{
  "abstract": "Background: Left Atrial Appendage (LAA) causes a thrombus risk in patients with atrial fibrillation (AF). Accurate identification of LAA morphology reduces thrombotic stroke. Methods: This study proposes a hybrid deep learning framework, Left Atrial Appendage Thrombus Framework (LAATF), which integrates Adaptive CLAHE, optimized Chan-Vese contour extraction, and a multi-headed AgileFormer transformer to classify LAA morphology, detect thrombus, and estimate image-derived thromboembolic risk using the LightGBM-based proposed Thromboembolic Risk index (TRI) score. A dataset of 4320 temporal images from 240 patients with AF was used, comprising four LAA morphologies (chicken-wing, windsock, cactus, and cauliflower). CLAHE enhanced local contrast. The Chan-Vese model delineated appendage contours. AgileFormer extracted image-derived textural and morphological features to classify morphology and thrombus presence. LightGBM-based TRI regression estimated thromboembolic risk in AF patients with LAA. Results: The proposed framework achieved a morphology classification accuracy of 91-94% and thrombus detection AUC of 0.935, with a sensitivity of 93.4% and specificity of 92.6%. The computed TRI correlated strongly with actual outcomes (R² = 0.98, r = 0.99). Among morphologies, cauliflower and cactus types show the highest thromboembolic risk due to their increased texture entropy and image-based features. Conclusions: The LAATF framework provides an interpretable, image-based technique for LAA morphology assessment and thrombus prediction.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Electrical and Electronics Engineering, Saveetha Engineering College, Chennai 602105, India.",
    "Department of Computer Science Engineering, Sriram Engineering College, Chennai 602024, India."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Subramanian Jayakrishnan"
    },
    {
      "affs": [
        0
      ],
      "name": "Jeyakumar Anish Kumar"
    },
    {
      "affs": [
        1
      ],
      "name": "Nagalingam Rajendiran Shanker"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ek8zsr20jf6ay.bib",
  "cited_by": 0,
  "doi": "10.3390/diagnostics16193154",
  "fulltext_url": "https://europepmc.org/articles/PMC13664605",
  "id": "ek8zsr20jf6ay",
  "issns": [
    "2075-4418"
  ],
  "landing_url": "https://europepmc.org/article/MED/42882310",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2705|Atrial Fibrillation Management and Outcomes"
    ]
  ],
  "openalex": "W7214619363",
  "pdf_url": "https://europepmc.org/articles/PMC13664605?pdf=render",
  "pmcid": "PMC13664605",
  "pmid": "42882310",
  "published": "2026-09-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ek8zsr20jf6ay.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:16Z",
      "native_id": "MED:42882310",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42882310"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2705|Atrial Fibrillation Management and Outcomes",
      "subfield": "cardiovascular"
    }
  ],
  "title": "Echocardiographic Framework for Left Atrial Appendage Morphology Assessment and Thromboembolic Risk Stratification in Atrial Fibrillation.",
  "title_text": "Echocardiographic Framework for Left Atrial Appendage Morphology Assessment and Thromboembolic Risk Stratification in Atrial Fibrillation.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:16Z",
  "url": "https://research.draconitic.com/paper/ek8zsr20jf6ay",
  "venue": "Diagnostics (Basel, Switzerland)",
  "versions": []
}