Treatment of Nocturnal Enuresis using miniaturised advanced mechatronics with Artificial Intelligence

Abstract

Objective: Our study was designed to develop a customisable, wearable medical — so-called — MyPAD device that generates pre-void alarms using miniaturised mechatronics with Artificial Intelligence (AI). Methods and procedures: The developed features include: multiple bespoke ultrasound (US) probes for sensing, a bespoke electronic device housing custom US electronics for signal processing, a bedside alarm box for processing the echoed pulses and generating alarms, and a phantom to mimic the human body. The validation of the system is conducted on the tissue-mimicking phantom and volunteers using Bidirectional Long Short-Term Memory Recurrent Neural Networks (Bi-LSTM-RNN) and Reinforcement Learning (RL). Results: A Se value of 99% and a Sp value of 99.5% with an overall accuracy rate of 99.3% are observed. Conclusion: The obtained results demonstrate successful empirical evidence for the viability of the device, both in monitoring bladder expansion to determine voiding need and in reinforcing the continuous learning and customisation of the device for bladder control through consecutive uses.