| CTRI Number |
CTRI/2025/08/092570 [Registered on: 07/08/2025] Trial Registered Prospectively |
| Last Modified On: |
06/08/2025 |
| Post Graduate Thesis |
Yes |
| Type of Trial |
Observational |
|
Type of Study
|
Cross Sectional Study |
| Study Design |
Single Arm Study |
|
Public Title of Study
|
Assessment of associations of people with pimples over face |
|
Scientific Title of Study
|
Evaluating associations and metabolic parameters in adult acne and exploration of deep learning techniques for acne grading: A cross-sectional study |
| 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 Ishita Bansal |
| Designation |
Junior Resident, Department of Dermatology Venereology and Leprosy |
| Affiliation |
Kasturba Medical College, MAHE, Manipal |
| Address |
OPD 21, Old building, Department of Dermatology Venereology and Leprosy, Kasturba Medical College, Eshwar Nagar, Manipal, Karnataka
Udupi
KARNATAKA
576104
India |
| Phone |
9711942808 |
| Fax |
|
| Email |
ishib99@gmail.com |
|
Details of Contact Person
Scientific Query
|
| Name |
Dr Ishita Bansal |
| Designation |
Junior Resident, Department of Dermatology Venereology and Leprosy |
| Affiliation |
Kasturba Medical College, MAHE, Manipal |
| Address |
OPD 21,Old building, Department of Dermatology Venereology and Leprosy, Kasturba Medical College, Eshwar Nagar, Manipal, Karnataka
Udupi
KARNATAKA
576104
India |
| Phone |
9711942808 |
| Fax |
|
| Email |
ishib99@gmail.com |
|
Details of Contact Person
Public Query
|
| Name |
Dr Ishita Bansal |
| Designation |
Junior Resident, Department of Dermatology Venereology and Leprosy |
| Affiliation |
Kasturba Medical College, MAHE, Manipal |
| Address |
OPD 21,Old building, Department of Dermatology Venereology and Leprosy, Kasturba Medical College, Eshwar Nagar, Manipal, Karnataka
Udupi
KARNATAKA
576104
India |
| Phone |
9711942808 |
| Fax |
|
| Email |
ishib99@gmail.com |
|
|
Source of Monetary or Material Support
|
| Department of Dermatology Venereology and Leprosy, Kasturba Medical College, Eshwar Nagar, Manipal, Karnataka |
|
|
Primary Sponsor
|
| Name |
Dr Ishita Bansal |
| Address |
Department of Dermatology Venereology and Leprosy, KMC , Eshwar Nagar, Manipal, Karnataka, 576104 |
| Type of Sponsor |
Other [SELF] |
|
|
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 Ishita Bansal |
Kasturba Medical College |
OPD 21, Old building, Department of Dermatology Venereology and Leprosy, Kasturba Medical College, Eshwar Nagar, Manipal, Karnataka
Udupi
KARNATAKA |
9711942808
ishib99@gmail.com |
|
|
Details of Ethics Committee
|
| No of Ethics Committees= 1 |
| Name of Committee |
Approval Status |
| Kasturba Medical College and Kasturba Hospital Institutional Ethics Committee - 2 (Student Research) |
Approved |
|
|
Regulatory Clearance Status from DCGI
|
|
|
Health Condition / Problems Studied
|
| Health Type |
Condition |
| Patients |
(1) ICD-10 Condition: L700||Acne vulgaris, |
|
|
Intervention / Comparator Agent
|
| Type |
Name |
Details |
| Intervention |
Nil |
Nil |
|
|
Inclusion Criteria
|
| Age From |
25.00 Year(s) |
| Age To |
55.00 Year(s) |
| Gender |
Both |
| Details |
All consulting adult patients of age more than 25 years of Acne vulgaris, who visit the dermatology outpatient/inpatient department at KMC, Manipal |
|
| ExclusionCriteria |
| Details |
1)Clinical diagnosis of rosacea which can mimic acne
2)Acneiform eruptions
3)Drug induced acne
4)Truncal acne without any face lesions |
|
|
Method of Generating Random Sequence
|
Not Applicable |
|
Method of Concealment
|
Not Applicable |
|
Blinding/Masking
|
Not Applicable |
|
Primary Outcome
|
| Outcome |
TimePoints |
1. To study the clinical types and associations of adult acne
2. To grade acne based on modified Global Acne Grading System |
Baseline |
|
|
Secondary Outcome
|
| Outcome |
TimePoints |
1. To integrate Artificial intelligence methods of machine learning / deep learning in acne grading
2. To correlate acne with visceral adiposity index and Insulin resistance in a subset of
study subjects |
2 years |
|
|
Target Sample Size
|
Total Sample Size="150"
Sample Size from India="150"
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)
|
18/08/2025 |
| 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="2"
Months="0"
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
|
Prevalence and Impact: Acne affects approximately 9.4% of the global population, with adolescents constituting about 85% of cases. It carries significant physical, psychological, and social burdens, straining the healthcare resources. Epidemiological evidence shows a strong correlation between Insulin resistance and moderate to severe acne. Acne is more prevalent in metabolic disorders such as PCOS and metabolic syndrome, suggesting a role for metabolic dysfunction in its pathogenesis. Therapeutic Implications: Early identification and management of metabolic abnormalities may enhance overall patient outcomes. Targeting insulin sensitivity (e.g., with metformin or dietary changes) could serve as an effective adjunct to traditional acne treatments. Role of Artificial Intelligence (AI): AI can ensures objective, standardized, and reproducible acne grading, reducing the variability of traditional assessments. AI-driven models offer fast, accurate, and personalized treatment recommendations and improve accessibility to dermatological care, particularly in remote areas. |