{
  "abstract": "A measurable fraction of examinations in retrospective imaging archives lack a recorded body weight, limiting metadata completeness for tasks such as cohort auditing, weight-stratified analysis, and the assembly of machine-learning training datasets. We developed and compared three approaches for body-weight prediction from a single middle-slice CT image: a LightGBM model on 19 CT-derived tabular features (water equivalent diameter, effective diameter, body composition, scanner parameters), an ImageNet-pretrained ResNet-18 on images alone, and a ResNet-18 with late fusion of the same 19 tabular features into the 512-dimensional image embedding. Models were trained with 5-fold patient-disjoint cross-validation on a combined train+validation development pool of 30,857 examinations from 16,169 patients; the best-performing fold of each model family was selected by within-fold validation mean absolute error (MAE) and evaluated, without further tuning, on a held-out test partition of 3,479 examinations from 1,796 patients (patient-disjoint from training and validation). On the test set, the fusion ResNet-18 achieved MAE 4.06 kg (95% CI 3.90–4.23; R² = 0.92), with 69.5% of predictions within ± 5 kg and 93.8% within ± 10 kg. A weighted ensemble of the fusion ResNet and LightGBM produced a small further reduction in MAE to 3.99 kg (paired bootstrap p < .001), but the 0.07 kg difference is below practically meaningful thresholds. The simpler single-model ResNet-18 with metadata fusion may be useful for retrospective missing-weight imputation, with split-conformal 90% and 95% prediction intervals (empirical test coverage 89% and 94%) available for case-level uncertainty quantification.\n\nSupplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s12194-026-01122-1.",
  "abstract_shown": true,
  "affiliations": [
    "Radiology, Monash Health, Clayton, VIC 3168 Australia",
    "Department of Medical Imaging and Radiation Sciences, Monash University, Clayton, VIC 3800 Australia"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Daniel Carrion",
      "orcid": "0000-0002-7564-8210"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "M. K. Badawy",
      "orcid": "0000-0001-8029-9951"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a1w0hw88c92x6.bib",
  "cited_by": 1,
  "doi": "10.1007/s12194-026-01122-1",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13740028/",
  "id": "a1w0hw88c92x6",
  "issns": [
    "1865-0333",
    "1865-0341"
  ],
  "landing_url": "https://doi.org/10.1007/s12194-026-01122-1",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Research Article"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7203753348",
  "pdf_url": "https://link.springer.com/content/pdf/10.1007/s12194-026-01122-1.pdf",
  "pmcid": "PMC13740028",
  "pmid": "42616350",
  "published": "2026-08-19",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/a1w0hw88c92x6.ris",
  "sources": [
    {
      "first_seen": "2026-11-10T12:21:21Z",
      "native_id": "PMC13740028",
      "source": "pmc",
      "url": "https://doi.org/10.1007/s12194-026-01122-1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Single-slice ct body-weight prediction with image and metadata fusion: a multi-model comparison",
  "title_text": "Single-slice ct body-weight prediction with image and metadata fusion: a multi-model comparison",
  "type": "peer-reviewed",
  "updated_at": "2026-11-10T12:21:21Z",
  "url": "https://research.draconitic.com/paper/a1w0hw88c92x6",
  "venue": "Radiological Physics and Technology",
  "versions": []
}