Diagnosis of Pneumonia by Cough Sounds Analyzed with Statistical Features and AI.

Pneumonia is a serious disease often accompanied by complications, sometimes leading to death. Unfortunately, diagnosis of pneumonia is frequently delayed until physical and radiologic examinations are performed. Diagnosing pneumonia with cough sounds would be advantageous as a non-invasive test that could be performed outside a hospital. We aimed to develop an artificial intelligence (AI)-based pneumonia diagnostic algorithm. We collected cough sounds from thirty adult patients with pneumonia or the other causative diseases of cough. To quantify the cough sounds, loudness and energy ratio were used to represent the level and its spectral variations. These two features were used for constructing the diagnostic algorithm. To estimate the performance of developed algorithm, we assessed the diagnostic accuracy by comparing with the diagnosis by pulmonologists based on cough sound alone. The algorithm showed 90.0% sensitivity, 78.6% specificity and 84.9% overall accuracy for the 70 cases of cough sound in pneumonia group and 56 cases in non-pneumonia group. For same cases, pulmonologists correctly diagnosed the cough sounds with 56.4% accuracy. These findings showed that the proposed AI algorithm has value as an effective assistant technology to diagnose adult pneumonia patients with significant reliability.

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. The initial sample size comprised 34 patients; 27 patients were included in the final analysis. Uroflow sounds generated from flow impacts on a structure were analyzed by loudness and roughness parameters. (3) Results: A similar signal pattern to the clinical urological measurements was observed and applied for health diagnosis. (4) Conclusions: Consistent flowrate values were obtained by applying the uroflow sound samples from the randomly selected patients to the constructed model for validation. The flowrate predicted using the acoustic signature accurately demonstrated actual physical characteristics. This could be used for developing a new smart flowmetry device applicable in everyday life with minimal constraints from settings and enable remote diagnosis of urinary system diseases by objective continuous measurements of bladder emptying function.

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. An accelerometer directly attached to the eyeball measured the vibration response. Excitations and measurements were performed for 1 s, and the excitation magnitude was varied for the same signal in repeat measurements. A 30-gauge needle was inserted into the anterior chamber of the eyeball to inject a balanced salt solution, and the height of the bottle was adjusted to adjust the IOP. A tonometer was used under identical conditions to measure the IOP five times, and the mean value was determined for further analyses. The measurements showed that the parameters resonance frequency and change in the magnitude of the vibration response (CMVR) increased with rising IOP values. The CMVR was highly correlated with the IOP (p-value < 0.0001). A linear mixed effects model (LMM) was used as a statistical analysis method. We confirmed that vibroacoustic properties of the eyeball are correlated with IOP changes. It is expected that the CMVR will serve as a new parameter for IOP measurements. Thus, in the future, continuous IOP measurements would be easily performed using the CMVR.

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. An FFT (fast Fourier transform) and an STFT (short-time Fourier transform) were used to analyze the vibration characteristics of the gauge plate depending on the mass flow rate. For precise measurements, the vibration level and roughness were computed and used as input features. The actual mass flow rate measured by using a weight transducer was employed as the output features for the LSTM prediction model. (3) Results: The estimated flow rate matched the actual measured mass flow rate very closely. The deviations in measurements for the total mass flow were less than 6%. (4) Conclusions: The estimation of the mass flow rate for external flow through the proposed flowmeter by use of vibration responses analyzed by the LSTM neural network was proposed and verified.