Computer-aided diagnosis with a convolutional neural network algorithm for automated detection of urinary tract stones on plain X-ray.

Journal: BMC urology
PMID: 34353306

Abstract

BACKGROUND: Recent increased use of medical images induces further burden of their interpretation for physicians. A plain X-ray is a low-cost examination that has low-dose radiation exposure and high availability, although diagnosing urolithiasis using this method is not always easy. Since the advent of a convolutional neural network via deep learning in the 2000s, computer-aided diagnosis (CAD) has had a great impact on automatic image analysis in the urological field. The objective of our study was to develop a CAD system with deep learning architecture to detect urinary tract stones on a plain X-ray and to evaluate the model's accuracy.

Authors

  • Masaki Kobayashi
    Mathematical Science Center, University of Yamanashi, Takeda 4-3-11, Kofu, Yamanashi 400-8511, Japan.
  • Junichiro Ishioka
    Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan.
  • Yoh Matsuoka
    Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan. yoh-m.uro@tmd.ac.jp.
  • Yuichi Fukuda
    Department of Urology, Tsuchiura Kyodo General Hospital, Tsuchiura, Japan.
  • Yusuke Kohno
    Department of Urology, Tsuchiura Kyodo General Hospital, Tsuchiura, Japan.
  • Keizo Kawano
    Department of Urology, Tsuchiura Kyodo General Hospital, Tsuchiura, Japan.
  • Shinji Morimoto
    Department of Urology, Tsuchiura Kyodo General Hospital, Tsuchiura, Japan.
  • Rie Muta
    Department of Urology, JA Toride Medical Center, Toride, Japan.
  • Motohiro Fujiwara
    Department of Urology, JA Toride Medical Center, Toride, Japan.
  • Naoko Kawamura
    Department of Urology, JA Toride Medical Center, Toride, Japan.
  • Tetsuo Okuno
    Department of Urology, JA Toride Medical Center, Toride, Japan.
  • Soichiro Yoshida
    Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan.
  • Minato Yokoyama
    Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan.
  • Rumi Suda
    Department of Information and Communications Engineering, Tokyo Institute of Technology, Tokyo, Japan.
  • Ryota Saiki
    Department of Information and Communications Engineering, Tokyo Institute of Technology, Tokyo, Japan.
  • Kenji Suzuki
    Department of General Thoracic Surgery, Juntendo University School of Medicine, Tokyo, Japan.
  • Itsuo Kumazawa
    Laboratory for Future, Interdisciplinary Research of Science and Technology, Institute of Innovative Research, Tokyo Institute of Technology, Tokyo, Japan.
  • Yasuhisa Fujii
    Department of Urology, Tokyo Medical and Dental University Graduate School, Tokyo, Japan. y-fujii.uro@tmd.ac.jp.

Keywords

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