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

Applications of machine learning in urodynamics: A narrative review

BACKGROUND

Machine 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.

METHODS

We 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.

RESULTS

The 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.

CONCLUSIONS

At 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.

The Use of Uroflowmetry as a Diagnostic Test

PURPOSE OF REVIEW

Uroflowmetry is widely used for initial non-invasive evaluation of lower urinary tract disorders. Current clinical use is mostly restricted to a scrutiny of the maximum flow rate and uroflow pattern recorded by a conventional flowmeter in a health care facility. There are several advancements in our understanding and in available technologies that promise to transform clinical utilization of this simple test.

RECENT FINDINGS

Several aspects of the uroflow test in addition to maximum flow rate and uroflow pattern show potential diagnostic utility. This includes flow acceleration, uroflow indices, uroflow-electromyography including lag time, stop uroflow test, and uroflow-based nomograms. There are initial attempts to use artificial intelligence in analysis. There is also new data with regard to factors influencing variability of uroflow testing that might influence the diagnostic value in as yet uncertain ways including diurnal variability, postural variability, locational variability, and operator variability. There are new technologies for uroflow testing in a home environment allowing for easy repetition. However, there are several challenges owing to a paucity of clinical data and standardization. There are also critical lacunae in terminology that need to be addressed. There are exciting new advancements in the field of uroflowmetry. However, there is need to standardize and validate the newer uroflow tracing analyses and technologies.

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

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.