{
  "abstract": "Background/Objectives: Reliable longitudinal dietary assessment is a prerequisite for precision nutrition, important for studying nutrition-related changes in health and physical function, but conventional dietary recording can impose substantial user burden. AI-driven, food-image-based assessment can reduce this burden, although uncertainty in food recognition and inter-individual dietary differences may propagate to nutrient estimation. Methods:We developed CP-BayesNE, an uncertainty-aware framework that combines temperature-scaled food classification, conformal candidate sets, training-prior-corrected dietary-history personalization, and posterior-weighted nutrient estimation to support AI-driven personalized nutrition. Results: EfficientNetV2-S achieved 78.01% Top-1 and 93.67% Top-5 accuracy, while temperature scaling reduced expected calibration error from 0.11238 to 0.04345. At α=0.10, regularized adaptive prediction sets reduced the average candidate-set size from 80.78 to 5.77 while maintaining 94.32% empirical category coverage. In FoodLogAthl-218, prior-corrected personalization reduced nutrient density mean absolute error (MAE) by 4.0–6.6% across energy and macronutrients over 51 users. A single-participant 92-meal personal dataset provided preliminary end-to-end feasibility evidence, while also revealing limitations, including slightly worse fat MAE and conservative nutrient intervals. Conclusions: These findings support uncertainty-aware personalized dietary assessment while emphasizing that category-level conformal validity does not directly imply accurate true dietary intake.",
  "abstract_shown": true,
  "affiliations": [
    "Graduate School of Engineering, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe 657-8501, Japan",
    "Graduate School of Medicine, Kobe University, 7-10-2 Tomogaoka, Suma-ku, Kobe 654-0142, Japan",
    "School of Gerontology and Long-Term Care, Taipei Medical University, No. 250, Wuxing St., Taipei 11031, Taiwan",
    "Center for Mathematical and Data Sciences, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe 657-8501, Japan"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Zhaozhen Tong",
      "orcid": "0009-0009-3202-0050"
    },
    {
      "affs": [
        1
      ],
      "name": "Kumiko Ono",
      "orcid": "0000-0003-4185-4339"
    },
    {
      "affs": [
        2
      ],
      "name": "Shu-Chun Lee"
    },
    {
      "affs": [
        2
      ],
      "name": "Hsin-Yen Yen"
    },
    {
      "affs": [
        0,
        3
      ],
      "name": "Masahide Nakamura"
    },
    {
      "affs": [
        0,
        3
      ],
      "name": "Sinan Chen",
      "orcid": "0000-0002-9898-7370"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/29vqb9p24f7q4.bib",
  "cited_by": 0,
  "doi": "10.3390/nu18213567",
  "fulltext_url": null,
  "id": "29vqb9p24f7q4",
  "issns": [
    "2072-6643"
  ],
  "landing_url": "https://www.mdpi.com/2072-6643/18/21/3567",
  "language": null,
  "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": [
    [
      "openalex",
      "1707|Advanced Neural Network Applications"
    ]
  ],
  "openalex": "W7224101529",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-28",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/29vqb9p24f7q4.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:56Z",
      "native_id": "10.3390/nu18213567",
      "source": "crossref",
      "url": "https://www.mdpi.com/2072-6643/18/21/3567"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "issn:2072-6643",
      "subfield": "nutrition"
    }
  ],
  "title": "CP-BayesNE: Uncertainty-Aware Personalized Nutrient-Density Estimation from Food Images Using Conformal Prediction and Prior-Corrected Dietary Adaptation",
  "title_text": "CP-BayesNE: Uncertainty-Aware Personalized Nutrient-Density Estimation from Food Images Using Conformal Prediction and Prior-Corrected Dietary Adaptation",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:56Z",
  "url": "https://research.draconitic.com/paper/29vqb9p24f7q4",
  "venue": "Nutrients",
  "versions": []
}