{
  "abstract": "Artificial Intelligence (AI) is transforming clinical dental practice, with most applications focusing on static images like radiographs. However, the integration of AI in analyzing video data, particularly in surgical settings rich in dynamic information, remains underexplored in oral medicine. This study introduces AI-miSMG, an intelligent surgical workflow recognition model designed for minimally invasive submandibular gland (SMG) resection. Building upon our previous work, we established a standardized annotation protocol to efficiently label endoscopic SMG resection procedures, which are divided into Creation, Position, Separation, Inspection, and Idle phases. The AI-miSMG model was trained on a dataset of 73 high-quality endoscopic SMG resection videos, comprising 386,122 labeled frames. To evaluate its performance, we used a multicenter dataset consisting of surgical videos from four different centers, totaling 85,913 images. The model achieved an overall accuracy of 0.87 on the external validation dataset. Additionally, it was explored for workflow-based analysis of surgical fluency across surgeons with different experience levels. Furthermore, our model reduced annotation time by approximately 47%, decreasing from 94.00 min to 49.90 min. Overall, we developed a deep learning-based workflow recognition model for minimally invasive SMG resection and demonstrated its feasibility for surgical phase recognition, workflow analysis, and model-assisted annotation.",
  "abstract_shown": true,
  "affiliations": [
    "State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Head and Neck Oncology West China Hospital of Stomatology, Sichuan University, Chengdu, China",
    "College of Computer Science, Sichuan University, Chengdu, China",
    "Department of Oral and Maxillofacial Surgery, The Second Clinical Medical College of North Sichuan Medical College, Nanchong Central Hospital, Nanchong, China",
    "Nanjing Stomatology Hospital, Medical School of Nanjing University, Nanjing, China",
    "Department of Oral and Maxillofacial Surgery, The Affiliated Hospital of Stomatology, Harbin Medical University, Harbin, China"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 12,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Zhongkai Ma"
    },
    {
      "affs": [
        0
      ],
      "name": "Yufei Hua"
    },
    {
      "affs": [
        0
      ],
      "name": "Lin Que"
    },
    {
      "affs": [
        0
      ],
      "name": "Bing Yan"
    },
    {
      "affs": [
        1
      ],
      "name": "Jialiang Huang"
    },
    {
      "affs": [
        2
      ],
      "name": "Huawei Ming"
    },
    {
      "affs": [
        3
      ],
      "name": "Xiaowei Hong"
    },
    {
      "affs": [
        4
      ],
      "name": "Songlin Piao"
    },
    {
      "affs": [
        1
      ],
      "name": "Chenwei Tang"
    },
    {
      "affs": [
        0
      ],
      "name": "Zhiyong Guo"
    },
    {
      "affs": [
        0
      ],
      "name": "Guiquan Zhu"
    },
    {
      "affs": [
        0
      ],
      "name": "Chunjie Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ahed55s5texvt.bib",
  "cited_by": 0,
  "doi": "10.1038/s41746-026-02976-w",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13720039/",
  "id": "ahed55s5texvt",
  "issns": [
    "2398-6352"
  ],
  "landing_url": "https://doi.org/10.1038/s41746-026-02976-w",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Article"
    ],
    [
      "openalex",
      "2746|Surgical Simulation and Training"
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  "openalex": "W7167609851",
  "pdf_url": "https://www.nature.com/articles/s41746-026-02976-w_reference.pdf",
  "pmcid": "PMC13720039",
  "pmid": "42414567",
  "published": "2026-07-07",
  "publisher": "Nature Publishing Group",
  "ris": "https://research.draconitic.com/paper/ahed55s5texvt.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T12:23:54Z",
      "native_id": "PMC13720039",
      "source": "pmc",
      "url": "https://doi.org/10.1038/s41746-026-02976-w"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2746|Surgical Simulation and Training",
      "subfield": "surgery"
    }
  ],
  "title": "Intelligent surgical workflow recognition-based skill assessment for minimally invasive submandibular gland resection",
  "title_text": "Intelligent surgical workflow recognition-based skill assessment for minimally invasive submandibular gland resection",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T12:21:00Z",
  "url": "https://research.draconitic.com/paper/ahed55s5texvt",
  "venue": "NPJ Digital Medicine",
  "versions": []
}