| CTRI Number |
CTRI/2025/08/092814 [Registered on: 11/08/2025] Trial Registered Prospectively |
| Last Modified On: |
11/08/2025 |
| Post Graduate Thesis |
Yes |
| Type of Trial |
Observational |
|
Type of Study
|
Cross Sectional Study |
| Study Design |
Single Arm Study |
|
Public Title of Study
|
Artificial Intelligence in diagnosis of microscopy images in skin disease |
|
Scientific Title of Study
|
A Cross sectional study of the utility of Artificial Intelligence in Direct
Immunofluorescence microscopy: An image based approach |
| Trial Acronym |
NIL |
|
Secondary IDs if Any
|
| Secondary ID |
Identifier |
| NIL |
NIL |
|
|
Details of Principal Investigator or overall Trial Coordinator (multi-center study)
|
| Name |
Aishwarya Dhanuka |
| Designation |
MD student, Junior Resident |
| Affiliation |
Kasturba Medical College, Manipal |
| Address |
Department of Dermatology Kasturba Medical College, Manipal, MAHE Karnataka
Eshwar nagar
Udupi
KARNATAKA
576104
India |
| Phone |
8288021265 |
| Fax |
|
| Email |
dhanuka.aishwarya@gmail.com |
|
Details of Contact Person
Scientific Query
|
| Name |
Dr Raghavendra Rao |
| Designation |
MBBS , MD , DNB Professor and Head of unit |
| Affiliation |
Kasturba Medical College, Manipal |
| Address |
Department of Dermatology Kasturba Medical College, Manipal, MAHE Karnataka
Eshwar nagar
Udupi
KARNATAKA
576104
India |
| Phone |
9845292640 |
| Fax |
|
| Email |
jenny.rao@manipal.edu |
|
Details of Contact Person
Public Query
|
| Name |
Dr Raghavendra Rao |
| Designation |
MBBS , MD , DNB Professor and Head of unit |
| Affiliation |
Kasturba Medical College, Manipal |
| Address |
Department of Dermatology Kasturba Medical College, Manipal, MAHE Karnataka
Eshwar nagar
Udupi
KARNATAKA
576104
India |
| Phone |
9845292640 |
| Fax |
|
| Email |
jenny.rao@manipal.edu |
|
|
Source of Monetary or Material Support
|
| OPD Room no 21,
Old building , Department of Dermatology Kasturba Medical College, Manipal, MAHE Karnataka
Udupi
KARNATAKA
576104
India |
|
|
Primary Sponsor
|
| Name |
Dr Aishwarya Dhanuka |
| Address |
OPD Room no 21,
Old building, Department of Dermatology Kasturba Medical College, Manipal, MAHE Karnataka
Udupi
KARNATAKA
576104
India |
| 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 Aishwarya Dhanuka |
Kasturba Medical College |
OPD room no 21 , Old building,
Department of Dermatology
Udupi
KARNATAKA |
8288021265
dhanuka.aishwarya@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: L100||Pemphigus vulgaris, (2) ICD-10 Condition: L102||Pemphigus foliaceous, (3) ICD-10 Condition: L120||Bullous pemphigoid, (4) ICD-10 Condition: L123||Acquired epidermolysis bullosa, (5) ICD-10 Condition: L959||Vasculitis limited to the skin, unspecified, |
|
|
Intervention / Comparator Agent
|
| Type |
Name |
Details |
| Comparator Agent |
NIL |
NIL |
| Intervention |
NIL |
NIL |
|
|
Inclusion Criteria
|
| Age From |
1.00 Day(s) |
| Age To |
99.00 Year(s) |
| Gender |
Both |
| Details |
1) All DIF images (obtained from slides from skin biopsy samples) from patients with clinically suspected AIBDs and Vasculitis – that are received in DIF lab
2) All DIF Images in DIF lab with confirmed and labelled diagnosis (AIBDs and Vasculitis)
3)Slides diagnosed as negative (For training of AI)
|
|
| ExclusionCriteria |
| Details |
1)Poor quality images obtained from slides which were obtained from biopsies with insufficient dermis
2)Formalin stained samples
|
|
|
Method of Generating Random Sequence
|
Not Applicable |
|
Method of Concealment
|
Not Applicable |
|
Blinding/Masking
|
Not Applicable |
|
Primary Outcome
|
| Outcome |
TimePoints |
1) To assess the utility of AI in diagnostic algorithm of DIF microscopy.
2) To compare human interpretation of DIF slides with that of AI diagnosis.
|
24 months |
|
|
Secondary Outcome
|
| Outcome |
TimePoints |
To compare different AI algorithms for their accuracy in diagnosis of DIF slides
|
24 months |
To establish potential advantages of AI in improving time required in diagnosing and accuracy of interpretation of diseases : a) Autoimmune bullous diseases b) Vasculitis |
24 months |
|
|
Target Sample Size
|
Total Sample Size="1600"
Sample Size from India="1600"
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)
|
28/08/2025 |
| 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 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
|
This study is a cross-sectional study which aims to assess the utility of artificial intelligence (AI) in interpreting direct immunofluorescence (DIF) images for diagnosing autoimmune skin diseases and vasculitis. DIF microscopy is the gold standard for diagnosis of Autoimmune bullous diseases and useful in diagnosis of vasculitis but depends heavily on the expertise of dermatopathologists and is time-consuming. The study will compare the diagnostic accuracy and time taken by AI models with that of human experts using 1600 DIF images from skin biopsy slides. Various AI approaches, including machine learning, deep learning, and vision transformers, will be evaluated to identify which provides the best performance. The goal is to determine if AI can assist dermatopathologists by offering faster and reliable diagnoses, potentially improving diagnostic accuracy and efficiency in clinical practice. |