New challenges for management of COVID-19 patients: Analysis of MDCT based “Automated pneumonia analysis program”

Abstract

CT pneumonia analysis program is an objective way to determine the disease severity.• The population over the age of 60 and with certain comorbidities such as DM, CHF, and COPD are more prone to severe disease than other patients.• CRP, Neutrophil/Lymphocyte, troponin levels are positive predictors for clinical worsening.

The Diagnostic Utility of Artificial Intelligence-Guided Computed Tomography-Based Severity Scores for Predicting Short-Term Clinical Outcomes in Adults with COVID-19 Pneumonia

“…Sezer et al [28] investigated the prognostic role of an automated MDCT pneumonia analysis program as an early outcome predictor for COVID-19 pneumonia in 96 hospitalized patients. They divided the patients into two clinical groups based on their clinical status: good or bad clinical course.…”

“…TOS values were higher in patients older than 60 years and in patients with comorbidities. The authors concluded that the automated MDCT pneumonia analysis program could serve as a valuable tool for rapidly and reliably assessing the extent of COVID-19 pneumonia [28]; however, no specific thresholds were given for high vs. low TOSs.…”

Artificial intelligence-based quantification of COVID-19 pneumonia burden using chest CT

“…They reported the percentage of lung involvement, chest CT score, and total opacity score to be independent predictors of ICU admission, need for artificial ventilation, and death [46]. Sezer et al [47] also correlated the disease severity depicted by higher total opacity scores in 96 COVID-19 patients, with ICU entries, mortality rates, and patients having a worse clinical course. Gashi et al [48] conducted a retrospective study on 108 COVID-19 patients (group A) and 88 asymptomatic patients performing pre-operative CT scans (group B), to assess the performance of syngo.via software, in detecting COVID-19 abnormalities compared to CO-RADS scores which were calculated by 3 experienced radiologists (inter-reader reliability kappa coefficient 0.87).…”

“…By detecting abnormal CT patterns, the algorithm quantifies the extent of overall abnormalities and the incidence of opacities. As an output, the software application calculates a pneumonia severity score by measuring the percentage of GGO, consolidation, and total opacity score for each lobe and the sum for all the lung parenchyma as an early outcome predictor for COVID-19 pneumonia [45][46][47][48][49][50].…”