Study

Clinical Purpose

AI Methods

Number of Datasets/Subjects

Performance Results of Model

Burton, 2019

Diagnosis of urinary tract infection

Random forest; neural network; extreme gradient boosting

212,554 reports

Classification sensitivity above 95%

Barzegari, 2018

Diagnosis of stress UI

MLP ANN

6453 data points (22 patients)

N/A

Geramipour, 2015

Real-time estimation of bladder pressure and stimulation to reduce incontinence

MLP feed forward network

levenberg-marquardt (for training)

3 rats

Tracking performance at 8.89% (neural network model);

5-s bladder prediction error is about 10.54%

Laurikkala, 1999

Diagnose female urinary incontinence

Genetic algorithm-based galactica; C.4.5

Trained on 1105 sets; tested on 485

Mean prediction accuracy of 91% (C.4.5 rule-based) and 90% (Galactica);

Mean descriptive accuracy of 92% (C.4.5 rule-based) and 88% (Galactica)

Hung et al.,

2019

Prediction of urinary continence after robot-assisted radical prostatectomy (RARP)

Deep learning model (DeepSurv)

Automated performance metrics acquired from 100 patients

Achieved the lowest mean absolute error (MAE) of 85.9;

CI of 0.6

Karem et al., 2016

Classification of bladder events such as detrusor contraction for urinary incontinence diagnosis

Context-Aware Thresholding (CAT) algorithm

14 human subjects;

vesical and abdominal pressure data from 64 tracings from the human subjects

97% accuracy (CAT);

1.34 false positivity rate (CAT)

Huang, 2007

Diagnosis of urodynamic Stress incontinence (USI) with computer-aided perineal ultrasound

MLP neural network with backpropagation algorithm for training

48 women—36 with USI; 12 normal

91.7% accuracy;

94.4% sensitivity;

94.4% positive predictive value; 83.3% negative predictive value