{
  "abstract": "Medical ultrasound is a portable, non-invasive, and cost effective imaging modality that is particularly well suited for resource-limited settings. Optic nerve sheath diameter (ONSD) measurement from ultrasound is used as a point-of-care assessment tool for intracranial pressure (ICP), which is associated with several neurological conditions. However, valid measurement depends on selecting a frame in which the optic nerve sheath is clearly visible. Manual selection requires a high level of expertise. To enable the use of ONSD in low resource settings by medical personnel of all levels, we present a sparsely supervised AI frame-work that scores each frame of an ONSD ultrasound video and selects the optimal frame for ONSD assessment. Per-frame embeddings from an ultrasound foundation model (USFM) are passed to a lightweight temporal head and trained using Gaussian soft labels, which assign graded targets around labeled key frames, rather than hard binary (0/1) per-frame targets. We evaluate the model performance using subject-level cross-validation on 18 subjects and 323 ultrasound videos spanning nine acquisition sweep types. Top-k frame selection is used as a metric for direct comparison against the baselines. While only requiring sparse labels, our method can identify an optimal frame in 82.2% of the evaluated sweeps, exceeding the strongest training-free baseline (49.2%) and hard-label classification (71.9%).",
  "abstract_shown": true,
  "affiliations": [
    "University of Central Florida"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "M. Ali"
    },
    {
      "name": "L. M. Castaneda"
    },
    {
      "name": "C. Wu"
    },
    {
      "name": "C. E. Escamilla-Ocanas"
    },
    {
      "name": "M. Hirzallah"
    },
    {
      "name": "L. J. Brattain"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8cxbh051daqck.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.21.26362872",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.09.21.26362872v1.full",
  "id": "8cxbh051daqck",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.09.21.26362872v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "radiology and imaging"
    ],
    [
      "openalex",
      "2706|Ultrasound in Clinical Applications"
    ]
  ],
  "openalex": "W7214039929",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.09.21.26362872v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8cxbh051daqck.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:10Z",
      "native_id": "10.64898/2026.09.21.26362872",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.09.21.26362872v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:radiology and imaging",
      "subfield": "imaging"
    }
  ],
  "title": "Soft Temporal Scoring Using a Foundation Model: Optimal Frame Selection for Improved ONSD Measurement in Ultrasound Videos",
  "title_text": "Soft Temporal Scoring Using a Foundation Model: Optimal Frame Selection for Improved ONSD Measurement in Ultrasound Videos",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:10Z",
  "url": "https://research.draconitic.com/paper/8cxbh051daqck",
  "venue": "medRxiv",
  "versions": []
}