Impact of AI-assisted CXR analysis in detecting incidental lung nodules and lung cancers in non-respiratory outpatient clinics

被引:2
作者
Kwak, Se Hyun [1 ]
Kim, Kyeong Yeon [1 ]
Choi, Ji Soo [1 ]
Kim, Min Chul [1 ]
Seol, Chang Hwan [1 ]
Kim, Sung Ryeol [1 ]
Lee, Eun Hye [1 ,2 ]
机构
[1] Yonsei Univ, Yongin Severance Hosp, Coll Med, Dept Internal Med,Div Pulmonol Allergy & Crit Care, Yongin, South Korea
[2] Yonsei Univ, Yongin Severance Hosp, Coll Med, Ctr Digital Hlth, Yongin, Gyeonggi Do, South Korea
基金
新加坡国家研究基金会;
关键词
artificial intelligence; X-rays; lung neoplasms; lung nodule; detection; CHEST RADIOGRAPHY;
D O I
10.3389/fmed.2024.1449537
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Purpose: The use of artificial intelligence (AI) for chest X-ray (CXR) analysis is becoming increasingly prevalent in medical environments. This study aimed to determine whether AI in CXR can unexpectedly detect lung nodule detection and influence patient diagnosis and management in non-respiratory outpatient clinics. Methods: In this retrospective study, patients over 18 years of age, who underwent CXR at Yongin Severance Hospital outpatient clinics between March 2021 and January 2023 and were identified to have lung nodules through AI software, were included. Commercially available AI-based lesion detection software (Lunit INSIGHT CXR) was used to detect lung nodules. Results: Out Of 56,802 radiographic procedures, 40,191 were from non-respiratory departments, with AI detecting lung nodules in 1,754 cases (4.4%). Excluding 139 patients with known lung lesions, 1,615 patients were included in the final analysis. Out of these, 30.7% (495/1,615) underwent respiratory consultation and 31.7% underwent chest CT scans (512/1,615). As a result of the CT scans, 71.5% (366 cases) were found to have true nodules. Among these, the final diagnoses included 36 lung cancers (7.0%, 36/512), 141 lung nodules requiring follow-up (27.5%, 141/512), 114 active pulmonary infections (22.3%, 114/512), and 75 old inflammatory sequelae (14.6%, 75/512). The mean AI nodule score for lung cancer was significantly higher than that for other nodules (56.72 vs. 33.44, p < 0.001). Additionally, active pulmonary infection had a higher consolidation score, and old inflammatory sequelae had the highest fibrosis score, demonstrating differences in the AI analysis among the final diagnosis groups. Conclusion: This study indicates that AI-detected incidental nodule abnormalities on CXR in non-respiratory outpatient clinics result in a substantial number of clinically significant diagnoses, emphasizing AI's role in detecting lung nodules and need for further evaluation and specialist consultation for proper diagnosis and management.
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页数:10
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共 29 条
  • [1] Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening
    Aberle, Denise R.
    Adams, Amanda M.
    Berg, Christine D.
    Black, William C.
    Clapp, Jonathan D.
    Fagerstrom, Richard M.
    Gareen, Ilana F.
    Gatsonis, Constantine
    Marcus, Pamela M.
    Sicks, JoRean D.
    [J]. NEW ENGLAND JOURNAL OF MEDICINE, 2011, 365 (05) : 395 - 409
  • [2] Association of Artificial Intelligence-Aided Chest Radiograph Interpretation With Reader Performance and Efficiency
    Ahn, Jong Seok
    Ebrahimian, Shadi
    McDermott, Shaunagh
    Lee, Sanghyup
    Naccarato, Laura
    Di Capua, John F.
    Wu, Markus Y.
    Zhang, Eric W.
    Muse, Victorine
    Miller, Benjamin
    Sabzalipour, Farid
    Bizzo, Bernardo C.
    Dreyer, Keith J.
    Kaviani, Parisa
    Digumarthy, Subba R.
    Kalra, Mannudeep K.
    [J]. JAMA NETWORK OPEN, 2022, 5 (08) : E2229289
  • [3] Deep Learning Demonstrates Potential for Lung Cancer Detection in Chest Radiography
    Armato, Samuel G., III
    [J]. RADIOLOGY, 2020, 297 (03) : 697 - 698
  • [4] Chest X-ray sensitivity and lung cancer outcomes: a retrospective observational study
    Bradley, Stephen H.
    Bhartia, Bobby Sk
    Callister, Matthew Ej
    Hamilton, William T.
    Hatton, Nathaniel Luke Fielding
    Kennedy, Martyn Pt
    Mounce, Luke Ta
    Shinkins, Bethany
    Wheatstone, Pete
    Neal, Richard D.
    [J]. BRITISH JOURNAL OF GENERAL PRACTICE, 2021, 71 (712) : E862 - E868
  • [5] This Week in the Journal
    de Koning, H. J.
    van der Aalst, C. M.
    de Jong, P. A.
    Scholten, E. T.
    Nackaerts, K.
    Heuvelmans, M. A.
    Lammers, J. -W. J.
    Weenink, C.
    Yousaf-Khan, U.
    Horeweg, N.
    van't Westeinde, S.
    Prokop, M.
    Mali, W. P.
    Hoesein, F. A. A. Mohamed
    van Ooijen, P. M. A.
    Aerts, J. G. J. V.
    den Bakker, M. A.
    Thunnissen, E.
    Verschakelen, J.
    Vliegenthart, R.
    Walter, J. E.
    ten Haaf, K.
    Groen, H. J. M.
    Oudkerk, M.
    [J]. NEW ENGLAND JOURNAL OF MEDICINE, 2020, 382 (06) : 503 - 513
  • [6] The IASLC Lung Cancer Staging Project: Proposals for Revision of the TNM Stage Groupings in the Forthcoming (Eighth) Edition of the TNM Classification for Lung Cancer
    Goldstraw, Peter
    Chansky, Kari
    Crowley, John
    Rami-Porta, Ramon
    Asamura, Hisao
    Eberhardt, Wilfried E. E.
    Nicholson, Andrew G.
    Groome, Patti
    Mitchell, Alan
    Bolejack, Vanessa
    [J]. JOURNAL OF THORACIC ONCOLOGY, 2016, 11 (01) : 39 - 51
  • [7] Deep Learning for Detecting Pneumothorax on Chest Radiographs after Needle Biopsy: Clinical Implementation
    Hong, Wonju
    Hwang, Eui Jin
    Lee, Jong Hyuk
    Park, Jongsoo
    Goo, Jin Mo
    Park, Chang Min
    [J]. RADIOLOGY, 2022, 303 (02)
  • [8] Use of Artificial Intelligence-Based Software as Medical Devices for Chest Radiography: A Position Paper from the Korean Society of Thoracic Radiology
    Hwang, Eui Jin
    Goo, Jin Mo
    Yoon, Soon Ho
    Beck, Kyongmin Sarah
    Seo, Joon Beom
    Choi, Byoung Wook
    Chung, Myung Jin
    Park, Chang Min
    Jin, Kwang Nam
    Lee, Sang Min
    [J]. KOREAN JOURNAL OF RADIOLOGY, 2021, 22 (11) : 1743 - 1748
  • [9] Deep Learning for Chest Radiograph Diagnosis in the Emergency Department
    Hwang, Eui Jin
    Nam, Ju Gang
    Lim, Woo Hyeon
    Park, Sae Jin
    Jeong, Yun Soo
    Kang, Ji Hee
    Hong, Eun Kyoung
    Kim, Taek Min
    Goo, Jin Mo
    Park, Sunggyun
    Kim, Ki Hwan
    Park, Chang Min
    [J]. RADIOLOGY, 2019, 293 (03) : 573 - 580
  • [10] Clinical outcomes and actual consequence of lung nodules incidentally detected on chest radiographs by artificial intelligence
    Hwang, Shin Hye
    Shin, Hyun Joo
    Kim, Eun-Kyung
    Lee, Eun Hye
    Lee, Minwook
    [J]. SCIENTIFIC REPORTS, 2023, 13 (01)