Ignite Creation Date:
2024-07-17 @ 11:57 AM
Last Modification Date:
2024-10-26 @ 3:34 PM
Study NCT ID:
NCT06501599
Status:
RECRUITING
Last Update Posted:
2024-07-15
First Post:
2024-07-09
Brief Title:
AI-based System for Assessing Suspected Viral Pneumonia Related Lung Changes
Sponsor:
Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
Organization:
Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
Study Overview
Official Title:
Artificial Intelligence Based System for Assessing Suspected Viral Pneumonia Related Lung Changes According to Visual Pulmonary Lesion Grading System CT 0-4 Retrospective Study
Status:
RECRUITING
Status Verified Date:
2024-07
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
The AI-based system designed to process chest computed tomography CT aims to 1 detect the presence of pathologic patterns associated with interstitial changes in pneumonia 2 highlight areas on the images with the probable presence of pathologies 3 provide the physician with the results of image processing including quantitative indicators of suspected viral pneumonia related lung changes according to visual pulmonary lesion grading system CT0-4
The retrospective study aims to demonstrate the clinical validation of the AI-based system Clinical validation measures sensitivity specificity accuracy and area under the ROC curve will be determined to provide evidence about the clinical efficacy of the AI-based system
The hypothesis is that the measures of clinical validation of the AI-based system differ by no more than 8 from those declared by the manufacturer
The retrospective study aims to demonstrate the clinical validation of the AI-based system Clinical validation measures sensitivity specificity accuracy and area under the ROC curve will be determined to provide evidence about the clinical efficacy of the AI-based system
The hypothesis is that the measures of clinical validation of the AI-based system differ by no more than 8 from those declared by the manufacturer
Detailed Description:
The AI-based system designed to process chest CT aims to 1 detect the presence of pathologic patterns associated with interstitial changes in pneumonia 2 highlight areas on the images with the probable presence of pathologies 3 provide the physician with the results of image processing including quantitative indicators of suspected viral pneumonia related lung changes according to visual pulmonary lesion grading system CT0-4
This retrospective clinical study will provide the clinical validation of the AI-based system to analyze chest CT images and identify pathological patterns associated with interstitial changes in pneumonia Clinical validation measures sensitivity specificity accuracy and area under the ROC curve will be determined and compared with values declared by the manufacturer to provide evidence about the clinical efficacy of the AI-based system
The first stage of clinical validation is the collection of a verified labeled dataset For this purpose the dataset is collected labeled and verified by a research group The verified dataset should include chest CT images without infiltrative and interstitial lung changes characteristic of viral pneumonia including COVID-19-associated CT-0 and chest CT images of all degrees of lung involvement CT-1 25 CT-2 25-50 CT-3 50-75 CT-4 75 1 Forming the verified dataset will allow reliable conclusions to be drawn upon completion of the clinical validation The verified dataset must include a sufficient volume of chest CT images The verified dataset must be de-identified to ensure the safety of patient personal data
The second stage of the clinical validation is assessing AI-based system performance by experts For that purpose the AI software is analyzed to identify radiological signs of viral pneumonia Then an examination is made of the correctness of the quantitative assessment of lung damage associated with interstitial changes in pneumonia The evaluation of both the ability to correctly identify signs of lung damage and to quantify the identified changes is carried out on the same verified dataset
The third stage of clinical validation is the calculation of clinical efficacy metrics accuracy sensitivity specificity area under the ROC-curve AUROC of the AI-based system by testing it on a verified data set Testing of the hypothesis to verify the main diagnostic characteristics sensitivity and specificity declared by the manufacturer is planned by constructing a two-sided 95 confidence interval CI which should not differ by more than 8 from the declared values of 95 and 97 respectively Those the lower limit of the 95 CI for sensitivity should not cross the 87 threshold and the lower limit of the 95 CI for specificity should not cross the 89 threshold
All stages of the clinical trial must be under the control of the Principal Investigator
Randomization of images is not provided in this clinical study because All CT images will be assessed by the research group and AI software Also this design does not involve blinding or masking of the research team The evaluation of CT images by experts and the software is carried out independently ie the results of each partys assessment are not known to the other party in advance
This retrospective clinical study will provide the clinical validation of the AI-based system to analyze chest CT images and identify pathological patterns associated with interstitial changes in pneumonia Clinical validation measures sensitivity specificity accuracy and area under the ROC curve will be determined and compared with values declared by the manufacturer to provide evidence about the clinical efficacy of the AI-based system
The first stage of clinical validation is the collection of a verified labeled dataset For this purpose the dataset is collected labeled and verified by a research group The verified dataset should include chest CT images without infiltrative and interstitial lung changes characteristic of viral pneumonia including COVID-19-associated CT-0 and chest CT images of all degrees of lung involvement CT-1 25 CT-2 25-50 CT-3 50-75 CT-4 75 1 Forming the verified dataset will allow reliable conclusions to be drawn upon completion of the clinical validation The verified dataset must include a sufficient volume of chest CT images The verified dataset must be de-identified to ensure the safety of patient personal data
The second stage of the clinical validation is assessing AI-based system performance by experts For that purpose the AI software is analyzed to identify radiological signs of viral pneumonia Then an examination is made of the correctness of the quantitative assessment of lung damage associated with interstitial changes in pneumonia The evaluation of both the ability to correctly identify signs of lung damage and to quantify the identified changes is carried out on the same verified dataset
The third stage of clinical validation is the calculation of clinical efficacy metrics accuracy sensitivity specificity area under the ROC-curve AUROC of the AI-based system by testing it on a verified data set Testing of the hypothesis to verify the main diagnostic characteristics sensitivity and specificity declared by the manufacturer is planned by constructing a two-sided 95 confidence interval CI which should not differ by more than 8 from the declared values of 95 and 97 respectively Those the lower limit of the 95 CI for sensitivity should not cross the 87 threshold and the lower limit of the 95 CI for specificity should not cross the 89 threshold
All stages of the clinical trial must be under the control of the Principal Investigator
Randomization of images is not provided in this clinical study because All CT images will be assessed by the research group and AI software Also this design does not involve blinding or masking of the research team The evaluation of CT images by experts and the software is carried out independently ie the results of each partys assessment are not known to the other party in advance
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None