(Early) Economic Evaluation of the AI-based software ‘icolung’ for the detection and prognosis of COVID cases from CT scans

Abstract

Early detection of COVID-19 infection, followed by appropriate patient management, has the potential to reduce costs related to developing severe forms of the disease, as well as spreading of the disease, if left undetected. Our objective was to evaluate the impact of an AI-based chest CT analysis software (icolung, icometrix) for the detection and prognosis of COVID-19 cases in patients receiving a CT scan in a hospital setting in Belgium. We developed a decision analytic model comparing routine practice scenario where patients receiving a CT scan in the hospital are not screened for COVID-19 with a scenario where icolung is used to analyze CT scans for the detection and prognosis of COVID-19 cases. We evaluated the impact of the technology in preventing the further spreading of the infection in the community and in reducing the length of hospitalization of COVID-19 patients. In the base case using a relatively low COVID-19 prevalence of 0.36%, icolung is cost-effective in preventing COVID-19 transmission in the community, costing € 8.221 to prevent one infection. At low prevalence of the disease and low risk of hospitalization, the technology is not cost-effective in reducing the length of hospitalization. However, icolung may be cost-effective in situations with high disease prevalence (>30%) or high risk of hospitalization (>6%) such as patients suffering from chronic oncological diseases and benefiting from recurring thoracic imaging. This model provides initial evidence of cost-effectiveness of AI-based chest CT analysis software and may help to provide guidance regarding further health care research and policy.

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