{
  "abstract": "Deep video networks estimate left ventricular ejection fraction (EF) from echocardiograms with expert-level accuracy, but the compute cost of running them is rarely reported, which leaves anyone building a handheld or bedside tool without guidance on what to deploy. We measured the accuracy versus compute trade space for EF estimation on EchoNet-Dynamic by training 22 configurations that vary clip length (8 to 64 frames), frame sampling period (1 to 4), and backbone (R(2+1)D-18, R3D-18, MC3-18, X3D-S, X3D-M, and a 2D ResNet-18 with temporal pooling), under one fixed training recipe. Every configuration was scored on accuracy (mean absolute error, R2, Bland-Altman agreement), on clinical utility (sensitivity and specificity at the EF 40% and 50% treatment thresholds, error stratified by EF band), and on cost (floating point operations, parameters, GPU and CPU latency, peak memory) under a single frozen measurement protocol. Headline claims were stress tested with replicate training seeds. Sparse temporal sampling consistently beat dense sampling: at a fixed frame count, period 4 improved mean absolute error by about one full point over period 1 across all nine cross-seed pairings while also cutting per-video cost. A plain R3D-18 achieved the best point accuracy in the study (mean absolute error 3.99), statistically tied with the reference, at 19% less CPU latency, and a 16-frame, period-4 R(2+1)D-18 halved the reference cost with no statistically confirmed accuracy loss, though with a small seed-consistent disadvantage that no single seed reveals. Removing temporal modeling entirely collapsed accuracy (mean absolute error 5.65), placing a floor under how cheap this task can get. We release the code, the cost protocol, and all per-configuration results.",
  "abstract_shown": true,
  "affiliations": [
    "The University of Texas at Austin"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "A. Pandey"
    },
    {
      "affs": [
        0
      ],
      "name": "K. Sharma"
    },
    {
      "name": "A. Shah"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fj539qwdb0d7w.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.02.26362055",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.09.02.26362055v1.full",
  "id": "fj539qwdb0d7w",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.09.02.26362055v1",
  "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",
      "cardiovascular medicine"
    ],
    [
      "openalex",
      "2705|Cardiovascular Function and Risk Factors"
    ]
  ],
  "openalex": "W7211935525",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.09.02.26362055v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fj539qwdb0d7w.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:53Z",
      "native_id": "10.64898/2026.09.02.26362055",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.09.02.26362055v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:cardiovascular medicine",
      "subfield": "cardiovascular"
    }
  ],
  "title": "Ejection fraction on a budget: mapping the accuracy-compute trade space for video-based ejection fraction estimation",
  "title_text": "Ejection fraction on a budget: mapping the accuracy-compute trade space for video-based ejection fraction estimation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:53Z",
  "url": "https://research.draconitic.com/paper/fj539qwdb0d7w",
  "venue": "medRxiv",
  "versions": []
}