International journal of urology : official journal of the Japanese Urological Association · 2021
Development of an artificial intelligence diagnostic system for lower urinary tract dysfunction in men.
СтатьяНазвание и аннотация: Перевод в очереди
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Matsukawa Yoshihisa, Kameya Yoshitaka, Takahashi Tomoichi, Shimazu Atsuki, Ishida Shohei, Yamada Muneo, Sassa Naoto, Yamamoto Tokunori
Аннотация
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 < 0.001; bladder outlet obstruction index: r = 0.46, P < 0.001). The artificial intelligence system's detrusor underactivity diagnosis had a sensitivity and specificity of 79.7% and 88.7%, respectively, and those for bladder outlet obstruction diagnosis were 76.8% and 84.7%, respectively. The artificial intelligence system's average diagnostic accuracy was 84%, which was significantly higher than that of urologists (56%).
CONCLUSIONS: Our artificial intelligence diagnostic system developed using the uroflowmetry waveform distinguished between detrusor underactivity and bladder outlet obstruction with high sensitivity and specificity in men with lower urinary tract symptoms.
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 < 0.001; bladder outlet obstruction index: r = 0.46, P < 0.001). The artificial intelligence system's detrusor underactivity diagnosis had a sensitivity and specificity of 79.7% and 88.7%, respectively, and those for bladder outlet obstruction diagnosis were 76.8% and 84.7%, respectively. The artificial intelligence system's average diagnostic accuracy was 84%, which was significantly higher than that of urologists (56%).
CONCLUSIONS: Our artificial intelligence diagnostic system developed using the uroflowmetry waveform distinguished between detrusor underactivity and bladder outlet obstruction with high sensitivity and specificity in men with lower urinary tract symptoms.