Abstract
Bladder urine volume has been estimated using an ellipsoid method based on triaxial measurements of the bladder extrapolated from two-dimensional ultrasound images. This study aimed to automate this process and to determine the accuracy of the automated estimation method for normal and small amounts of urine. A training set of 81 pairs of transverse and longitudinal ultrasound images were collected from healthy volunteers on a tablet-type ultrasound device, and an automatic detection tool was developed using them. The tool was evaluated using paired transverse/longitudinal ultrasound images from 27 other healthy volunteers. After imaging, the participants voided and their urine volume was measured. For determining accuracy, regression coefficients were calculated between estimated bladder volume and urine volume. Further, sensitivity and specificity for 50 and 100 ml bladder volume thresholds were evaluated. Data from 50 procedures were included. The regression coefficient was very similar between the automatic estimation (β = 0.99, R2 = 0.96) and manual estimation (β = 1.05, R2 = 0.97) methods. The sensitivity and specificity of the automatic estimation method were 88.5% and 100.0%, respectively, for 100 ml and were 94.1% and 100.0%, respectively, for 50 ml. The newly-developed automated tool accurately and reliably estimated bladder volume at two different volume thresholds of approximately 50 ml and 100 ml.
Highlights
The convolutional neural network (CNN) is a deep learning model commonly used in the field of image recognition because it delivers the best performance in various tasks related to object detection and semantic segmentation [15,16]
We developed a new tool to automatically estimate the three axial diameters of the bladder from 2D US images, which was found to be highly accurate in estimating the actual voided urine volumes of approximately 50 ml and 100 ml
Many reports validate the accuracy of US-based bladder urine volume measurement [4,5,6,7,9,10,11,12], few reports focus on small volumes [18]
Summary
This study aimed to automate this process and to determine the accuracy of the automated estimation method for normal and small amounts of urine. The aim of this study was (1) to develop a new tool for conventional 2D US imaging with a function that automatically determines the three diameters of the bladder from the images and accurately calculates bladder urine volume and (2) to validate the accuracy of the developed tool when bladder urine volume is low, i.e., below 100 ml
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