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

(1) Background: Non-invasive uroflowmetry is used in clinical practice for diagnosing lower urinary tract symptoms (LUTS) and the health status of a patient. To establish a smart system for measuring the flowrate during urination without any temporospatial constraints for patients with a urinary disorder, the acoustic signatures from the uroflow of patients being treated for LUTS at a tertiary hospital were utilized. (2) Methods: Uroflowmetry data were collected for construction and verification of a long short-term memory (LSTM) deep-learning algorithm.

A pilot study for intraocular pressure measurements based on vibroacoustic parameters.

The present study aimed to identify vibroacoustic properties associated with intraocular pressure (IOP) changes and to suggest a new way to measure the IOP based on these properties. Ten ex vivo porcine eyeballs were used in this study. Each eyeball was fixated in a central hole of a Styrofoam block, and vibration applied to the Styrofoam block was transmitted to the eyeball.

Development of a Flowmeter Using Vibration Interaction between Gauge Plate and External Flow Analyzed by LSTM.

(1) Background: This study is aimed at the development of a precise and inexpensive device for flow information measurement for external flow. This novel flowmeter uses an LSTM (long short-term memory) neural network algorithm to analyze the vibration responses of the gauge plate. (2) Methods: A signal processing method using an LSTM neural network is proposed for the development of mass flow rate estimation by sensing the vibration responses of a gauge plate.