| CTRI Number |
CTRI/2025/04/084499 [Registered on: 09/04/2025] Trial Registered Prospectively |
| Last Modified On: |
08/04/2025 |
| Post Graduate Thesis |
Yes |
| Type of Trial |
Observational |
|
Type of Study
|
Cross Sectional Study |
| Study Design |
Other |
|
Public Title of Study
|
Using Artificial Intelligence to Help Detect Early and Recent Strokes on Brain Scans Without Contrast: A Study in Patients with Suspected Stroke |
|
Scientific Title of Study
|
AI-Powered Real-Time Detection and Quantitative Analysis of Bilateral acute and sub-acute Infarcts Using Non-Contrast Computed Tomography Imaging: A Deep Learning-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 |
DrAbdul Majith Seeni Mohammed |
| Designation |
Post Grduate |
| Affiliation |
Saveetha medical college and hospital, Saveetha institute of medical and technical sciences |
| Address |
Room no.50, Department of radiology, Saveetha medical college and hospital, Saveetha institute of medical and technical sciences,Saveetha nagar, Thandalam, Chennai, India
Kancheepuram
TAMIL NADU
602105
India |
| Phone |
8122336643 |
| Fax |
|
| Email |
drmajith2297@gmail.com |
|
Details of Contact Person
Scientific Query
|
| Name |
DrMuthiah Pitchandi |
| Designation |
Professor |
| Affiliation |
Saveetha medical college and hospital, Saveetha institute of medical and technical sciences |
| Address |
Room no.50, Department of radiology, Saveetha medical college and hospital, Saveetha institute of medical and technical sciences,Saveetha nagar, Thandalam, Chennai, India
Kancheepuram
TAMIL NADU
602105
India |
| Phone |
919843175404 |
| Fax |
|
| Email |
drmuthiahmd@gmail.com |
|
Details of Contact Person
Public Query
|
| Name |
DrMuthiah Pitchandi |
| Designation |
Professor |
| Affiliation |
Saveetha medical college and hospital, Saveetha institute of medical and technical sciences |
| Address |
Room no.50, Department of radiology, Saveetha medical college and hospital, Saveetha institute of medical and technical sciences,Saveetha nagar, Thandalam, Chennai, India
Kancheepuram
TAMIL NADU
602105
India |
| Phone |
919843175404 |
| Fax |
|
| Email |
drmuthiahmd@gmail.com |
|
|
Source of Monetary or Material Support
|
| Saveetha Medical College Hospital, Saveetha Nagar, Thandalam, Chennai-602105
|
|
|
Primary Sponsor
|
| Name |
Dr.Abdul Majith Seeni Mohammed |
| 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 |
| DrAbdul Majith Seeni Mohammed |
Saveetha Medical College Hospital |
Room no 50,
Department of
Radiology, Saveetha
Medical College
Hospital, Saveetha
Nagar, Thandalam,
Chennai.
Chennai
TAMIL NADU
Kancheepuram
TAMIL NADU |
8122336643
drmajith2297@gmail.com |
|
|
Details of Ethics Committee
|
| No of Ethics Committees= 1 |
| Name of Committee |
Approval Status |
| No of Ethics Committees= 1 Name of Committee Ethics Committee registered with DHR /CDSCO or not Ethics Committee Registration No. Approval Status Date of Approval Approval Document Is IEC? 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: G988||Other disorders of nervous system, |
|
|
Intervention / Comparator Agent
|
|
|
Inclusion Criteria
|
| Age From |
18.00 Year(s) |
| Age To |
90.00 Year(s) |
| Gender |
Both |
| Details |
1)Patients aged 18 years and above.
2)Presenting with acute or subacute neurological symptoms suggestive of stroke (e.g., weakness, slurred speech, visual disturbances, altered sensorium).
3)Undergoing non-contrast CT (NCCT) of the brain as part of initial evaluation.
4)Undergoing MRI with diffusion-weighted imaging (DWI) within 72 hours of the NCCT for diagnostic correlation.
5)Symptom onset within 1 to 72 hours prior to imaging.
6)Informed consent obtained from the patient or legally authorized representative |
|
| ExclusionCriteria |
| Details |
1)Poor-quality NCCT images due to motion artifacts or technical issues.
2)Presence of non-ischemic pathology on CT such as hemorrhage, tumors, trauma, or postoperative changes.
3)History of prior stroke in the same vascular territory.
4)Inability to undergo MRI, including contraindications such as pacemakers, metallic implants, severe claustrophobia, or clinical instability.
5)Patients who have received reperfusion therapy (e.g., thrombolysis or thrombectomy) before the baseline NCCT.
6)Incomplete clinical, laboratory, or follow-up imaging data that prevents accurate analysis.
|
|
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Method of Generating Random Sequence
|
Computer generated randomization |
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Method of Concealment
|
An Open list of random numbers |
|
Blinding/Masking
|
Participant and Investigator Blinded |
|
Primary Outcome
|
| Outcome |
TimePoints |
| Diagnostic accuracy of AI in detecting bilateral MCA infarcts on non-contrast CT, measured by sensitivity, specificity, PPV, NPV, and AUC-ROC. |
Real-time analysis immediately after CT scan |
|
|
Secondary Outcome
|
| Outcome |
TimePoints |
1)Diagnostic accuracy of AI in detecting infarcts across ASPECTS severity categories (0–4, 5–7, 8–10), measured by sensitivity.
2)Time-to-diagnosis comparison between AI & radiologists, measured in minutes from CT scan completion to diagnosis report generation.
3)Clinical impact of AI on decision-making, measured by the proportion of cases with expedited thrombolysis, avoided MRI, & discordant reads requiring MRI confirmation. |
1)Within 1 hour of AI analysis.
2)mmediately after CT scan completion.
3)Within 24–48 hours post-imaging. |
|
|
Target Sample Size
|
Total Sample Size="100"
Sample Size from India="100"
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)
|
19/04/2025 |
| 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
|
This study aims to evaluate the diagnostic accuracy of an AI-driven real-time detection system for acute and sub-acute infarcts using non-contrast CT images. The study will assess the sensitivity, specificity, and clinical utility of AI-based infarct detection compared to expert radiologist interpretation. By leveraging deep learning algorithms, the system provides automated infarct detection, real-time image analysis, and AI-generated reports, assisting in early stroke diagnosis and management. The study will be conducted as a prospective observational diagnostic accuracy study, comparing AI results with the gold standard (radiologist reports and follow-up MRI-DWI findings). The findings will help determine the effectiveness and reliability of AI in infarct detection, particularly in resource-limited settings where radiology expertise may be scarce. |