IMPORTANCEMost early lung cancers present as pulmonary nodules on imaging, but these can be easily missed on chest radiographs. OBJECTIVE To assess if a novel artificial intelligence (AI) algorithm can help detect pulmonary nodules on radiographs at different levels of detection difficulty. DESIGN, SETTING, AND PARTICIPANTSThis diagnostic study included 100 posteroanterior chest radiograph images taken between 2000 and 2010 of adult patients from an ambulatory health care center in Germany and a lung image database in the US. Included images were selected to represent nodules with different levels of detection difficulties (from easy to difficult), and comprised both normal and nonnormal control.EXPOSURES All images were processed with a novel AI algorithm, the AI Rad Companion Chest X-ray. Two thoracic radiologists established the ground truth and 9 test radiologists from Germany and the US independently reviewed all images in 2 sessions (unaided and AI-aided mode) with at least a 1-month washout period. MAIN OUTCOMES AND MEASURESEach test radiologist recorded the presence of 5 findings (pulmonary nodules, atelectasis, consolidation, pneumothorax, and pleural effusion) and their level of confidence for detecting the individual finding on a scale of 1 to 10 (1 representing lowest confidence; 10, highest confidence). The analyzed metrics for nodules included sensitivity, specificity, accuracy, and receiver operating characteristics curve area under the curve (AUC). RESULTSImages from 100 patients were included, with a mean (SD) age of 55 (20) years and including 64 men and 36 women. Mean detection accuracy across the 9 radiologists improved by 6.4% (95% CI, 2.3% to 10.6%) with AI-aided interpretation compared with unaided interpretation. Partial AUCs within the effective interval range of 0 to 0.2 false positive rate improved by 5.6% (95% CI, −1.4% to 12.0%) with AI-aided interpretation. Junior radiologists saw greater improvement in sensitivity for nodule detection with AI-aided interpretation as compared with their senior counterparts (12%; 95% CI, 4% to 19% vs 9%; 95% CI, 1% to 17%) while senior radiologists experienced similar improvement in specificity (4%; 95% CI, −2% to 9%) as compared with junior radiologists (4%; 95% CI, −3% to 5%). CONCLUSIONS AND RELEVANCEIn this diagnostic study, an AI algorithm was associated with improved detection of pulmonary nodules on chest radiographs compared with unaided interpretation for different levels of detection difficulty and for readers with different experience.

Purpose The technically caused delay between low-energy (LE) and high-energy (HE) acquisitions allows motion artifacts in contrast-enhanced dual-energy mammography (CEDEM). In this study the effect of motion correction by nonrigid registration on image quality of the recombined images was investigated. Materials and Methods Retrospectively for 354 recombined CEDEM images an additional recombined image was processed from the raw data of LE and HE images using the motion correction algorithm. Five radiologists with many years of experience in breast cancer diagnostic imaging compared side-by-side one conventional processed CEDEM image with the corresponding image processed by the motion correction algorithm. Every pair of images was compared based on six criteria: General image quality (1), sharpness of skin contour (2), reduction of image artifacts (3), sharpness of lesion contour (4), contrast of lesion (5), visibility of lymph nodes (6). These criteria were rated on a Likert scale (improvement: + 1, + 2; deterioration: –1, –2). Results The mean ratings concerning criteria 1–5 showed a superiority of the recombined images processed by the motion correction algorithm. For example, the mean rating of general image quality was 0.86 (95 % CI: 0.78; 0.93). Only the mean rating concerning criterion 6 showed an inferiority of the recombined images processed by the motion correction algorithm (–0.29 (–0.46; –0.13)). Conclusion The usage of nonrigid registration for motion correction significantly improves the general image quality and the quality of subordinate criteria on the recombined CEDEM images at the expense of somewhat reduced lymph node visibility in some cases. Key Points:  Citation Format