Monitoring bladder filling is crucial in patients with neurogenic lower urinary tract dysfunction, particularly for safe and timely clean intermittent catheterization. Wearable ultrasound devices have recently gained attention as potential aids for continuous bladder monitoring, but they differ fundamentally from traditional bladder scanners. While portable scanners directly measure postvoid residual volume in milliliters, first-generation wearable devices using amplitude mode (A-mode) ultrasound primarily monitor bladder fullness trends and provide threshold-based alerts rather than precise quantitative volume measurements. This narrative review examines wearable ultrasound technologies in neurourology, searching PubMed, Embase, IEEE Xplore, and Google Scholar for relevant clinical studies. Most available data come from small observational studies in pediatric populations. In children with functional voiding dysfunction (FVD), devices such as SENS-U achieved sensitivities of 85%–90% for detecting bladder fullness thresholds with good correlation to urodynamic measurements. However, performance in neurogenic bladder populations is concerning: a recent study in children with neurogenic bladder reported only 46% accuracy and 13% specificity using DFree, indicating unacceptably high false-positive rates. No prospective validation studies have been conducted in adults with spinal cord injury, multiple sclerosis, or those at risk for autonomic dysreflexia. Regulatory classifications vary significantly among devices: SENS-U holds Conformité Européenne marking as a Class IIa medical device for pediatric use, while DFree is marketed as a consumer wellness product without medical device approval. EdgeFlow UW20, which employs a deep learning-based automatic bladder contour detection, recently received Ministry of Food and Drug Safety approval in Korea as a digital medical device. Currently, evidence supports considering wearable bladder monitors only for pediatric FVD under medical supervision. For neurogenic bladder populations, these devices are not recommended for routine clinical use until population-specific validation studies demonstrate adequate diagnostic accuracy. The emergence of B-mode (brightness mode) deep learning-based devices may address the fundamental limitations of first-generation A-mode technology.
Kim et al. (Mon,) studied this question.