{
  "abstract": "Computed tomography (CT) interpretation requires recognition of the anatomical level of each slice. Classification may facilitate preprocessing and AI-assisted fracture diagnosis. We developed deep learning models to classify craniofacial CT slices into six anatomical categories. A total of 11,769 images were collected from a multicenter database involving 10 Japanese university hospitals and the public Head-Neck-Radiomics-HN1 dataset. Patients were divided into training (70%) and evaluation (30%) sets. Five CNN architectures were trained from scratch, and hard- and soft-voting ensembles were evaluated. Individual-model accuracy ranged from 94.72% to 96.09%; the hard-voting ensemble achieved 96.46% accuracy (Cohens {kappa} = 0.954). Although performance differed among individual models (Cochrans Q = 99.29, p < 0.001), the ensemble did not significantly outperform EfficientNetV2B1 (McNemars p = 0.163). Errors occurred mainly between adjacent anatomical regions. Deep learning can classify craniofacial CT slices accurately and may support automated dataset organization and AI-assisted fracture diagnosis. External validation is needed.",
  "abstract_shown": true,
  "affiliations": [
    "Hyogo Medical University"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "S. Nishimoto"
    },
    {
      "name": "K. Nakajima"
    },
    {
      "name": "H. Yoshiike"
    },
    {
      "name": "H. Ishise"
    },
    {
      "name": "T. Saito"
    },
    {
      "name": "K. Kawai"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/017qnj07vjhkt.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.16.26365710",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.10.16.26365710v1.full",
  "id": "017qnj07vjhkt",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.10.16.26365710v1",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "radiology and imaging"
    ],
    [
      "openalex",
      "2204|Medical Imaging and Analysis"
    ]
  ],
  "openalex": "W7222914285",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.10.16.26365710v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/017qnj07vjhkt.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:10Z",
      "native_id": "10.64898/2026.10.16.26365710",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.10.16.26365710v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:radiology and imaging",
      "subfield": "imaging"
    }
  ],
  "title": "Deep Learning-Based Anatomical Level Classification of Craniofacial CT Slices: Comparison of CNN Architectures and Ensemble Methods",
  "title_text": "Deep Learning-Based Anatomical Level Classification of Craniofacial CT Slices: Comparison of CNN Architectures and Ensemble Methods",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:10Z",
  "url": "https://research.draconitic.com/paper/017qnj07vjhkt",
  "venue": "medRxiv",
  "versions": []
}