| CTRI Number |
CTRI/2024/11/077356 [Registered on: 26/11/2024] Trial Registered Prospectively |
| Last Modified On: |
26/11/2024 |
| Post Graduate Thesis |
Yes |
| Type of Trial |
Interventional |
|
Type of Study
|
Diagnostic |
| Study Design |
Randomized, Parallel Group Trial |
|
Public Title of Study
|
Evaluating Osteoarthritis in Adults with Knee or Hip X-Rays Using Artificial Intelligence A Comparison of AI Grading vs. Doctor Assessments to Determine Accuracy |
|
Scientific Title of Study
|
revolutionizing osteoarthritis evaluation in conventional radiography with AI powered grading. |
| Trial Acronym |
NIL |
|
Secondary IDs if Any
|
| Secondary ID |
Identifier |
| NIL |
NIL |
|
|
Details of Principal Investigator or overall Trial Coordinator (multi-center study)
|
| Name |
Dhivya G |
| Designation |
Postgraduate , Radio-diagnosis |
| Affiliation |
Saveetha Medical College Hospital |
| Address |
Room NO 50 ,Department of Radiodiagnosis ,Saveetha Medical College Hospital, Saveetha Nagar, Thandalam,
Chennai-602105
Chennai
TAMIL NADU
602105
India
Room NO 50 ,Department of Radiodiagnosis ,Saveetha Medical College Hospital, Saveetha Nagar, Thandalam,
Chennai-602105
Chennai
TAMIL NADU
602105
India |
| Phone |
9865537997 |
| Fax |
|
| Email |
dhivya3355@gmail.com |
|
Details of Contact Person
Scientific Query
|
| Name |
Yuvaraj Muralidharan |
| Designation |
Professor |
| Affiliation |
Saveetha Medical College Hospital |
| Address |
Room NO 50 ,Department of Radiodiagnosis ,Saveetha Medical College Hospital, Saveetha Nagar, Thandalam,
Chennai-602105
Chennai
TAMIL NADU
602105
India
Room NO 50 ,Department of Radiodiagnosis ,Saveetha Medical College Hospital, Saveetha Nagar, Thandalam,
Chennai-602105
Chennai
TAMIL NADU
602105
India |
| Phone |
9865537997 |
| Fax |
|
| Email |
dr.yuvraj1987@gmail.com |
|
Details of Contact Person
Public Query
|
| Name |
Dhivya G |
| Designation |
Postgraduate , Radio-diagnosis |
| Affiliation |
Saveetha Medical College Hospital |
| Address |
Room NO 50 ,Department of Radiodiagnosis ,Saveetha Medical College Hospital, Saveetha Nagar, Thandalam,
Chennai-602105
Chennai
TAMIL NADU
602105
India
Room NO 50 ,Department of Radiodiagnosis ,Saveetha Medical College Hospital, Saveetha Nagar, Thandalam,
Chennai-602105
Chennai
TAMIL NADU
602105
India |
| Phone |
9865537997 |
| Fax |
|
| Email |
dhivya3355@gmail.com |
|
|
Source of Monetary or Material Support
|
| Saveetha Medical College Hospital, Saveetha Nagar, Thandalam, Chennai-602105 |
|
|
Primary Sponsor
|
| Name |
DHIVYA G |
| Address |
Saveetha Medical College Hospital, Saveetha Nagar, Thandalam,
Chennai-602105 |
| 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 DHIVYA G |
Saveetha Medical College Hospital |
Room NO 50 ,Department of Radiodiagnosis ,Saveetha Nagar, Thandalam,
Chennai-602105
Chennai
TAMIL NADU
602105
India
Chennai
TAMIL NADU |
09865537997
dhivya3355@gmail.com |
|
|
Details of Ethics Committee
|
| No of Ethics Committees= 1 |
| Name of Committee |
Approval Status |
| Saveetha Medical College and Hospital Institutional Ethics Committee |
Approved |
|
|
Regulatory Clearance Status from DCGI
|
|
|
Health Condition / Problems Studied
|
| Health Type |
Condition |
| Patients |
(1) ICD-10 Condition: M179||Osteoarthritis of knee, unspecified, |
|
|
Intervention / Comparator Agent
|
| Type |
Name |
Details |
| Comparator Agent |
AI-based Osteoarthritis Grading System |
The comparator agent is the manual grading method performed by trained radiologists, who visually assess radiographs and assign OA severity scores based on standardized grading systems, such as the Kellgren-Lawrence scale. This traditional approach relies on the radiologist’s expertise and judgment, making it susceptible to inter- and intra-observer variability. While widely used, manual grading is often limited by its subjective nature and reduced sensitivity for identifying subtle, early-stage OA changes. |
| Intervention |
AI-based Osteoarthritis Grading System |
The intervention in this study is an AI-powered grading system designed to evaluate osteoarthritis (OA) severity using conventional radiographs. This system leverages advanced deep learning algorithms to detect and quantify key radiographic features such as joint space narrowing, osteophytes, and subchondral sclerosis. It provides automated, quantitative, and standardized grading of OA, adhering to established classification criteria like the Kellgren-Lawrence scale. The system is intended to enhance diagnostic precision, reduce variability in assessments, and facilitate the early detection of OA through objective analysis. |
|
|
Inclusion Criteria
|
| Age From |
18.00 Year(s) |
| Age To |
99.00 Year(s) |
| Gender |
Both |
| Details |
Patients diagnosed with osteoarthritis based on clinical and radiographic criteria.
Availability of high-quality radiographic images.
Patients of varying ages and genders to ensure a representative sample.
Complete demographic and clinical data accompanying the radiographic images.
|
|
| ExclusionCriteria |
| Details |
Poor quality or incomplete radiographic images.
Patients without a confirmed diagnosis of osteoarthritis.
Images without sufficient annotations from radiologists.
Lack of demographic or clinical data necessary for the study.
|
|
|
Method of Generating Random Sequence
|
Computer generated randomization |
|
Method of Concealment
|
An Open list of random numbers |
|
Blinding/Masking
|
Participant and Investigator Blinded |
|
Primary Outcome
|
| Outcome |
TimePoints |
Development of a robust AI algorithm for detecting and grading
osteoarthritis in radiographic images: |
Every month for 3 months
|
|
|
Secondary Outcome
|
| Outcome |
TimePoints |
Development of a robust AI algorithm for detecting and grading
osteoarthritis in radiographic imageS. |
3 MONTHS |
|
|
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
|
N/A |
|
Date of First Enrollment (India)
|
07/12/2024 |
| 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="1"
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
|
The study aims to improve the accuracy and consistency of osteoarthritis (OA) diagnosis using an AI-based algorithm. Traditional radiographic assessments are subjective and variable, often leading to delayed or inappropriate treatments. This research will develop an AI model trained on a diverse, annotated dataset of radiographic images to detect and grade OA features such as joint space narrowing, osteophytes, subchondral sclerosis, and cysts. Employing a stratified random sampling technique, the study ensures a representative dataset. The AI’s performance will be validated against traditional assessments, aiming to achieve high accuracy, sensitivity, and specificity. The expected outcome is a robust AI tool that enhances diagnostic reliability, improves patient outcomes, and optimizes radiology workflows. |