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Artificial Intelligence for Detecting Aortic Arch Calcification on Chest Radiographs: A Systematic Review.

Krzysztof Żerdziński1,2, Julita Janiec1,2, Maja Dreger1,3

  • 1Student Scientific Association of Computer Analysis and Artificial Intelligence, Department of Radiology and Nuclear Medicine, Medical University of Silesia, 40-752 Katowice, Poland.

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Summary
This summary is machine-generated.

Artificial intelligence (AI) models can effectively detect aortic arch calcification (AAC) on chest X-rays (CXRs) for cardiovascular risk assessment. However, variations in AI model design and validation need standardization for widespread clinical use.

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aortic arch calcificationartificial intelligencecardiovascular riskchest radiographyconvolutional neural networksdeep learning

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Cardiovascular Disease

Background:

  • Aortic arch calcification (AAC) on chest radiographs (CXRs) is a significant cardiovascular event predictor.
  • AAC is frequently overlooked in routine radiological assessments.
  • Artificial intelligence (AI) offers potential for automated AAC detection.

Purpose of the Study:

  • To systematically review the diagnostic accuracy of AI models for detecting AAC on CXRs.
  • To assess the clinical implementation potential of AI-driven AAC detection.

Main Methods:

  • Systematic review following PRISMA 2020 guidelines.
  • Searched Embase, PubMed, Scopus, and Web of Science (Jan 2020-Oct 2025).
  • Included retrospective studies using Convolutional Neural Networks (CNNs) on large datasets; bias assessed with QUADAS-2.

Main Results:

  • Three studies (2022-2024) involving ~2.7 million images were analyzed.
  • AI models showed high diagnostic discrimination (AUROC 0.81-0.99), with performance varying in external cohorts.
  • Significant sensitivity-specificity trade-offs were observed; GRADE certainty was low due to heterogeneity and lack of cross-sectional imaging standards.

Conclusions:

  • Deep learning models show promise for reliable AAC detection on CXRs, enabling opportunistic cardiovascular risk stratification.
  • Heterogeneity in AI model architectures and validation hinders broad comparability.
  • Standardized annotation and external validation are crucial for clinical generalizability of AI tools.