Development and Validation of a Deep Learning System for Sound-based Prediction of Urinary Flow.

Journal: European urology focus
PMID: 35835694

Abstract

BACKGROUND: Uroflowmetry remains an important tool for the assessment of patients with lower urinary tract symptoms (LUTS), but accuracy can be limited by within-subject variation of urinary flow rates. Voiding acoustics appear to correlate well with conventional uroflowmetry and show promise as a convenient home-based alternative for the monitoring of urinary flows.

Authors

  • Han Jie Lee
    Department of Urology, Singapore General Hospital, Singapore.
  • Edwin Jonathan Aslim
    Department of Urology, Singapore General Hospital, Singapore.
  • B T Balamurali
    Department of Science, Mathematics and Technology, Singapore University of Technology and Design, Singapore. Electronic address: balamurali_bt@sutd.edu.sg.
  • Lynn Yun Shu Ng
    Department of Urology, Singapore General Hospital, Singapore.
  • Tricia Li Chuen Kuo
    Urohealth Medical Clinic, Singapore.
  • Cindy Ming Ying Lin
    Department of Science, Mathematics and Technology, Singapore University of Technology and Design, Singapore.
  • Christopher Johann Clarke
    Department of Science, Mathematics and Technology, Singapore University of Technology and Design, Singapore.
  • Prachee Priyadarshinee
    Department of Science, Mathematics and Technology, Singapore University of Technology and Design, Singapore.
  • Jer-Ming Chen
    Singapore University of Technology and Design, Singapore balamurali_bt@sutd.edu.sg, saumitrakapoor@gmail.com, jerming_chen@sutd.edu.sg.
  • Lay Guat Ng
    Department of Urology, Singapore General Hospital, Singapore. Electronic address: ng.lay.guat@singhealth.com.sg.

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