Abstract
ObjectivesWe aimed to evaluate a commercial artificial intelligence (AI) solution on a multicenter cohort of chest radiographs and to compare physicians' ability to detect and localize referable thoracic abnormalities with and without AI assistance.MethodsIn this retrospective diagnostic cohort study, we investigated 6,006 consecutive patients who underwent both chest radiography and CT. We evaluated a commercially available AI solution intended to facilitate the detection of three chest abnormalities (nodule/masses, consolidation, and pneumothorax) against a reference standard to measure its diagnostic performance. Moreover, twelve physicians, including thoracic radiologists, board-certified radiologists, radiology residents, and pulmonologists, assessed a dataset of 230 randomly sampled chest radiographic images. The images were reviewed twice per physician, with and without AI, with a 4-week washout period. We measured the impact of AI assistance on observer's AUC, sensitivity, specificity, and the area under the alternative free-response ROC (AUAFROC).ResultsIn the entire set (n = 6,006), the AI solution showed average sensitivity, specificity, and AUC of 0.885, 0.723, and 0.867, respectively. In the test dataset (n = 230), the average AUC and AUAFROC across observers significantly increased with AI assistance (from 0.861 to 0.886; p = 0.003 and from 0.797 to 0.822; p = 0.003, respectively).ConclusionsThe diagnostic performance of the AI solution was found to be acceptable for the images from respiratory outpatient clinics. The diagnostic performance of physicians marginally improved with the use of AI solutions. Further evaluation of AI assistance for chest radiographs using a prospective design is required to prove the efficacy of AI assistance.Key Points• AI assistance for chest radiographs marginally improved physicians’ performance in detecting and localizing referable thoracic abnormalities on chest radiographs.• The detection or localization of referable thoracic abnormalities by pulmonologists and radiology residents improved with the use of AI assistance.
Highlights
Chest radiography is the most commonly used radiologic examination to screen chest diseases and monitor patients with thoracic abnormalities, including lung cancer and pneumonia [1,2,3,4]
We evaluated a commercial artificial intelligence (AI) solution on a consecutive diagnostic cohort dataset collected from multiple respiratory outpatient clinics and compared physicians’ ability to detect and localize referable thoracic abnormalities with and without AI assistance
We investigated 26,988 consecutive patients who visited respiratory outpatient clinics at three participating institutions in 2018, and their chest radiography was retrospectively analyzed
Summary
Chest radiography is the most commonly used radiologic examination to screen chest diseases and monitor patients with thoracic abnormalities, including lung cancer and pneumonia [1,2,3,4]. Among the various applications of artificial intelligence (AI) in diagnostic imaging, commercial AI solutions for chest radiographs designed using deep learning (DL) algorithms have gathered attention and shown excellent performance in detecting malignant pulmonary nodules, tuberculosis, and various abnormalities in experimental datasets [9,10,11]. In a study by Hwang et al [14], the application of the DL algorithm in emergency cohort datasets for the identification of clinically relevant abnormalities on chest radiographs resulted in an AUC, sensitivity, and specificity of 0.95, 0.816–0.887, and 0.692–0.903, respectively. We evaluated a commercial AI solution on a consecutive diagnostic cohort dataset collected from multiple respiratory outpatient clinics and compared physicians’ ability to detect and localize referable thoracic abnormalities with and without AI assistance
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