{
  "abstract": "Purpose\n\nThe purpose of this study was to develop an interpretable, two-stage artificial intelligence (AI) system (fundus fluorescein angiography [FFA]-SegGPT) designed to optimize the clinical workflow of diabetic retinopathy (DR) FFA interpretation and reduce the risk of factual inaccuracies common in end-to-end models.Methods\n\nWe developed a cross-modal system that decouples lesion quantification from report generation. A segmentation module first explicitly segments non-perfusion areas (NPAs) and neovascularization (NV). These segmentation maps then serve as visual constraints to guide an evidence-based report generator, ensuring diagnostic text is strictly grounded in detected lesions.Results\n\nIn a simulated clinical workflow, AI-aided reporting significantly reduced mean interpretation time by 50% (from 8.13 ± 0.47 to 4.09 ± 0.21 minutes, P < 0.001) and improved median report quality (4.0 vs. 3.5, P < 0.001) compared to manual interpretation. Objectively, the model achieved high Dice Similarity Coefficients (NV = 85.35% and NPAs = 86.49%) and maintained high semantic fidelity for report generation (BERTScore = 0.91), with robust generalization demonstrated across three external cohorts.Conclusions\n\nFFA-SegGPT enhances both the efficiency and quality of DR diagnostics. By decoupling lesion quantification from report generation, the system provides a transparent, dual-output mechanism that reduces the risk of factual inaccuracies, offering an interpretable workflow to facilitate safe clinical adoption.Translational relevance\n\nThis interpretable AI system optimizes the clinical workflow of DR by providing verifiable lesion segmentation and automated reporting, potentially reducing clinician workload and standardizing diagnostic quality.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Ophthalmology, The Affiliated Eye Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, People's Republic of China.",
    "Jiangxi Province Key Laboratory of Ophthalmology and Vision Sciences, Jiangxi Clinical Research Center for Ophthalmic Disease, Jiangxi Provincial Key Laboratory of Vitreoretinal Diseases for Health, Nanchang, Jiangxi, People's Republic of China.",
    "School of Mathematics and Computer Sciences, Nanchang University, Nanchang, Jiangxi, People's Republic of China.",
    "The First Affiliated Hospital of Baotou Medical College, Baotou, Inner Mongolia Autonomous Region, People's Republic of China.",
    "Department of Ophthalmology, Gaoxin Branch of the First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People's Republic of China.",
    "Shenshan Medical Center, SunYat-sen Memorial Hospital, Sun Yat-sen University, Shanwei, Guangdong, People's Republic of China.",
    "Jiangxi Provincial Changzheng Hospital, Nanchang, Jiangxi, People's Republic of China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 16,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Fan Gan"
    },
    {
      "affs": [
        0,
        2
      ],
      "name": "Xian Chen"
    },
    {
      "affs": [
        3
      ],
      "name": "Yinan Shao"
    },
    {
      "affs": [
        4
      ],
      "name": "Henghui Li"
    },
    {
      "affs": [
        5
      ],
      "name": "Wanyun Chen"
    },
    {
      "affs": [
        6
      ],
      "name": "Weiguo Qin"
    },
    {
      "affs": [
        2
      ],
      "name": "Xin Long"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Huimin Fan"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Hanying Hu"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Zihan Yu"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Kaiwen Chen"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Nuo Xu"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Liping Chen"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Jian Cao"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Jing Li"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Zhipeng You"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f8da3v80a1scz.bib",
  "cited_by": 0,
  "doi": "10.1167/tvst.15.10.16",
  "fulltext_url": "https://europepmc.org/articles/PMC13676240",
  "id": "f8da3v80a1scz",
  "issns": [
    "2164-2591"
  ],
  "landing_url": "https://europepmc.org/article/MED/42872913",
  "language": "eng",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2741|Retinal Imaging and Analysis"
    ]
  ],
  "openalex": "W7221812807",
  "pdf_url": "https://europepmc.org/articles/PMC13676240?pdf=render",
  "pmcid": "PMC13676240",
  "pmid": "42872913",
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/f8da3v80a1scz.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:16Z",
      "native_id": "MED:42872913",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42872913"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Retinal Imaging and Analysis",
      "subfield": "imaging"
    }
  ],
  "title": "FFA-SegGPT: A Two-Stage Cross-Modal AI System to Optimize the Clinical Workflow of Diabetic Retinopathy FFA Interpretation.",
  "title_text": "FFA-SegGPT: A Two-Stage Cross-Modal AI System to Optimize the Clinical Workflow of Diabetic Retinopathy FFA Interpretation.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:16Z",
  "url": "https://research.draconitic.com/paper/f8da3v80a1scz",
  "venue": "Translational vision science & technology",
  "versions": []
}