Artificial Intelligence Algorithm Detecting Lung Infection in Supine Chest Radiographs of Critically Ill Patients With a Diagnostic Accuracy Similar to Board-Certified Radiologists
Authors: Rueckel Johannes , Kunz Wolfgang G. , Hoppe Boj F. , Patzig Maximilian , Notohamiprodjo Mike , Meinel Felix G. , Cyran Clemens C. , Ingrisch Michael , Ricke Jens , Sabel Bastian O. 
Published: May 2020

Objectives:
Interpretation of lung opacities in ICU supine chest radiographs remains challenging. We evaluated a prototype artificial intelligence algorithm to classify basal lung opacities according to underlying pathologies.

Design:
Retrospective study. The deep neural network was trained on two publicly available datasets including 297,541 images of 86,876 patients.

Patients:
One hundred sixty-six patients received both supine chest radiograph and CT scans (reference standard) within 90 minutes without any intervention in between.

Measurements and Main Results:
Algorithm accuracy was referenced to board-certified radiologists who evaluated supine chest radiographs according to side-separate reading scores for pneumonia and effusion (0 = absent, 1 = possible, and 2 = highly suspected). Radiologists were blinded to the supine chest radiograph findings during CT interpretation. Performances of radiologists and the artificial intelligence algorithm were quantified by receiver-operating characteristic curve analysis. Diagnostic metrics (sensitivity, specificity, positive predictive value, negative predictive value, and accuracy) were calculated based on different receiver-operating characteristic operating points. Regarding pneumonia detection, radiologists achieved a maximum diagnostic accuracy of up to 0.87 (95% CI, 0.78–0.93) when considering only the supine chest radiograph reading score 2 as positive for pneumonia. Radiologist’s maximum sensitivity up to 0.87 (95% CI, 0.76–0.94) was achieved by additionally rating the supine chest radiograph reading score 1 as positive for pneumonia and taking previous examinations into account. Radiologic assessment essentially achieved nonsignificantly higher results compared with the artificial intelligence algorithm: artificial intelligence-area under the receiver-operating characteristic curve of 0.737 (0.659–0.815) versus radiologist’s area under the receiver-operating characteristic curve of 0.779 (0.723–0.836), diagnostic metrics of receiver-operating characteristic operating points did not significantly differ. Regarding the detection of pleural effusions, there was no significant performance difference between radiologist’s and artificial intelligence algorithm: artificial intelligence-area under the receiver-operating characteristic curve of 0.740 (0.662–0.817) versus radiologist’s area under the receiver-operating characteristic curve of 0.698 (0.646–0.749) with similar diagnostic metrics for receiver-operating characteristic operating points.

Conclusions:
Considering the minor level of performance differences between the algorithm and radiologists, we regard artificial intelligence as a promising clinical decision support tool for supine chest radiograph examinations in the clinical routine with high potential to reduce the number of missed findings in an artificial intelligence–assisted reading setting.