Condition: Pneumonia, Viral · COVID-19 Pneumonia · Sponsor: Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
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.
This description comes directly from the study's public registry record.
Victoria Zinchenko · +7 (495) 276-04-36 · ZinchenkoVV1@zdrav.mos.ru
Anton Vladzymyrskyy · VladzimirskijAV@zdrav.mos.ru
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| Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department | Moscow, Russia | Recruiting |
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Source record: clinicaltrials.gov/study/NCT06501599