Prediction of the composition of urinary stones using deep learning.

Journal: Investigative and clinical urology
PMID: 35670006

Abstract

PURPOSE: This study aimed to predict the composition of urolithiasis using deep learning from urinary stone images.

Authors

  • Ui Seok Kim
    Department of Urology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong, Korea.
  • Hyo Sang Kwon
    Department of Urology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong, Korea.
  • Wonjong Yang
    Department of Urology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong, Korea.
  • Wonchul Lee
    Department of Urology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong, Korea.
  • Changil Choi
    Department of Urology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong, Korea.
  • Jong Keun Kim
    Department of Urology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong-si, Geonggi-do, Republic of Korea; Department of Computer Engineering, Hallym University (SU, DHK, JK), Chuncheon, Gangwon-do, Republic of Korea.
  • Seong Ho Lee
    Department of Urology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong-si, Geonggi-do, Republic of Korea; Department of Computer Engineering, Hallym University (SU, DHK, JK), Chuncheon, Gangwon-do, Republic of Korea. Electronic address: shleeuro@hallym.ac.kr.
  • Dohyoung Rim
    Department of Cognitive Science, Yonsei University, Seoul, Korea.
  • Jun Hyun Han
    Department of Urology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong-si, Geonggi-do, Republic of Korea; Department of Computer Engineering, Hallym University (SU, DHK, JK), Chuncheon, Gangwon-do, Republic of Korea.

Keywords

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