MP62-02 A NOVEL MACHINE-LEARNING AUGMENTED AUDIO-UROFLOWMETRY – COMPARISON WITH STANDARD UROFLOWMETRY
Bibliographic record
Abstract
You have accessJournal of UrologyUrodynamics/Lower Urinary Tract Dysfunction/Female Pelvic Medicine: Basic Research & Pathophysiology (MP62)1 Apr 2019MP62-02 A NOVEL MACHINE-LEARNING AUGMENTED AUDIO-UROFLOWMETRY – COMPARISON WITH STANDARD UROFLOWMETRY Edwin Jonathan Aslim*, Balamurali BK, Yun Shu Lynn Ng, Tricia Li Chuen Kuo, Jacob Shihang Chen, Jer-ming Chen, and Lay Guat Ng Edwin Jonathan Aslim*Edwin Jonathan Aslim* More articles by this author , Balamurali BK Balamurali BK More articles by this author , Yun Shu Lynn NgYun Shu Lynn Ng More articles by this author , Tricia Li Chuen KuoTricia Li Chuen Kuo More articles by this author , Jacob Shihang ChenJacob Shihang Chen More articles by this author , Jer-ming ChenJer-ming Chen More articles by this author , and Lay Guat NgLay Guat Ng More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000556825.09468.1aAboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: The current standard of uroflowmetry is equipment-intensive and needs to be performed on site. We attempt to correlate the audio recordings of urinary flows with standard uroflowmetry, with the aim to create a novel audio-uroflow app for use with a smart phone. This study is IRB approved (CIRB 2017/2241) and supported by the SingHealth Surgery ACP-SUTD Technology and Design Multi-Disciplinary Development Programme (grant No. TDMD-2016-1). METHODS: This study prospectively enrolled 25 healthy male volunteers without lower urinary tract symptoms (LUTS), aged 21 to 50 years, from 01 June 2017 to 31 October 2017. Participants were asked to void into a digital standard uroflowmetry machine (MMS version 9.1z, LABORIE, Mississauga, Canada) with a minimum voided volume of 100ml, and urinary flow sounds were simultaneously recorded using a smartphone. Audio recordings were digitally pre-processed to remove background noise, and then paired with the corresponding uroflowmetry readings (UF) to train a machine-learning (ML) algorithm to understand the relationship between them. 70% of the voiding sessions were used to train the ML algorithm, and the remaining 30% sessions were used for testing. The predicted audio-uroflowmetry readings (AF) derived from the acoustic patterns learned by the ML algorithm (not calculated from flow rate vs time plots) were compared against UF parameters such as maximum flow rates (Qmax) and voided volumes (VV). The comparison was done by visually analysing the scatter plot and calculating Pearson’s correlation coefficient (r). RESULTS: There were 52 paired uroflow readings and audio recordings, of which 35 datasets were used to train the ML algorithm, and 17 datasets for AF prediction. In the training datasets, the median Qmax and VV were 27.5ml/sec (range 10.7 to 40.1) and 326ml (range 155 to 723), respectively. The trained model was evaluated by comparing UF and AF readings. The median Qmax corresponding to the test UF and predicted AF were 25.6ml/sec (range 8.6 to 39.7) and 27.0ml/sec (range 15.0 to 29.0), with an r value of 0.70. The median VV corresponding to the test UF and predicted AF were 419ml (range 138 to 791) and 360ml (range 242 to 707), with an r value of 0.83. The scatterplots for VV and Qmax, between UF and AF, showed good correlations. CONCLUSIONS: There is good correlation between AI-assisted audio-uroflow predictions with uroflowmetry parameters. Work is ongoing to train the machine-learning algorithm on a larger sample of men with LUTS to improve its prediction capability. Source of Funding: SingHealth Surgery ACP-SUTD Technology and Design Multi-Disciplinary Development Programme (grant No. TDMD-2016-1) Singapore, Singapore© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e887-e887 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Edwin Jonathan Aslim* More articles by this author Balamurali BK More articles by this author Yun Shu Lynn Ng More articles by this author Tricia Li Chuen Kuo More articles by this author Jacob Shihang Chen More articles by this author Jer-ming Chen More articles by this author Lay Guat Ng More articles by this author Expand All Advertisement PDF downloadLoading ...
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".