| CTRI Number |
CTRI/2024/01/062157 [Registered on: 31/01/2024] Trial Registered Prospectively |
| Last Modified On: |
15/10/2024 |
| Post Graduate Thesis |
No |
| Type of Trial |
Observational |
|
Type of Study
|
Cross Sectional Study |
| Study Design |
Other |
|
Public Title of Study
|
Assessment of nadi patterns in Normal and Diabetic individuals |
|
Scientific Title of Study
|
Identification of Nadi patterns in individuals with defferent glycemic profiles using AI based technology and their validation for mass screening of T2DM |
| 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 Supriya Bhalerao |
| Designation |
Professor |
| Affiliation |
Interactive Research School for Health Affairs, Obesity Diabetes Lab |
| Address |
Interactive Research School for Health Affairs Obesity Diabetes Lab 1 st floor Bharati Vidyapeeth Pune
Pune
MAHARASHTRA
411043
India |
| Phone |
0204366920 |
| Fax |
|
| Email |
supriya.bhalerao@gmail.com |
|
Details of Contact Person
Scientific Query
|
| Name |
Dr Supriya Bhalerao |
| Designation |
Professor |
| Affiliation |
Interactive Research School for Health Affairs, Obesity Diabetes Lab |
| Address |
Interactive Research School for Health Affairs Obesity Diabetes Lab 1st floor Bharati Vidyapeeth Pune
Pune
MAHARASHTRA
411043
India |
| Phone |
0204366920 |
| Fax |
|
| Email |
supriya.bhalerao@gmail.com |
|
Details of Contact Person
Public Query
|
| Name |
Dr Tanuja Sawant |
| Designation |
Study Coordinator |
| Affiliation |
Interactive Research School for Health Affairs Bharati Vidyapeeth, Obesity Diabetes LAb |
| Address |
Interactive Research School for Health Affairs Obesity Diabetes Lab 1st floor Bharati Vidyapeeth Pune
Pune
MAHARASHTRA
411043
India |
| Phone |
0204366920 |
| Fax |
|
| Email |
vd.tanuja@gmail.com |
|
|
Source of Monetary or Material Support
|
| Atreya Innovations, Hinjewadi Phase 2, Pune |
|
|
Primary Sponsor
|
| Name |
Atreya Solutions Pvt Ltd Pune |
| Address |
Hinjewadi Phase 2, Pune |
| Type of Sponsor |
Research institution |
|
|
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 |
| Dr Supriya Bhalerao |
Interactive Research School for Health Affairs, Bharati Vidyapeeth, Pune |
Bharati Vidyapeeth University Campus, Pune Satara Road, Katraj, Pune
Pune
MAHARASHTRA |
02024366920
supriya.bhalerao@gmail.com |
|
|
Details of Ethics Committee
|
| No of Ethics Committees= 1 |
| Name of Committee |
Approval Status |
| Bharati Vidyapeeth medical College Institutional ethics Committee |
Approved |
|
|
Regulatory Clearance Status from DCGI
|
|
|
Health Condition / Problems Studied
|
| Health Type |
Condition |
| Patients |
(1) ICD-10 Condition:E118||Type 2 diabetes mellitus with unspecified complications. Ayurveda Condition: Diabetes, (2) ICD-10 Condition:E118||Type 2 diabetes mellitus with unspecified complications. Ayurveda Condition: PRAMEHAH, |
|
|
Intervention / Comparator Agent
|
| sno | Intervention/Comparator | Type | Drug-Type | Procedure Name | Details |
|
|
|
Inclusion Criteria
|
| Age From |
25.00 Year(s) |
| Age To |
55.00 Year(s) |
| Gender |
Both |
| Details |
Ready to give written informed consent and abide by study procedures |
|
| ExclusionCriteria |
| Details |
1. Pregnant ladies and lactating females
2. Individuals on corticosteroids, anti-psychotic, anti-anxiety and sleep inducing medications
3. Individuals participating in another clinical study at the time of study enrolment |
|
|
Method of Generating Random Sequence
|
Not Applicable |
|
Method of Concealment
|
Case Record Numbers |
|
Blinding/Masking
|
Not Applicable |
|
Primary Outcome
|
| Outcome |
TimePoints |
| Correlation of different Nadi patterns with diabetic profiles |
Baseline |
|
|
Secondary Outcome
|
| Outcome |
TimePoints |
| Different Nadi patterns |
2 hours
6 minutes |
| None |
None |
|
|
Target Sample Size
|
Total Sample Size="500"
Sample Size from India="500"
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)
|
03/02/2024 |
| 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)
Modification(s)
|
Not Applicable |
| Recruitment Status of Trial (India) |
Open to Recruitment |
|
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 conventional method for diagnosing diabetes is based on blood tests such as serum blood glucose and glycosylated haemoglobin (HbA1c) levels, which is invasive, time consuming and pain-inducing. Atreya Innovations Pvt. Ltd., a Niti Ayog Start-up Grant awardee has developed Nadi Tarangini for assessment of Nadi patterns. Nadi examination is an integral method for prediction and prognosis of diseases in Ayurveda. The Start-up wants to implement Nadi Tarangini for the mass screening of Type 2 diabetes using Artificial Intelligence (AI) based technology. For which apart from Nadi captured through Nadi Tarangini, other examinations namely Jihwa Pariksha (Tongue Examination), Shabda Pariksha (Voice Examination), Drik PAriksha (Face Examination) which are part of Ashtavidha Pariksha or eight tools of examinations recommended by Ayurveda will be taken into account. In addition, it also considers assessment of Prikriti (constitution) of an individual and his/her diet and lifestyle which form important part of Ayurveda diagnosis.
The present study is therefore planned in two parts. In the first part, information pertaining to all the above mentioned parameters will be captured in individuals with known glycemic profile and will be correlated with HbA1c. In the second part, based on the patterns established, the technology will be used for community screening of diabetes. Further, using Pricipal Component Analysis, the essential parameters from the above list required to capture for reliable screening of diabetes will be identified and the AI based protocol will be standardized. |