{
  "abstract": "This study aimed to develop and validate an automated framework for detecting pleural plaques (PPs) at the patient level using a single chest CT scan. Methods: A database of chest CT scans with corresponding individual binary annotations for PP presence was first established through expert visual assessment, based on consensus among a board of specialized radiologists. This dataset served to train and validate a patient-level classification framework for automated PP presence detection. This framework leverages a pre-trained PP segmentation network, thus obviating the need for retraining large-scale deep learning models. This segmentation backbone is supplemented by a lightweight classification module that integrates the network’s outputs to infer the presence or absence of PPs at patient level. Furthermore, a patch-wise processing strategy was employed to optimize computational efficiency and training time while retaining critical local contextual information. Results: The framework was evaluated on a cohort of 1241 retired workers with documented occupational asbestos exposure, using 10-fold cross-validation (30% training, 10% validation, 60% testing). It achieved a balanced accuracy of 97.0%, sensitivity of 96.6%, and specificity of 97.4%, demonstrating performance slightly superior to that of two expert radiologists (balanced accuracy: 90.9%/91.4%, sensitivity: 88.0%/88.8%, specificity: 93.9%/94.1% for Expert1/Expert2) and substantially superior to that of a radiologist without specialized expertise in asbestos-related manifestations (balanced accuracy: 80.0%, sensitivity: 75.1%, specificity: 84.8%). Conclusion: The proposed framework demonstrates strong potential for automated patient-level identification of pleural plaques from a single CT scan. Our approach could be particularly useful in screening settings that involve compensation claims related to occupational diseases.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 14,
  "authors": [
    {
      "name": "Yannis Petitpas"
    },
    {
      "name": "Ilyes Benlala"
    },
    {
      "name": "Fabien Baldacci"
    },
    {
      "name": "Gaël Dournes"
    },
    {
      "name": "Kellian Jaunas"
    },
    {
      "name": "Bénédicte Clin"
    },
    {
      "name": "Patrick Brochard"
    },
    {
      "name": "M. Antoine Gislard"
    },
    {
      "name": "Fleur Delva"
    },
    {
      "name": "Aude Lacourt"
    },
    {
      "name": "Christophe Paris"
    },
    {
      "name": "François Laurent"
    },
    {
      "name": "Jean-Claude Pairon"
    },
    {
      "name": "Baudouin Denis de Senneville"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/129gmd84xdp90.bib",
  "cited_by": 0,
  "doi": "10.3390/diagnostics16162519",
  "fulltext_url": null,
  "id": "129gmd84xdp90",
  "issns": [
    "2075-4418"
  ],
  "landing_url": "https://hal.science/hal-05748869v1",
  "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": [
    [
      "hal",
      "0.sdv"
    ],
    [
      "openalex",
      "2740|Occupational and environmental lung diseases"
    ]
  ],
  "openalex": "W7202092862",
  "pdf_url": "https://hal.science/hal-05748869/document",
  "pmcid": null,
  "pmid": "42650921",
  "published": "2026-10-19",
  "publisher": "MDPI",
  "ris": "https://research.draconitic.com/paper/129gmd84xdp90.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:59Z",
      "native_id": "hal-05748869",
      "source": "hal",
      "url": "https://hal.science/hal-05748869v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "hal:0.sdv",
      "subfield": "general"
    }
  ],
  "title": "AI-Driven Automated Detection of Pleural Plaques on Chest CT Scans in Retired Asbestos-Exposed Workers",
  "title_text": "AI-Driven Automated Detection of Pleural Plaques on Chest CT Scans in Retired Asbestos-Exposed Workers",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:59Z",
  "url": "https://research.draconitic.com/paper/129gmd84xdp90",
  "venue": "Diagnostics",
  "versions": []
}