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Building Dual AI Models and Nomograms Using Noninvasive Parameters for Aiding Male Bladder Outlet Obstruction Diagnosis and Minimizing the Need for Invasive Video-Urodynamic Studies: Development and Validation Study.

Chung-You TsaiJing-Hui TianChien-Cheng LeeHann-Chorng Kuo
Published in: Journal of medical Internet research (2024)
The 2 machine learning models predicting ICS-BOO and VBOO, based on 6 noninvasive clinical parameters, demonstrate commendable discrimination performance. Using the dual-model prediction approach, when both models predict positively, VUDS may be avoided, assisting in male BOO diagnosis and reducing the need for such invasive procedures.
Keyphrases
  • machine learning
  • artificial intelligence
  • spinal cord injury
  • urinary tract
  • deep learning
  • lower urinary tract symptoms