RT Journal Article
SR Electronic
T1 Pilot study for the comparison of machine-learning augmented audio-uroflowmetry with standard uroflowmetry in healthy men
JF BMJ Innovations
JO BMJ Innov
FD All India Institute of Medical Sciences
SP 199
OP 203
DO 10.1136/bmjinnov-2019-000382
VO 6
IS 4
A1 Aslim, Edwin Jonathan
A1 B T, Balamurali
A1 Ng, Yun Shu Lynn
A1 Kuo, Tricia Li Chuen
A1 Lim, Kheng Sit
A1 Chen, Jacob Shihan
A1 Chen, Jer-Ming
A1 Ng, Lay Guat
YR 2020
UL http://innovations.bmj.com/content/6/4/199.abstract
AB Background Routine assessments of lower urinary tract symptoms (LUTS) include standard uroflowmetry (UF), which is labour and equipment intensive to perform, and stressful and unnatural for patients. An ideal test should be accurate, repeatable, affordable and portable.Objective To evaluate the accuracy of a machine-learning (ML) augmented audio-uroflowmetry (AF) algorithm in predicting urinary flows.Subjects and methods This pilot study enrolled 25 healthy men without LUTS, who were asked to void into a gravimetric uroflowmeter. A smartphone recorded the voiding sounds simultaneously. Paired uroflow and audio parameters were used to train an ensemble ML model to predict urinary flows from voiding sounds. Pearson’s correlation coefficient was used to compare UF with AF values. Statistical significance was defined as p<0.05.Results A total of 52 voiding session were captured, of which n=35 were used for training and n=17 for testing the algorithm. Each voiding session was divided into 0.1 s frames, resulting in >300 analysable datapoints per session. Pearson’s coefficients showed strong correlations for flowtimes (r=0.96, p<0.0001), voided volumes (r=0.83, p<0.0001) and average flowrates (r=0.70, p=0.0019), and moderate correlation for maximal flowrate (r=0.69, p=0.0022). AF predicted flow patterns showed good agreement with UF tracings. The main limitations were the small participants sample size and use of a single smartphone type.Conclusions ML augmented AF can predict uroflow parameters with a good accuracy, and can be a viable alternative to standard UF. Further work is needed to develop this platform for use in real-life conditions and across genders.