{
  "abstract": "Purpose: Deep Learning (DL) has transformed cardiac image segmentation, yet its application to congenital heart disease (CHD) remains underexplored, with no prior systematic review in this emerging domain. This review evaluates current DL approaches for CHD CT segmentation, identifies best performing model architectures, assesses quality of reporting and highlights challenges for clinical implementation. Materials and methods: Following PRISMA guidelines, six databases: PubMed, IEEE Xplore, MDPI, Science Direct, Web of Science, and Scopus were searched (2004–2025). Extracted characteristics include source of dataset, ground truth labelling approach, DL architecture and evaluation methods. Reporting quality was evaluated using the Checklist for Artificial Intelligence in Medical imaging (CLAIM). Results: 14 studies met the inclusion criteria, reflecting the emergence of this research area. The UNet architecture and its variants dominated (8 studies). The highest reported Dice Similarity Coefficient (DSC) for cardiac structures segmentation was achieved utilising an advanced hybrid ResNet-based model (Aorta, DSC = 0.945). Well documented areas include the training approach (12 studies) and model description (all). Only two studies performed external validation, and two assessed inter- and intra-rater variability. No studies reported sample size determination and handing of missing data. Small vascular structures consistently underperformed compared to whole heart segmentation. Conclusion: Deep learning models show strong potential for CHD CT segmentation but remain limited by small datasets, inconsistent reporting and lack of clinical evaluation. Advancing this field requires multidisciplinary collaboration, standardised reporting, and integration of clinical co- design. As the first comprehensive review in this area, we provide a strong baseline for evaluating future studies.",
  "abstract_shown": true,
  "affiliations": [
    "Engineering for Health Research Centre, Aston University, Aston Triangle, Birmingham, United Kingdom",
    "Aston Institute of Health & Neurodevelopment, Aston University, Aston Triangle, Birmingham, United Kingdom",
    "School of Computer Science and Digital Technologies, Aston University, Aston Triangle, Birmingham, United Kingdom"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Sinling Tiffany Yu"
    },
    {
      "affs": [
        1
      ],
      "name": "Zahra Amini"
    },
    {
      "affs": [
        2
      ],
      "name": "Shereen Fouad"
    },
    {
      "name": "Jan Novak"
    },
    {
      "name": "Antonio Fratini"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/31n4fh7qtpes2.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0357600",
  "fulltext_url": null,
  "id": "31n4fh7qtpes2",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0357600",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1707|Medical Image Segmentation Techniques"
    ],
    [
      "plos",
      "/Biology and life sciences/Anatomy/Cardiovascular anatomy/Heart"
    ],
    [
      "plos",
      "/Biology and life sciences/Anatomy/Cardiovascular anatomy/Heart/Cardiac ventricles"
    ],
    [
      "plos",
      "/Biology and life sciences/Developmental biology/Morphogenesis/Birth defects/Congenital heart defects"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Neuroimaging/Computed axial tomography"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning/Deep learning"
    ],
    [
      "plos",
      "/Medicine and health sciences/Anatomy/Cardiovascular anatomy/Heart"
    ],
    [
      "plos",
      "/Medicine and health sciences/Anatomy/Cardiovascular anatomy/Heart/Cardiac ventricles"
    ],
    [
      "plos",
      "/Medicine and health sciences/Cardiology/Cardiovascular medicine/Cardiovascular diseases/Congenital heart defects"
    ],
    [
      "plos",
      "/Medicine and health sciences/Diagnostic medicine/Diagnostic radiology/Tomography/Computed axial tomography"
    ],
    [
      "plos",
      "/Medicine and health sciences/Medical conditions/Cardiovascular diseases/Congenital heart defects"
    ],
    [
      "plos",
      "/Medicine and health sciences/Medical conditions/Congenital disorders/Birth defects/Congenital heart defects"
    ],
    [
      "plos",
      "/Medicine and health sciences/Radiology and imaging/Diagnostic radiology/Tomography/Computed axial tomography"
    ],
    [
      "plos",
      "/People and places/Population groupings/Professions/Medical personnel/Radiologists"
    ],
    [
      "plos",
      "/Research and analysis methods/Imaging techniques/Diagnostic radiology/Tomography/Computed axial tomography"
    ],
    [
      "plos",
      "/Research and analysis methods/Imaging techniques/Neuroimaging/Computed axial tomography"
    ],
    [
      "plos",
      "/Research and analysis methods/Research assessment/Systematic reviews"
    ]
  ],
  "openalex": "W7214750564",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0357600&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-29",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/31n4fh7qtpes2.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:37Z",
      "native_id": "10.1371/journal.pone.0357600",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0357600"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "plos:cardiovascular diseases",
      "subfield": "cardiovascular"
    },
    {
      "field": "medicine",
      "origin": "plos:diagnostic radiology",
      "subfield": "imaging"
    },
    {
      "field": "medicine",
      "origin": "plos:anatomy",
      "subfield": "physiology"
    }
  ],
  "title": "Deep learning for cardiac CT segmentation for congenital heart disease: A systematic review",
  "title_text": "Deep learning for cardiac CT segmentation for congenital heart disease: A systematic review",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:37Z",
  "url": "https://research.draconitic.com/paper/31n4fh7qtpes2",
  "venue": "PLOS One",
  "versions": []
}