| CTRI Number |
CTRI/2025/11/097115 [Registered on: 10/11/2025] Trial Registered Prospectively |
| Last Modified On: |
10/11/2025 |
| Post Graduate Thesis |
No |
| Type of Trial |
Observational |
|
Type of Study
|
Cross Sectional Study |
| Study Design |
Single Arm Study |
|
Public Title of Study
|
Testing a New AI-Based Eye Screening Tool (3Nethra Ultima) to Detect Chronic Eye Diseases |
|
Scientific Title of Study
|
Clinical Validation of an Artificial Intelligence-Based Screening and Diagnostic Tool 3Nethra Ultima for chronic eye diseases compared to standard diagnostic methods |
| Trial Acronym |
nil |
|
Secondary IDs if Any
|
| Secondary ID |
Identifier |
| NIL |
NIL |
|
|
Details of Principal Investigator or overall Trial Coordinator (multi-center study)
|
| Name |
Dr. Kaushik Murali |
| Designation |
President Medical Administration |
| Affiliation |
sanakara Eye Foundation |
| Address |
Sankara Eye Hospital
Dept. of Peidatric OPhthalmology and strabismus, Varthur Main Road , Kundalahalli Gate
Bangalore
KARNATAKA
560037
India
Bangalore
KARNATAKA
560037
India |
| Phone |
9739000096 |
| Fax |
|
| Email |
kaushik@sankaraeye.com |
|
Details of Contact Person
Scientific Query
|
| Name |
Dr. Kaushik Murali |
| Designation |
President Medical Administration |
| Affiliation |
sanakara Eye Foundation |
| Address |
Dept. of pediatric Ophthalmology and strabismus Sankara Eye Hospital Varthur Main Road , Kundalahalli Gate
Bangalore
KARNATAKA
560037
India
Bangalore
KARNATAKA
560037
India |
| Phone |
9739000096 |
| Fax |
|
| Email |
kaushik@sankaraeye.com |
|
Details of Contact Person
Public Query
|
| Name |
Dr. Kaushik Murali |
| Designation |
President Medical Administration |
| Affiliation |
sanakara Eye Foundation |
| Address |
Sankara Eye Hospital Varthur Main Road , Kundalahalli Gate
Bangalore
KARNATAKA
560037
India
Bangalore
KARNATAKA
560037
India |
| Phone |
9739000096 |
| Fax |
|
| Email |
kaushik@sankaraeye.com |
|
|
Source of Monetary or Material Support
|
| Sankara Eye Hospital, Varthur Main Road, Kundalahalli Signal, Bengaluru 560037, Karnataka |
|
|
Primary Sponsor
|
| Name |
Sankara Academy of Vision |
| Address |
Sankara Eye Hospital Varthur Main Road Kundalahalli Gate Bengaluru 560037, Karnataka, India |
| Type of Sponsor |
Research institution and hospital |
|
|
Details of Secondary Sponsor
|
|
|
Countries of Recruitment
|
India |
|
Sites of Study
|
| No of Sites = 1 |
| Name of Principal
Investigator |
Name of Site |
Site Address |
Phone/Fax/Email |
| Kaushik Murali |
Sankara Eye Hospital Bangalore |
Dept. Pediatric OPhthalmolgy and strabismus, Sankara Eye Hospital Varthur Main Road Kundalahalli Gate Bengaluru 560037, Karnataka, India
Bangalore
KARNATAKA |
9739000096
kaushik@sankaraeye.com |
|
|
Details of Ethics Committee
|
| No of Ethics Committees= 1 |
| Name of Committee |
Approval Status |
| Sankara Eye Hospital Bangalore |
Approved |
|
|
Regulatory Clearance Status from DCGI
|
|
|
Health Condition / Problems Studied
|
| Health Type |
Condition |
| Patients |
(1) ICD-10 Condition: H36||Retinal disorders in diseases classified elsewhere, |
|
|
Intervention / Comparator Agent
|
| Type |
Name |
Details |
| Comparator Agent |
AI tool |
Efficiency of AI tool in detecting dry eyes, Age Related Macular Degeneration and Diabetic Retinopathy |
| Comparator Agent |
Clinical diagnosis |
Clinical diagnosis of dry eye, ARMD and DR by clinicians based on clinical findings |
|
|
Inclusion Criteria
|
| Age From |
18.00 Year(s) |
| Age To |
75.00 Year(s) |
| Gender |
Both |
| Details |
Participants who can understand and provide informed consent for participation in the study.
All Genders are included.
Adults with a diagnosis of Type 1 or Type 2 diabetes
|
|
| ExclusionCriteria |
| Details |
Participants with Active ocular infection
Participants with Mature cataract
Participants with Media opacities preventing good-quality retinal images
History of Corneal surgery or trauma in past 6 months
|
|
|
Method of Generating Random Sequence
|
Not Applicable |
|
Method of Concealment
|
Not Applicable |
|
Blinding/Masking
|
Not Applicable |
|
Primary Outcome
|
| Outcome |
TimePoints |
| Comparison of clinical efficiency of AI tool in detecting Chronic eye conditions when compared to specialist (gold standard) diagnoses. For NIBUT, TMH, MGD, LLI, diabetic retinopathy (DR),age-related macular degeneration (AMD) |
first visit |
|
|
Secondary Outcome
|
| Outcome |
TimePoints |
| To test the sensitivity & specificity of AI tool in detecting the dry eye, retinal diseases like ARMD & Diabetic retinopathy |
baseline one time visit |
|
|
Target Sample Size
|
Total Sample Size="738"
Sample Size from India="738"
Final Enrollment numbers achieved (Total)= "Applicable only for Completed/Terminated trials"
Final Enrollment numbers achieved (India)="Applicable only for Completed/Terminated trials" |
|
Phase of Trial
|
N/A |
|
Date of First Enrollment (India)
|
25/11/2025 |
| Date of Study Completion (India) |
Applicable only for Completed/Terminated trials |
| Date of First Enrollment (Global) |
Date Missing |
| Date of Study Completion (Global) |
Applicable only for Completed/Terminated trials |
|
Estimated Duration of Trial
|
Years="0"
Months="8"
Days="0" |
|
Recruitment Status of Trial (Global)
|
Not Applicable |
| Recruitment Status of Trial (India) |
Not Yet Recruiting |
|
Publication Details
|
N/A |
|
Individual Participant Data (IPD) Sharing Statement
|
Will individual participant data (IPD) be shared publicly (including data dictionaries)?
Response - NO
|
|
Brief Summary
|
This study aims to prospectively evaluate the diagnostic accuracy and clinical utility of the AI-based ophthalmic device 3Nethra Ultima for detecting Diabetic Retinopathy (DR), Age-Related Macular Degeneration (AMD), and Dry Eye Disease in a clinical setting. Diagnostic accuracy for DR and AMD will be assessed using fundus images from 3Nethra Ultima, 3Nethra Pico, and EIDON cameras, compared to a reference standard of independent dual expert grading and senior specialist adjudication. For Dry Eye Disease, the AI tool’s performance in measuring TBUT, TMH, LLI, and MGD will be evaluated against standard clinical diagnostic methods. The goal is to validate the AI system’s ability to support accurate, efficient, and scalable eye disease screening.To prospectively determine the diagnostic accuracy and clinical utility of the AI-driven 3nethra Ultima device for the automated detection of Diabetic Retinopathy (DR), Age-Related Macular Degeneration (AMD), and Dry Eye Disease, compared to established clinical diagnostic standards and masked expert evaluation. |