Memory-efficient low-compute segmentation algorithms for bladder-monitoring smart ultrasound devices.

ABSTRACT: Post-operative urinary retention is a medical condition where patients cannot urinate despite having a full bladder. Ultrasound imaging of the bladder is used to estimate urine volume for early diagnosis and management of urine retention. Moreover, the use of bladder ultrasound can reduce the need for an indwelling urinary catheter and the risk of catheter-associated urinary tract infection. Wearable ultrasound devices combined with machine-learning based bladder volume estimation algorithms reduce the burdens of nurses in hospital settings and improve outpatient care. However, existing algorithms are memory and computation intensive, thereby demanding the use of expensive GPUs. In this paper, we develop and validate a low-compute memory-efficient deep learning model for accurate bladder r

SUBMITTER: Song Z 

PROVIDER: S-EPMC10542811 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature