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An Artificial Intelligence-Based Chest X-ray Model on Human Nodule Detection Accuracy From a Multicenter Study
Journal article   Open access   Peer reviewed

An Artificial Intelligence-Based Chest X-ray Model on Human Nodule Detection Accuracy From a Multicenter Study

Fatemeh Homayounieh, Subba Digumarthy, Shadi Ebrahimian, Johannes Rueckel, Boj Friedrich Hoppe, Bastian Oliver Sabel, Sailesh Conjeti, Karsten Ridder, Markus Sistermanns, Lei Wang, …
JAMA network open, Vol.4(12), pp.e2141096-e2141096
12/01/2021
DOI: 10.1001/jamanetworkopen.2021.41096
PMCID: PMC8717119
PMID: 34964851
url
https://doi.org/10.1001/jamanetworkopen.2021.41096View
Published (Version of record) Open Access

Abstract

Most early lung cancers present as pulmonary nodules on imaging, but these can be easily missed on chest radiographs. To assess if a novel artificial intelligence (AI) algorithm can help detect pulmonary nodules on radiographs at different levels of detection difficulty. This 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. 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. Each 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). Images 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%). In 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.
 Adult Algorithms Artificial Intelligence Female Germany Humans Lung Neoplasms - diagnostic imaging Male Middle Aged Multiple Pulmonary Nodules - diagnostic imaging Radiographic Image Interpretation, Computer-Assisted Radiography, Thoracic Sensitivity and Specificity Solitary Pulmonary Nodule - diagnostic imaging

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