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Development and Validation of Deep Learning-based Automatic Detection Algorithm for Malignant Pulmonary Nodules on

Ju Gang Nam1, Sunggyun Park1, Eui Jin Hwang1

  • 1From the Department of Radiology and Institute of Radiation Medicine, Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.G.N., E.J.H., J.M.G., C.M.P.); Lunit Incorporated, Seoul, Republic of Korea (S.P.); Department of Radiology, Armed Forces Seoul Hospital, Seoul, Republic of Korea (J.H.L.); Department of Radiology, Seoul National University Boramae Medical Center, Seoul, Republic of Korea (K.N.J.); Department of Radiology, National Cancer Center, Goyang, Republic of Korea (K.Y.L.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (T.H.V., J.H.S.); and Department of Industrial & Information Systems Engineering, Seoul National University of Science and Technology, Seoul, Republic of Korea (S.H.).

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|September 26, 2018
PubMed
Summary

A new deep learning algorithm for detecting malignant pulmonary nodules on chest radiographs outperformed physicians. This artificial intelligence tool also improved physician performance when used as a second reader.

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Malignant pulmonary nodules are a critical finding on chest radiographs.
  • Accurate and timely detection of these nodules is essential for patient outcomes.
  • Current detection methods rely heavily on human interpretation, which can be subject to variability.

Purpose of the Study:

  • To develop and validate a deep learning-based automatic detection algorithm (DLAD) for malignant pulmonary nodules.
  • To compare the DLAD's performance against physicians, including thoracic radiologists.
  • To assess the DLAD's utility as an assistive tool for physicians.

Main Methods:

  • A convolutional neural network was trained on 43,292 chest radiographs from 34,676 patients.
  • The DLAD was validated using internal and four external datasets from multiple hospitals.
  • Performance was evaluated using area under the receiver operating characteristic curve (AUROC) and JAFROC figure of merit (FOM).
  • An observer study compared DLAD, physicians, and physicians assisted by DLAD.

Main Results:

  • DLAD achieved high performance in radiograph classification (AUROC: 0.92-0.99) and nodule detection (JAFROC FOM: 0.831-0.924) across validation datasets.
  • DLAD outperformed 17 of 18 physicians in AUROC and 15 of 18 in JAFROC FOM.
  • Physician nodule detection performance improved significantly when assisted by DLAD (mean JAFROC FOM improvement: 0.043).

Conclusions:

  • The developed deep learning-based automatic detection algorithm demonstrates superior performance in classifying radiographs and detecting malignant pulmonary nodules compared to physicians.
  • The DLAD effectively enhances physician performance, acting as a valuable second reader in the interpretation of chest radiographs.
  • This technology holds significant potential for improving the accuracy and efficiency of lung nodule detection in clinical practice.