138 71

Cited 0 times in

Clinical outcomes and actual consequence of lung nodules incidentally detected on chest radiographs by artificial intelligence

 Shin Hye Hwang  ;  Hyun Joo Shin  ;  Eun-Kyung Kim  ;  Eun Hye Lee  ;  Minwook Lee 
 SCIENTIFIC REPORTS, Vol.13(1) : 19732, 2023-11 
Journal Title
Issue Date
Adolescent ; Artificial Intelligence ; Humans ; Inflammation ; Lung ; Lung Neoplasms* / diagnostic imaging ; Neoplasms* ; Radiography, Thoracic ; Retrospective Studies
This study evaluated how often clinically significant lung nodules were detected unexpectedly on chest radiographs (CXR) by artificial intelligence (AI)-based detection software, and whether co-existing findings can aid in differential diagnosis of lung nodules. Patients (> 18 years old) with AI-detected lung nodules at their first visit from March 2021 to February 2022, except for those in the pulmonology or thoracic surgery departments, were retrospectively included. Three radiologists categorized nodules into malignancy, active inflammation, post-inflammatory sequelae, or "other" groups. Characteristics of the nodule and abnormality scores of co-existing lung lesions were compared. Approximately 1% of patients (152/14,563) had unexpected lung nodules. Among 73 patients with follow-up exams, 69.9% had true positive nodules. Increased abnormality scores for nodules were significantly associated with malignancy (odds ratio [OR] 1.076, P = 0.001). Increased abnormality scores for consolidation (OR 1.033, P = 0.040) and pleural effusion (OR 1.025, P = 0.041) were significantly correlated with active inflammation-type nodules. Abnormality scores for fibrosis (OR 1.036, P = 0.013) and nodules (OR 0.940, P = 0.001) were significantly associated with post-inflammatory sequelae categorization. AI-based lesion-detection software of CXRs in daily practice can help identify clinically significant incidental lung nodules, and referring accompanying lung lesions may help classify the nodule.
Files in This Item:
T202306632.pdf Download
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Eun-Kyung(김은경) ORCID logo https://orcid.org/0000-0002-3368-5013
Shin, Hyun Joo(신현주) ORCID logo https://orcid.org/0000-0002-7462-2609
Lee, Minwook(이민욱) ORCID logo https://orcid.org/0000-0003-2822-0489
Lee, Eun Hye(이은혜) ORCID logo https://orcid.org/0000-0003-2570-3442
Hwang, Shin Hye(황신혜)
사서에게 알리기


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.