Tehran University of Medical Sciences
To investigate the performance of an AI-Aided quantification model in predicting the clinical outcomes of hospitalized patients with COVID-19.
A total of 90 patients with covid-19 (men, n=59 [65.6%]; age, 52.9±16.7 years) were recruited. quantification of the total and compromised lung parenchyma was performed by two expert radiologists using volumetric image analysis software and compared against an AI-assisted package consisting of a modified u-net model for segmenting covid-19 lesions and an off-the-shelf u-net model augmented with covid-19 data for segmenting lung volume. the fraction of compromised lung parenchyma (%cl) was calculated. the patients were divided into two groups according to the clinical outcomes: critical (n=45) and noncritical (n=45). all admission data were compared between the two groups.
There was an excellent agreement between the radiologist-obtained and AI-Assisted measurements (intraclass correlation coefficient=0.88, P<0.001). Both the AI-Assisted and radiologist-obtained %Cls were significantly higher in the critical patients (P=0.009 and 0.02, respectively) than in the non-critical patients. In the multivariate logistic regression analysis, an AI-Assisted %CL of ≥35% (odds ratio [OR]=17.0), the oxygen saturation level of <88% (OR=33.6), immunocompromised condition (OR=8.1), and other comorbidities (OR=15.2) independently remained as significant variables in the models. Our proposed model showed a sensitivity of 79.1%, a specificity of 88.6%, and an accuracy of 83.9% in predicting critical outcomes.
AI-Assisted Measurements Are As Robust As Quantitative Radiologist-Obtained Measurements In Predicting Adverse Outcomes.