A new single-sagittal-plane deep learning approach for continuous bladder volume monitoring: a feasibility study.
Authors
Affiliations (6)
Affiliations (6)
- Edgecare Inc., Seoul, Korea.
- Anesthesia and Pain Research Institute, Yonsei University College of Medicine, Seoul, Korea.
- Medical Solution Institute, Sogang University, Seoul, Korea.
- Department of Urology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
- Department of Electronic Engineering, Sogang University, Seoul, Korea.
- Department of Biomedical Engineering, Sogang University, Seoul, Korea.
Abstract
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. 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. 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. 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.