms and Interventions

Arm Intervention/Treatment
Long-standing T1D patients

Patients with long-standing type 1 diabetes

Diagnostic Test: Eye-tracking video test
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.

Healthy volunteers

Healthy volunteers

Diagnostic Test: Eye-tracking video test
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.

Outcome Measures

Primary Outcome Measures

  1. 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 [End of the study (when all patients have completed all sessions of the test), an average of 6 months]

    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.

Secondary Outcome Measures

  1. Percentage of patients with a change in eye movement test in the Resistance class [End of the study (when all patients have completed all sessions of the test), an average of 6 months]

    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.

  2. Percentage of patients with a change in eye movement test in the Wideness class [End of the study (when all patients have completed all sessions of the test), an average of 6 months]

    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.

  3. Percentage of patients with a change in eye movement test in the Velocity class [End of the study (when all patients have completed all sessions of the test), an average of 6 months]

    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.

Eligibility Criteria

Criteria

Ages Eligible for Study:
16 Years to 60 Years
Sexes Eligible for Study:
All
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

Contacts and Locations

Locations

Site City State Country Postal Code
1 Sacco University Hospital Milan MI Italy 20157

Sponsors and Collaborators

  • University of Milan
  • ASST Fatebenefratelli Sacco

Investigators

None specified.

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.
Responsible Party:
Paolo Fiorina, MD, Associate Professor of Endocrinology, University of Milan
ClinicalTrials.gov Identifier:
NCT04608890
Other Study ID Numbers:
  • EyekonT1D
First Posted:
Oct 29, 2020
Last Update Posted:
May 17, 2022
Last Verified:
May 1, 2022
Individual Participant Data (IPD) Sharing Statement:
No
Plan to Share IPD:
No
Studies a U.S. FDA-regulated Drug Product:
No
Studies a U.S. FDA-regulated Device Product:
No
Additional relevant MeSH terms:

Study Results

No Results Posted as of May 17, 2022