| CTRI Number |
CTRI/2025/11/096807 [Registered on: 03/11/2025] Trial Registered Prospectively |
| Last Modified On: |
31/10/2025 |
| Post Graduate Thesis |
No |
| Type of Trial |
Interventional |
|
Type of Study
|
Diagnostic
Preventive
Screening
Other (Specify) [Non Medical Device For Screening Diagnosis] |
| Study Design |
Non-randomized, Active Controlled Trial |
|
Public Title of Study
|
Neurological Condition Diagnosis using EGG & AI |
|
Scientific Title of Study
|
Artificial Intelligence AI Driven Analysis of Electroencephalography for the Detection of Dementia |
| Trial Acronym |
|
|
Secondary IDs if Any
|
| Secondary ID |
Identifier |
| NIL |
NIL |
|
|
Details of Principal Investigator or overall Trial Coordinator (multi-center study)
|
| Name |
Nidhi Nidhi |
| Designation |
Researcher |
| Affiliation |
NEMA AI |
| Address |
Block A-8, Lower Ground Floor
near Agarwal, Block A-8, Delhi, 110019
New Delhi
DELHI
110019
India |
| Phone |
9811366064 |
| Fax |
|
| Email |
nidhi@nemaai.com |
|
Details of Contact Person
Scientific Query
|
| Name |
Nidhi Nidhi |
| Designation |
Researcher |
| Affiliation |
NEMA AI |
| Address |
Block A-8
near Agarwal, Block A-8, Delhi, 110019
New Delhi
DELHI
110019
India |
| Phone |
9811366064 |
| Fax |
|
| Email |
nidhi@nemaai.com |
|
Details of Contact Person
Public Query
|
| Name |
Dr Divyani Garg |
| Designation |
Assistant Professor |
| Affiliation |
AIIMS |
| Address |
Room no 705, Department of Neurology, Neurosciences Centre, AIIMS, New Delhi,
All India Institute of Medical Sciences,
Ansari Nagar, New Delhi - 110029
South
DELHI
110029
India |
| Phone |
9810914907 |
| Fax |
|
| Email |
divyanig@gmail.com |
|
|
Source of Monetary or Material Support
|
| Room no 705, Department of Neurology, Neurosciences Centre, AIIMS, New Delhi, Nagar East, Delhi 110029 |
|
|
Primary Sponsor
|
| Name |
NEMA AI AIIMS Delhi |
| Address |
31, A-8, Lower Ground Floor, Kalkaji Extn, ND-11019 |
| Type of Sponsor |
Other [Private Ltd] |
|
|
Details of Secondary Sponsor
|
| Name |
Address |
| NEMA AI |
E115, Lower Ground Floor, Kalkaji Extn, ND-11019 |
| NIL |
NIL |
|
|
Countries of Recruitment
|
India |
|
Sites of Study
|
| No of Sites = 1 |
| Name of Principal
Investigator |
Name of Site |
Site Address |
Phone/Fax/Email |
| Dr Divyang Garg |
AIIMS |
Room no 705, Department of Neurology, Neurosciences Centre, AIIMS, New Delhi, Nagar East, Delhi 110029
South
DELHI |
9810914907
divyanig@gmail.com |
|
|
Details of Ethics Committee
|
| No of Ethics Committees= 1 |
| Name of Committee |
Approval Status |
| AIIMS |
Approved |
|
|
Regulatory Clearance Status from DCGI
|
|
|
Health Condition / Problems Studied
|
| Health Type |
Condition |
| Healthy Human Volunteers |
Individuals with no known Dementia or any other cognitive issues. |
| Patients |
(1) ICD-10 Condition: F028||Dementia in other diseases classified elsewhere, |
|
|
Intervention / Comparator Agent
|
| Type |
Name |
Details |
| Intervention |
NEMA AI EEG–Cognitive Diagnostic Software (SaMD) |
The intervention involves the use of NEMA AI EEG–Cognitive Diagnostic Software, a non-invasive AI-driven platform that analyzes brainwave data from a standard EEG headset to assist in identifying cognitive decline patterns associated with dementia and related neurological conditions.
Participants will undergo a 10–15 minute EEG recording session, after which EEG signals are processed through the NEMA AI software to generate a Cognitive & Neurological Pattern Report.
The output will be reviewed by a qualified neurologist/neuropsychologist for validation against clinical and neuropsychological assessments.
Mode of Administration: Non-invasive EEG scan + AI-based cognitive analytics (SaMD)
Duration of Use: Single session per participant (approx. 10 minutes total)
Frequency: One-time assessment per participant
Intended Purpose: To evaluate the diagnostic concordance and sensitivity of NEMA AI’s EEG-AI software in differentiating dementia from age-matched controls. |
| Comparator Agent |
Standard Clinical Diagnostic Assessment for Dementia |
The comparator will be the standard clinical diagnostic process for dementia followed at AIIMS, including:
Neurological evaluation by a specialist,
Neuropsychological testing (e.g., MMSE, MoCA, CDR, or equivalent scales), and
Imaging or laboratory evaluations (if applicable).
The results from these conventional methods will serve as the gold standard comparator to evaluate the accuracy, sensitivity, and specificity of the NEMA AI EEG–Cognitive Diagnostic Software.
Duration of Assessment: Typically completed within 1–2 clinical visits
Frequency: Single diagnostic evaluation |
|
|
Inclusion Criteria
|
| Age From |
18.00 Year(s) |
| Age To |
75.00 Year(s) |
| Gender |
Both |
| Details |
Cognitive deficits interfering with independence in everyday activities in case of major neurocognitive disorder or not interfering with independence in everyday activities in the case of minor neurocognitive disorder |
|
| ExclusionCriteria |
| Details |
Patients with other causes of neurocognitive impairment like metabolic, head injury, seizures, delirium etc.
Patients with neurocognitive impairment due to psychiatric disorders like depression, schizophrenia etc.
|
|
|
Method of Generating Random Sequence
|
Computer generated randomization |
|
Method of Concealment
|
Case Record Numbers |
|
Blinding/Masking
|
Not Applicable |
|
Primary Outcome
|
| Outcome |
TimePoints |
| To evaluate the diagnostic accuracy of the NEMA AI EEG software in identifying patients with dementia as compared to the standard clinical and neuropsychological assessment methods. The analysis will focus on the software’s ability to accurately differentiate between dementia and non-dementia cases using brainwave-derived cognitive biomarkers. |
The outcome measures will be assessed at baseline pre-intervention, 4 weeks, and 8 weeks to examine the diagnostic consistency, reproducibility, and longitudinal stability of NEMA AI’s EEG-based analytical model across multiple patient evaluations. |
|
|
Secondary Outcome
|
| Outcome |
TimePoints |
| To validate & publish a study with outcome of new model developed in the field of AI and health. |
6 month study and publication timeline |
|
|
Target Sample Size
|
Total Sample Size="60"
Sample Size from India="60"
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
|
Phase 2/ Phase 3 |
|
Date of First Enrollment (India)
|
15/11/2025 |
| 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="6"
Days="0" |
|
Recruitment Status of Trial (Global)
|
Not Yet Recruiting |
| 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
|
The increasing global prevalence of
dementia, coupled with the limitations of conventional diagnostic methods,
highlights the need for non-invasive, scalable, and cost-effective solutions. Electroencephalography (EEG)-based cognitive analytics
powered by AI presents an opportunity to bridge this gap by detecting subtle electrophysiological changes
before clinical symptoms manifest. EEG is a widely available and non-invasive tool, which can
be harnessed for this purpose. Traditional
dementia diagnostics rely on neuropsychological
assessment, MRI scans, or PET imaging, which are costly, time-consuming, and inaccessible in
many regions. There is a critical need for an affordable and scalable EEG-based
screening tool to enable early diagnosis,
intervention and improve patient
outcomes. Develop an AI-driven EEG analysis framework to detect neuro cognitive impairment (dementia) compared to
healthy controls |