{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Edgecare Inc., Seoul, Korea",
    "Department of Urology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Junhwan Kwon"
    },
    {
      "affs": [
        0
      ],
      "name": "Ilseob Song"
    },
    {
      "affs": [
        0
      ],
      "name": "Sangeun Park"
    },
    {
      "affs": [
        1
      ],
      "name": "Jongsoo Lee"
    },
    {
      "affs": [
        0
      ],
      "name": "Yangmo Yoo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4fqbzgtsjm525.bib",
  "cited_by": 0,
  "doi": "10.14366/usg.25258",
  "fulltext_url": null,
  "id": "4fqbzgtsjm525",
  "issns": [
    "2288-5943",
    "2288-5919"
  ],
  "landing_url": "https://doi.org/10.14366/usg.25258",
  "language": "en",
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "R855-855.5"
    ],
    [
      "openalex",
      "1707|Medical Image Segmentation Techniques"
    ]
  ],
  "openalex": "W7165916398",
  "pdf_url": "http://www.e-ultrasonography.org/upload/usg-25258.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-01",
  "publisher": "Korean Society of Ultrasound in Medicine",
  "ris": "https://research.draconitic.com/paper/4fqbzgtsjm525.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T14:22:48Z",
      "native_id": "0d07c6c218fd4cf28553884fc8f426b4",
      "source": "doaj",
      "url": "https://doi.org/10.14366/usg.25258"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R855-855.5",
      "subfield": "general"
    }
  ],
  "title": "A new single-sagittal-plane deep learning approach for continuous bladder volume monitoring: a feasibility study",
  "title_text": "A new single-sagittal-plane deep learning approach for continuous bladder volume monitoring: a feasibility study",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T14:20:46Z",
  "url": "https://research.draconitic.com/paper/4fqbzgtsjm525",
  "venue": "Ultrasonography",
  "versions": []
}