Development of an artificial intelligence diagnostic system for lower urinary tract dysfunction in men

Objectives To establish an artificial intelligence diagnostic system for lower urinary tract function in men with lower urinary tract symptoms using only uroflowmetry data and to evaluate its usefulness. Methods Uroflowmetry data of 256 treatment‐naive men with detrusor underactivity, bladder outlet...

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Bibliographic Details
Published in:International journal of urology 2021-11, Vol.28 (11), p.1143-1148
Main Authors: Matsukawa, Yoshihisa, Kameya, Yoshitaka, Takahashi, Tomoichi, Shimazu, Atsuki, Ishida, Shohei, Yamada, Muneo, Sassa, Naoto, Yamamoto, Tokunori
Format: Article
Language:English
Subjects:
Artificial intelligence
Bladder
Contractility
deep learning
detrusor underactivity
Diagnosis
lower urinary tract dysfunction
Neural networks
Urinary tract
urodynamics
Urogenital system
Urology
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Summary:Objectives To establish an artificial intelligence diagnostic system for lower urinary tract function in men with lower urinary tract symptoms using only uroflowmetry data and to evaluate its usefulness. Methods Uroflowmetry data of 256 treatment‐naive men with detrusor underactivity, bladder outlet obstruction, or detrusor underactivity + bladder outlet obstruction were used for artificial intelligence learning and validation using neural networks. An optimal artificial intelligence diagnostic model was established using 10‐fold stratified cross‐validation and data augmentation. Correlations of bladder contractility index and bladder outlet obstruction index values for the artificial intelligence system and pressure flow study values were examined using Spearman’s correlation coefficients. Additionally, diagnostic accuracy was compared between the established artificial intelligence system and trained urologists with uroflowmetry data of 25 additional patients by χ2‐tests. Detrusor underactivity was defined as bladder contractility index ≤100 and bladder outlet obstruction index ≤40, bladder outlet obstruction was defined as bladder contractility index >100 and bladder outlet obstruction index >40, and detrusor underactivity + bladder outlet obstruction was defined as bladder contractility index ≤100 and bladder outlet obstruction index >40. Results The artificial intelligence system’s estimated bladder contractility index and bladder outlet obstruction index values showed significant positive correlations with pressure flow study values (bladder contractility index: r = 0.60, P 
ISSN:0919-8172
1442-2042
DOI:10.1111/iju.14661