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Record W2896702453

A novel mobile acoustic uroflowmetry: Comparison of uroflowmetry and mobile acoustic uroflowmetry

2018· article· en· W2896702453 on OpenAlexaff
Ju L. Young, S. Jeeyoung, J. Jiyoung, S. Min-Ho, C. Hansol, Lee Sang-Chul

Bibliographic record

VenueNeurourology and Urodynamics · 2018
Typearticle
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsMedicine
DOInot available

Abstract

fetched live from OpenAlex

HYPOTHESIS / AIMS OF STUDY Uroflowmetry is a screening urodynamic test in urology. Standard conventional uroflowmetry is inconvenient for patients because the measurement environment is unfamiliar and unnatural, demanding a timed voiding for the test. A novel acoustic uroflowmetry is based on sound analysis using a smart phone, and can be used at home without cumbersome settings. The aim of this study is to evaluate the accuracy of this method comparing the new acoustic uroflowmetry to a standard uroflowmetry. STUDY DESIGN, MATERIALS AND METHODS A novel mobile acoustic uroflowmetry is an easy-to-use, non-invasive method to estimate the urine flow simply by recording the sound during voiding with a smart phone. After the approval of institutional review board, patients with voiding complaints were recruited and the voiding sound was recorded during standard uroflowmetry measurements. Male subjects were recorded in standing position and females in sitting position. The urine flow rate is calculated as the voiding sound was recorded and processed. Voided volume can be obtained by integrating the calculated flow rate. Cases with voided volume <20mL or having recording problems were excluded. Pearson’s correlation coefficient (PCC, r) was used to compare the maximal flow rate (Qmax), average flow rate (Qavg), and voided volume estimated by the standard uroflowmetry with those calculated via acoustic uroflowmetry. RESULTS A total of 97 patients including 60 males and 37 females were analyzed. Mean age was 59.8, 59.9 years for men and women respectively. Urination sounds differently by gender, perhaps due to differences in their anatomy of pelvis/lower urinary tract and posture during urination. Therefore, the data was analyzed separately for men and women. Flow patterns recorded by acoustic uroflowmetry and conventional uroflowmetry showed a good visual correlation (Fig 1). For male patients, average Qmax, Qavg and voided volume were 15.4mL/s, 8.4mL/s and 198mL, respectively. An excellent correlation was observed between the two methods for Qmax (r=0.88), Qavg (r=0.91) and voided volume (r=0.95). For female patients, average Qmax, Qavg and voided volume were 18.5mL/s, 9.7mL/s and 204mL, respectively. Qavg (r=0.93) and voided volume (r=0.96) showed excellent correlation, while Qmax showed good correlation (r=0.78) between the two methods in females. Figure 1. Examples of flow pattern recorded by the standard uroflowmetry (line) and acoustic uroflowmetry (dots) using voiding sound record after processing. INTERPRETATION OF RESULTS In 2015, Krhut et al. have reported a sound-based uroflowmetry, named ‘sono-uroflowmetry (SUF)’ [1]. This study was consisting 25 healthy male volunteers and demonstrated strong correlation for the duration (r=0.87). However, moderate correlation was observed in voided volume (r=0.68), and Qavg (r=0.57). For Qmax, poor correlation (r=0.38) was observed. Another study from the same team with 36 healthy female volunteers showed strong correlation for duration (r=0.95) but moderate correlation for voided volume (r=0.68) and poor correlation for the Qmax (r=0.38) [2]. While SUF focused on the basic relationship between sound intensity to instant flow rate in time domain, our prediction method analyzes various sound features and its combination in spectral domain. Additional algorithms were applied to suppress sound artifacts, offset environmental characteristics, and improve its prediction accuracy. Our results showed strong correlation between the result of standard uroflowmetry and estimated parameters by the acoustic sound based uroflowmetry. Qmax, Qavg and voided volume showed a strong correlation both in men and women who are with wide range of severity and various voiding patterns. This easy to use mobile acoustic uroflowmetry can be used to check and monitor the urinary flow rate and volume both in patients and healthy people in daily, natural settings. It can also offer longitudinal trends of key urodynamic parameters in a quantitative manner, so would be helpful for not only patients and caregivers, but also for healthcare providers and payers who need to pre-screen and monitor lower urinary tract symptoms. The smartphone app has a uses an automatic voiding diary for daily usage. Time to void and voided volume can be calculated and filled by predicted urine flow from recording each voiding event, and automatically consolidated for each day. This quantitative and ease-of-use app might improve shortcomings of current voiding diary such as incomplete voiding diaries with missing values and low compliance. Limitations include that males voiding in sitting position are not included for the analysis. The concept and baseline technology can also be applied for pediatric applications, but more investigation and validation will be necessary. CONCLUDING MESSAGE This study shows that an acoustic uroflowmetry is possible with a good correlation with the standard uroflowmetry. Further works on prediction accuracy and error with different toilet settings is needed for broader use.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.867
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.248
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations1
Published2018
Admission routes1
Has abstractyes

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