Classification of Bladder Emptying Patterns by LSTM Neural Network Trained Using Acoustic Signatures

Jin, Jie; Chung, Youngbeen; Kim, Wanseung; Heo, Yonggi; Jeon, Jin-Yong; Hoh, Jeong Kyu; Park, Junhong; Jo, Jung Ki

doi:10.3390/s21165328

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…However, the validation and reproducibility of clinical applicability must be improved. Jin et al [17] generally used non-invasive uroflowmetry to diagnose lower urinary tract symptoms (LUTS) and the patient's health status. This study established an AI system that used acoustic features for LUTS assessment and was validated with a long short-term memory (LSTM) deep learning algorithm.…”

Section: Related Workmentioning

confidence: 99%

Application of a Deep Learning Neural Network for Voiding Dysfunction Diagnosis Using a Vibration Sensor

et al. 2022

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In a clinical context, there are increasing numbers of people with voiding dysfunction. To date, the methods of monitoring the voiding status of patients have included voiding diary records at home or urodynamic examinations at hospitals. The former is less objective and often contains missing data, while the latter lacks frequent measurements and is an invasive procedure. In light of these shortcomings, this study developed an innovative and contact-free technique that assists in clinical voiding dysfunction monitoring and diagnosis. Vibration signals during urination were first detected using an accelerometer and then converted into the mel-frequency cepstrum coefficient (MFCC). Lastly, an artificial intelligence model combined with uniform manifold approximation and projection (UMAP) dimensionality reduction was used to analyze and predict six common patterns of uroflowmetry to assist in diagnosing voiding dysfunction. The model was applied to the voiding database, which included data from 76 males aged 30 to 80 who required uroflowmetry for voiding symptoms. The resulting system accuracy (precision, recall, and f1-score) was around 98% for both the weighted average and macro average. This low-cost system is suitable for at-home urinary monitoring and facilitates the long-term uroflow monitoring of patients outside hospital checkups. From a disease treatment and monitoring perspective, this article also reviews other studies and applications of artificial intelligence-based methods for voiding dysfunction monitoring, thus providing helpful diagnostic information for physicians.

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Section: Related Workmentioning

confidence: 99%

Application of a Deep Learning Neural Network for Voiding Dysfunction Diagnosis Using a Vibration Sensor

et al. 2022

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Applications of machine learning in urodynamics: A narrative review

Liu,

Zhong,

Gao

et al. 2024

Neurourology and Urodynamics

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BackgroundMachine learning algorithms as a research tool, including traditional machine learning and deep learning, are increasingly applied to the field of urodynamics. However, no studies have evaluated how to select appropriate algorithm models for different urodynamic research tasks.MethodsWe undertook a narrative review evaluating how the published literature reports the applications of machine learning in urodynamics. We searched PubMed up to December 2023, limited to the English language. We selected the following search terms: artificial intelligence, machine learning, deep learning, urodynamics, and lower urinary tract symptoms. We identified three domains for assessment in advance of commencing the review. These were the applications of urodynamic studies examination, applications of diagnoses of dysfunction related to urodynamics, and applications of prognosis prediction.ResultsThe machine learning algorithm applied in the field of urodynamics can be mainly divided into three aspects, which are urodynamic examination, diagnosis of urinary tract dysfunction and prediction of the efficacy of various treatment methods. Most of these studies were single‐center retrospective studies, lacking external validation, requiring further validation of model generalization ability, and insufficient sample size. The relevant research in this field is still in the preliminary exploration stage; there are few high‐quality multi‐center clinical studies, and the performance of various models still needs to be further optimized, and there is still a distance from clinical application.ConclusionsAt present, there is no research to summarize and analyze the machine learning algorithms applied in the field of urodynamics. The purpose of this review is to summarize and classify the machine learning algorithms applied in this field and to guide researchers to select the appropriate algorithm model for different task requirements to achieve the best results.

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The Use of Uroflowmetry as a Diagnostic Test

Sinha

2024

Curr Urol Rep

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Classification of Bladder Emptying Patterns by LSTM Neural Network Trained Using Acoustic Signatures

Cited by 4 publications

References 17 publications

Application of a Deep Learning Neural Network for Voiding Dysfunction Diagnosis Using a Vibration Sensor

Application of a Deep Learning Neural Network for Voiding Dysfunction Diagnosis Using a Vibration Sensor

Applications of machine learning in urodynamics: A narrative review

The Use of Uroflowmetry as a Diagnostic Test

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