[en] Early diagnosis of COVID-19 is required to provide the best treatment to our patients, to prevent the epidemic from spreading in the community, and to reduce costs associated with the aggravation of the disease. We developed a decision tree model to evaluate the impact of using an artificial intelligence-based chest computed tomography (CT) analysis software (icolung, icometrix) to analyze CT scans for the detection and prognosis of COVID-19 cases. The model compared routine practice where patients receiving a chest CT scan were not screened for COVID-19, with a scenario where icolung was introduced to enable COVID-19 diagnosis. The primary outcome was to evaluate the impact of icolung on the transmission of COVID-19 infection, and the secondary outcome was the in-hospital length of stay. Using EUR 20000 as a willingness-to-pay threshold, icolung is cost-effective in reducing the risk of transmission, with a low prevalence of COVID-19 infections. Concerning the hospitalization cost, icolung is cost-effective at a higher value of COVID-19 prevalence and risk of hospitalization. This model provides a framework for the evaluation of AI-based tools for the early detection of COVID-19 cases. It allows for making decisions regarding their implementation in routine practice, considering both costs and effects.
Disciplines :
Cardiovascular & respiratory systems Radiology, nuclear medicine & imaging Strategy & innovation Special economic topics (health, labor, transportation...)
Author, co-author :
Esposito, Giovanni
Ernst, Benoit ; Université de Liège - ULiège > Département des sciences cliniques > Pneumologie - Allergologie
WINANDY, Marie-Laurence ; Centre Hospitalier Universitaire de Liège - CHU > Service de pneumologie - allergologie
Chatterjee, Avishek
Van Eyndhoven, Simon
Praet, Jelle
Smeets, Dirk
MEUNIER, Paul ; Centre Hospitalier Universitaire de Liège - CHU > > Service médical de radiodiagnostic
LOUIS, Renaud ; Centre Hospitalier Universitaire de Liège - CHU > > Service de pneumologie - allergologie
KOLH, Philippe ; Université de Liège - ULiège > GIGA ; Université de Liège - ULiège > Département des sciences biomédicales et précliniques > Biochimie et physiologie générales, humaines et pathologiques ; Centre Hospitalier Universitaire de Liège - CHU > > Service des informations médico économiques (SIME)
Guiot, Julien ; Centre Hospitalier Universitaire de Liège - CHU > > Service de pneumologie - allergologie
Language :
English
Title :
AI-Based Chest CT Analysis for Rapid COVID-19 Diagnosis and Prognosis: A Practical Tool to Flag High-Risk Patients and Lower Healthcare Costs
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