PDF of Trial CTRI Website URL - http://ctri.nic.in

Clinical Trial Details (PDF Generation Date :- Thu, 30 Sep 2021 07:25:48 GMT)

CTRI Number CTRI/2020/09/028156 [Registered on: 30/09/2020] - Trial Registered Prospectively Last Modified On 28/09/2020 Post Graduate Thesis No Type of Trial Observational Type of Study Cross Sectional Study Study Design Other Public Title of Study Comparative Study of Artificial Intelligence and Radiologists in Assessing Severity of COVID19 Patient Images Scientific Title of Clinical Validation of LungIQ for Severity Scoring for COVID19 using Chest Computed Tomography Study Imaging Secondary IDs if Any Secondary ID Identifier NIL NIL Details of Principal Details of Principal Investigator Investigator or overall Name Dr Amit Kumar Sahu Trial Coordinator (multi-center study) Designation Associate Consultant Affiliation Max Healthcare Address Department of Radiology, Max Super Speciality Hospital, Saket, 1, Press Enclave Road, Saket New Delhi South DELHI 110017 Phone Fax Email [email protected] Details Contact Details Contact Person (Scientific Query) Person (Scientific Name Dr Amit Kumar Sahu Query) Designation Associate Consultant Affiliation Max Healthcare Address Department of Radiology, Max Super Speciality Hospital, Saket, 1, Press Enclave Road, Saket New Delhi South DELHI 110017 India Phone Fax Email [email protected] Details Contact Details Contact Person (Public Query) Person (Public Query) Name Dr Amit Kumar Sahu Designation Associate Consultant Affiliation Max Healthcare Address Department of Radiology, Max Super Speciality Hospital, Saket, 1, Press Enclave Road, Saket New Delhi South DELHI 110017 India

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Phone Fax Email [email protected] Source of Monetary or Source of Monetary or Material Support Material Support > Predible Health Private Limited, 2nd Floor, IKP-Eden, #16, Bhuvanappa Layout, Taverekere Main Road, , 560029, Primary Sponsor Primary Sponsor Details Name Predible Health Private Limited Address IKP Eden, Bhuvanappa Layout, Tavarekere Main Road, Adugodi, Benglauru Type of Sponsor Other [Medical Device Industry - Indian] Details of Secondary Name Address Sponsor NIL NIL Countries of List of Countries Recruitment India Sites of Study Name of Principal Name of Site Site Address Phone/Fax/Email Investigator Dr Amit Sahu Max Healthcare Department of 7428304988 Radiology, Max Super Speciality Hospital, [email protected] Saket, 1, Press Enclave Road, Saket New Delhi South DELHI Details of Ethics Name of Committee Approval Status Date of Approval Is Independent Ethics Committee Committee? Max Healthcare Ethics Approved 30/08/2020 No Committee Regulatory Clearance Status Date Status from DCGI Not Applicable No Date Specified Health Condition / Health Type Condition Problems Studied Patients Coronavirus as the cause of diseases classified elsewhere Intervention / Type Name Details Comparator Agent Comparator Agent Manual Radiologists Report Standard of care routine radiology reports without any software aid Intervention LungIQ Artificial Intelligence Based Software Tool for COVID19 Severity Scoring from CT imaging Inclusion Criteria Inclusion Criteria Age From 18.00 Year(s) Age To 99.00 Year(s) Gender Both Details 1. Confirmed diagnosis of COVID-19, confirmed by RT-PCR
2. Non-contrast CT scan with slice thickness < 5mm
3. Both lungs must be fully visible within the field of view Exclusion Criteria Exclusion Criteria Details 1. Individuals with CT scans that have motion artefacts and/or poor

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image quality 2. Apices cannot be cropped Method of Generating Computer generated randomization Random Sequence Method of Case Record Numbers Concealment Blinding/Masking Investigator Blinded Primary Outcome Outcome Timepoints On 25 point Severity Score, Within 1 class End of Study accuracy of LungIQ with Radiologists Assessment more than 80% Secondary Outcome Outcome Timepoints Statistical correlation of CT Severity Score with End of Study clinical severity (p) less than 0.05 Target Sample Size Total Sample Size=500 Sample Size from India=500 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 04/10/2020 Enrollment (India) Date of First No Date Specified Enrollment (Global) Estimated Duration of Years=0 Trial Months=6 Days=0 Recruitment Status of Not Applicable Trial (Global) Recruitment Status of Not Yet Recruiting Trial (India) Publication Details None yet Brief Summary In this study, we propose to study the performance of a deep learning-based severity assessment tool (Predible LungIQ for COVID19) trained on Thoracic CTs studies with proven COVID-19 status.

The algorithm, specifically, has been trained to carry out the following tasks:

1. Recognize the lungs and lobes from the CT scans and estimate volume

2. Recognize ground-glass opacities and consolidation volumes in each lobe

3. Estimate severity in each zone using volumes from step (1) and (2)

The severity score is then calculated using the severity scoring methodology described in Pan et al which assigns a score between 0-25 based on 0 to 5, with 0 indicating no involvement; 1 for less than 5% involvement; 2 for 5%–25% involvement; 3 for

26%–49% involvement; 4 for 50%–75% involvement; and 5 for more than 75% involvement. The total CT score was the sum of the individual lobar scores and ranged from 0 (no involvement) to 25 (maximum involvement).

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The study aims to compare the severity score estimated by LungIQ with the opinion of radiologists in terms of concordance and time taken. Each chest CT will be independently read by one radiologist who will mark the following attributes for each study: 1) Severity score from 0-25 based on Pan et al, and 2) Time taken to carry out above task. The radiologist who would be carrying out the assessments will be blinded for the radiological and clinical findings of the CT Images. Once all the data has been processed, all the above fields will be tabulated on a spreadsheet for analysis.

CT severity score and the volumetric quantification of the lung parenchymal pathology will also be correlated with the clinical outcome of individual patients, wherever available; based on the number of days of stay in hospital before discharge or death.

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