A new single-sagittal-plane deep learning approach for continuous bladder volume monitoring: a feasibility study
- 1Edgecare Inc., Seoul, Korea
- 2Department of Urology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
Abstract
Purpose Conventional ultrasound-based bladder volumetry multiplies three orthogonal diameters and applies a heuristic shape coefficient. This approach requires near-simultaneous transverse and sagittal imaging and is vulnerable to interplane misregistration and coefficient-dependent bias. This study evaluated whether a single sagittal B-mode image can provide accurate, patient-specific bladder volume estimation by leveraging sagittal morphology, perivesical echotexture, and latent image features associated with overall bladder geometry. Methods In this Institutional Review Board–approved, single-center study, participants were scanned in the supine position, and bladder volume was referenced to immediate Foley catheter drainage. Segmentation masks were independently annotated by two sonographers and adjudicated by a urologist. Maximum sagittal length was extracted using OpenCV and included as an auxiliary scalar input. A single-stage deep learning model was developed that receives one sagittal B-mode image and its expert-annotated segmentation mask as input. The model used a lightweight convolutional neural network backbone with integrated self-attention to capture global echotextural cues. Performance on a held-out test set (n=53) was assessed using linear regression, Bland-Altman analysis, mean absolute error, and mean absolute percentage error. Results The model demonstrated a strong correlation with catheter-derived volumes (R²=0.87). Bland-Altman analysis showed a mean bias of −3.96±48.44 mL, with limits of agreement from −98.90 to +90.98 mL. Stratified error analysis showed a mean absolute error of 14.15 mL for volumes of 0–100 mL and a mean absolute percentage error of 16.64% for volumes ≥100 mL. Attention maps emphasized the bladder wall and perivesical soft tissues, suggesting reliance on contextual echotexture. Conclusion A deep learning framework using a single sagittal plane estimated bladder volume with accuracy approaching that of two-plane techniques while simplifying image acquisition and eliminating reliance on fixed shape coefficients.
Related
- Sagittal Long-Axis Bladder Diameter and CT-Measured Bladder Wall Thickness in Healthy Female Patients.
- Acceptability, feasibility, quality of life and diabetes distress score outcomes: A pragmatic randomised clinical trial on continuous glucose monitoring for people with type 1 diabetes
- Usability, acceptability and feasibility of continuous glucose monitoring among children and adolescents with type 1 diabetes in Kenya
- Localization-Aware Multiscale Deep Learning for Lumbar Foraminal Stenosis in Multi-Scanner Sagittal MRI: A Leakage-Controlled Evaluation
- 124 Integrated single-cell transcriptomic profiling of urine-derived cells as a strategy for non-invasive therapeutic monitoring in bladder cancer
BibTeX
@article{kwon2026singlesagittalplane,
title = {{A new single-sagittal-plane deep learning approach for continuous bladder volume monitoring: a feasibility study}},
author = {Junhwan Kwon and Ilseob Song and Sangeun Park and Jongsoo Lee and Yangmo Yoo},
journal = {Ultrasonography},
year = {2026},
month = jul,
doi = {10.14366/usg.25258},
url = {https://doi.org/10.14366/usg.25258},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - A new single-sagittal-plane deep learning approach for continuous bladder volume monitoring: a feasibility study
AU - Junhwan Kwon
AU - Ilseob Song
AU - Sangeun Park
AU - Jongsoo Lee
AU - Yangmo Yoo
PY - 2026
DA - 2026/07/01
JO - Ultrasonography
DO - 10.14366/usg.25258
UR - https://doi.org/10.14366/usg.25258
AB - Purpose Conventional ultrasound-based bladder volumetry multiplies three orthogonal diameters and applies a heuristic shape coefficient. This approach requires near-simultaneous transverse and sagittal imaging and is vulnerable to interplane misregistration and coefficient-dependent bias. This study evaluated whether a single sagittal B-mode image can provide accurate, patient-specific bladder volume estimation by leveraging sagittal morphology, perivesical echotexture, and latent image features associated with overall bladder geometry. Methods In this Institutional Review Board–approved, single-center study, participants were scanned in the supine position, and bladder volume was referenced to immediate Foley catheter drainage. Segmentation masks were independently annotated by two sonographers and adjudicated by a urologist. Maximum sagittal length was extracted using OpenCV and included as an auxiliary scalar input. A single-stage deep learning model was developed that receives one sagittal B-mode image and its expert-annotated segmentation mask as input. The model used a lightweight convolutional neural network backbone with integrated self-attention to capture global echotextural cues. Performance on a held-out test set (n=53) was assessed using linear regression, Bland-Altman analysis, mean absolute error, and mean absolute percentage error. Results The model demonstrated a strong correlation with catheter-derived volumes (R²=0.87). Bland-Altman analysis showed a mean bias of −3.96±48.44 mL, with limits of agreement from −98.90 to +90.98 mL. Stratified error analysis showed a mean absolute error of 14.15 mL for volumes of 0–100 mL and a mean absolute percentage error of 16.64% for volumes ≥100 mL. Attention maps emphasized the bladder wall and perivesical soft tissues, suggesting reliance on contextual echotexture. Conclusion A deep learning framework using a single sagittal plane estimated bladder volume with accuracy approaching that of two-plane techniques while simplifying image acquisition and eliminating reliance on fixed shape coefficients.
SN - 2288-5943
SN - 2288-5919
ER -