AI-assisted Analysis to Facilitate Detection of Humeral Lesions on Chest Radiographs.

Journal: Radiology. Artificial intelligence
PMID:

Abstract

Purpose To develop an artificial intelligence (AI) system for humeral tumor detection on chest radiographs (CRs) and evaluate the impact on reader performance. Materials and Methods In this retrospective study, 14 709 CRs (January 2000 to December 2021) were collected from 13 468 patients, including CT-proven normal ( = 13 116) and humeral tumor ( = 1593) cases. The data were divided into training and test groups. A novel training method called (FPAR) was introduced to enhance the diagnostic performance by focusing on the humeral region. The AI program and 10 radiologists were assessed using holdout test set 1, wherein the radiologists were tested twice (with and without AI test results). The performance of the AI system was evaluated using holdout test set 2, comprising 10 497 normal images. Receiver operating characteristic analyses were conducted for evaluating model performance. Results FPAR application in the AI program improved its performance compared with a conventional model based on the area under the receiver operating characteristic curve (0.87 vs 0.82, = .04). The proposed AI system also demonstrated improved tumor localization accuracy (80% vs 57%, < .001). In holdout test set 2, the proposed AI system exhibited a false-positive rate of 2%. AI assistance improved the radiologists' sensitivity, specificity, and accuracy by 8.9%, 1.2%, and 3.5%, respectively ( < .05 for all). Conclusion The proposed AI tool incorporating FPAR improved humeral tumor detection on CRs and reduced false-positive results in tumor visualization. It may serve as a supportive diagnostic tool to alert radiologists about humeral abnormalities. Artificial Intelligence, Conventional Radiography, Humerus, Machine Learning, Shoulder, Tumor © RSNA, 2024.

Authors

  • Harim Kim
    Department of Orthodontics, Institute of Craniofacial Deformity, Yonsei University College of Dentistry, Seoul, Republic of Korea.
  • Kyungsu Kim
    Medical AI Research Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea. kskim.doc@gmail.com.
  • Seong Je Oh
    Medical AI Research Center, Samsung Medical Center, Seoul, South Korea.
  • Sungjoo Lee
    From the Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-Gu, Seoul 06351, South Korea (H.K., J.H.W., J.H.K., Y.K.C., M.J.C.); Medical AI Research Center, Samsung Medical Center, Seoul, South Korea (Kyungsu Kim, M.J.C.); Department of Data Convergence and Future Medicine, Sungkyunkwan University School of Medicine, Seoul, South Korea (Kyungsu Kim, Kyunga Kim, M.J.C.); and Department of Health Sciences and Technology (S.J.O.) and Department of Digital Health (S.L., Kyunga Kim), Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, South Korea.
  • Jung Han Woo
    From the Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-Gu, Seoul 06351, South Korea (H.K., J.H.W., J.H.K., Y.K.C., M.J.C.); Medical AI Research Center, Samsung Medical Center, Seoul, South Korea (Kyungsu Kim, M.J.C.); Department of Data Convergence and Future Medicine, Sungkyunkwan University School of Medicine, Seoul, South Korea (Kyungsu Kim, Kyunga Kim, M.J.C.); and Department of Health Sciences and Technology (S.J.O.) and Department of Digital Health (S.L., Kyunga Kim), Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, South Korea.
  • Jong Hee Kim
    From the Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-Gu, Seoul 06351, South Korea (H.K., J.H.W., J.H.K., Y.K.C., M.J.C.); Medical AI Research Center, Samsung Medical Center, Seoul, South Korea (Kyungsu Kim, M.J.C.); Department of Data Convergence and Future Medicine, Sungkyunkwan University School of Medicine, Seoul, South Korea (Kyungsu Kim, Kyunga Kim, M.J.C.); and Department of Health Sciences and Technology (S.J.O.) and Department of Digital Health (S.L., Kyunga Kim), Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, South Korea.
  • Yoon Ki Cha
    Department of Radiology, Dongguk University Ilsan Hospital, Goyang, Republic of Korea.
  • Kyunga Kim
    Statistics and Data Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea.
  • Myung Jin Chung
    From the Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea (Y.S., K.H., B.W.C.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (M.J.C.); Department of Radiology, University Medical Center Freiburg, Freiburg, Germany (E.K.); Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Mass (S. Yune, M.K., S.D.); and Samsung Electronics, Suwon, Republic of Korea (H.K., S. Yang, D.J.L.).