| CTRI Number |
CTRI/2025/04/085079 [Registered on: 17/04/2025] Trial Registered Prospectively |
| Last Modified On: |
16/04/2025 |
| Post Graduate Thesis |
No |
| Type of Trial |
Interventional |
|
Type of Study
|
Dentistry |
| Study Design |
Other |
|
Public Title of Study
|
ChkUrSmle: AI-Powered Mobile App for Orthodontic Treatment Need Assessment Using Intraoral Photos. |
|
Scientific Title of Study
|
Development and Validation of an AI-Powered Mobile Application (ChkUrSmle) for Orthodontic Treatment Need Assessment Using Intraoral Photographs |
| 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 AKSHATA AWACHAT |
| Designation |
Post Graduate Student |
| Affiliation |
RANJEET DEKHMUKH DENTAL COLLEGE AND RESEARCH CENTRE, NAGPUR |
| Address |
Room no. 110, 1st floor, Department of Orthodontics and Dentofacial Orthopaedics, Ranjeet Deshmukh Dental College and Research Centre, Nagpur.
Nagpur
MAHARASHTRA
440019
India |
| Phone |
08554916649 |
| Fax |
|
| Email |
awachat24akshata@gmail.com |
|
Details of Contact Person
Scientific Query
|
| Name |
Dr Ananya Hazare |
| Designation |
Associate Professor |
| Affiliation |
RANJEET DEKHMUKH DENTAL COLLEGE AND RESEARCH CENTRE, NAGPUR |
| Address |
Room no. 110, 1st floor, Department of Orthodontics and Dentofacial Orthopaedics, Ranjeet Deshmukh Dental College and Research Centre, Nagpur.
Nagpur
MAHARASHTRA
440019
India |
| Phone |
08554916649 |
| Fax |
|
| Email |
ananya.hazare@gmail.com |
|
Details of Contact Person
Public Query
|
| Name |
Dr Ananya Hazare |
| Designation |
Associate Professor |
| Affiliation |
RANJEET DEKHMUKH DENTAL COLLEGE AND RESEARCH CENTRE, NAGPUR |
| Address |
Room no. 110, 1st floor, Department of Orthodontics and Dentofacial Orthopaedics, Ranjeet Deshmukh Dental College and Research Centre, Nagpur.
Nagpur
MAHARASHTRA
440019
India |
| Phone |
08554916649 |
| Fax |
|
| Email |
ananya.hazare@gmail.com |
|
|
Source of Monetary or Material Support
|
| Ranjeet Deshmukh Dental college and Research Centre, Nagpur |
|
|
Primary Sponsor
|
| Name |
Dr Akshata Awachat |
| Address |
Room No. 110, 1st Floor Department of Orthodontics and Dentofacial Orthopaedics, Ranjeet Deshmukh Dental College and Research Centre, Nagpur. |
| 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 AKSHATA AWACHAT |
Ranjeet Deshmukh Dental College and Research Centre, Nagpur. |
Room no. 110, 1st floor, Department of Orthodontics and Dentofacial Orthopaedics, Ranjeet Deshmukh Dental College and Research Centre, Nagpur.
Nagpur
MAHARASHTRA |
08554916649
awachat24akshata@gmail.com |
|
|
Details of Ethics Committee
|
| No of Ethics Committees= 1 |
| Name of Committee |
Approval Status |
| INSTITUTIONAL ETHICS COMMITTEE VSPM DCRC, NAGPUR |
Approved |
|
|
Regulatory Clearance Status from DCGI
|
|
|
Health Condition / Problems Studied
|
| Health Type |
Condition |
| Healthy Human Volunteers |
Only Including Individuals with Dental Malocclusion Requiring Treatment for Correction of Their Dental Problems |
| Patients |
(1) ICD-10 Condition: K008||Other disorders of tooth development, |
|
|
Intervention / Comparator Agent
|
| Type |
Name |
Details |
| Intervention |
AI-Powered Mobile App (ChkUrSmle) for Intraoral Photograph-based Orthodontic Screening |
ChkUrSmle is an artificial intelligence (AI)-powered mobile application developed to assess orthodontic treatment needs using intraoral photographs captured through a smartphone camera. The application utilizes deep learning algorithms trained on the Index of Orthodontic Treatment Need (IOTN) to classify and grade malocclusions.
Participants will be asked to provide intraoral photographs (frontal, left buccal, right buccal, and occlusal views), which will be uploaded to the ChkUrSmle app. The AI model will analyze these images and generate an IOTN grade (Dental Health Component), which will be compared to the standard grading done by trained orthodontists.
The aim of the intervention is to validate the diagnostic accuracy, reliability, and feasibility of the mobile app in real-time clinical settings and assess its potential for screening orthodontic treatment needs in adolescents and young adults.
|
|
|
Inclusion Criteria
|
| Age From |
10.00 Year(s) |
| Age To |
18.00 Year(s) |
| Gender |
Both |
| Details |
1.Oral assent from subjects and written consent from parents/Guardian willing to participate in the study.
2.Participants aged between 10 and 18 years.
3.Availability of intraoral photographs taken as part of the study.
4. No prior orthodontic treatment (such as braces or aligners).
5.Ability to follow simple instructions for capturing intraoral photographs.
6.Participants willing to participate in the study.
|
|
| ExclusionCriteria |
| Details |
1.Participants with a history of significant dental trauma.
2.Individuals who have already undergone orthodontic treatment.
3.Participants who are unable to provide consent or have not received parental consent (for minors).
4.Presence of active oral infections or severe dental caries that could interfere with the assessment.
|
|
|
Method of Generating Random Sequence
|
Not Applicable |
|
Method of Concealment
|
Not Applicable |
|
Blinding/Masking
|
Not Applicable |
|
Primary Outcome
|
| Outcome |
TimePoints |
1.Developing an AI-driven system for automated orthodontic assessment.
2.Validating the accuracy and reliability of AI-based IOTN scoring by comparing it with manual evaluations by orthodontic specialists.
3.Enhancing accessibility to orthodontic diagnostics by providing a non-invasive, remote, and cost-effective assessment tool.
|
12 Months
|
|
|
Secondary Outcome
|
|
|
Target Sample Size
|
Total Sample Size="1000"
Sample Size from India="1000"
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)
|
14/05/2025 |
| 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="1"
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
|
SUMMARY: Rationale and Background: Orthodontic malocclusions, if untreated, can lead to significant oral health issues, including speech difficulties, chewing impairments, and periodontal diseases. In underserved regions, limited access to orthodontic care exacerbates these issues, leaving many individuals without timely diagnosis or treatment. With the growing capabilities of artificial intelligence (AI), automated assessment offers a promising solution to enhance accessibility and efficiency in orthodontic diagnostics. Novelty: This study introduces a novel AI-powered mobile application for orthodontic treatment need assessment using intraoral photographs. By automating the Index of Orthodontic Treatment Need (IOTN) scoring, the app provides standardized and objective evaluations. Unlike traditional manual assessments that require in-person visits, this AI-based tool enables remote, cost-effective, and rapid diagnosis, making orthodontic care more accessible, particularly in resource-limited areas. Objective: The study aims to develop and validate an AI-based system for automated IOTN scoring by comparing it with manual assessments by orthodontic specialists. Methods: This cross-sectional study will include 1,000 participants aged 10–18 years from Ranjeet Deshmukh Dental College, Nagpur. Intraoral photographs will be analyzed by both the AI system and orthodontic experts. Statistical validation using Kappa statistics and Spearman correlation will measure the agreement and accuracy between AI and manual evaluations. Expected Outcome: The study aims to demonstrate that AI can deliver reliable and standardized orthodontic assessments, improving access to early diagnosis and treatment. This innovation has the potential to enhance oral healthcare equity, especially in remote and underserved areas. |