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Related Concept Videos

Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

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Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
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Related Experiment Video

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Assessing the Accuracy of an Artificial Intelligence-Based Segmentation Algorithm for the Thoracic Aorta in Computed

Christoph Artzner1, Malte N Bongers1, Rainer Kärgel2

  • 1Department for Diagnostic and Interventional Radiology, University Hospital Tuebingen, Eberhard Karls University Tuebingen, 72076 Tubingen, Germany.

Diagnostics (Basel, Switzerland)
|July 27, 2022
PubMed
Summary

An AI algorithm accurately measures thoracic aorta (TA) diameters on CT scans, showing high agreement with radiologists. This tool demonstrates significant potential for rapid clinical evaluation of aortic conditions.

Keywords:
artificial intelligencedimensional measurement accuracysoftwarespiral computed tomographythoracic aorta

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Cardiovascular Imaging

Background:

  • Accurate measurement of the thoracic aorta (TA) is crucial for diagnosing aortic pathology.
  • Manual segmentation and diameter measurement of the TA on CT scans can be time-consuming and subject to inter-observer variability.

Purpose of the Study:

  • To evaluate the accuracy and reliability of a novel artificial intelligence (AI)-based algorithm for automated TA segmentation and diameter measurement using CT.
  • To compare the AI algorithm's measurements against those of experienced radiologists.

Main Methods:

  • Retrospective analysis of 122 dual-source CT scans, with 93 including contrast.
  • Automated segmentation and diameter measurement of the TA by the AI prototype according to American Heart Association guidelines.
  • Reference standard established by two independent, blinded radiologists; inter-reader agreement assessed using intra-class correlation (ICC).

Main Results:

  • The AI algorithm achieved 99.2% assessability for TA parameters, with minor failures in 9 patients.
  • No significant difference was found between AI and radiologist measurements (p > 0.05), establishing measurement equivalence.
  • Excellent inter-reader agreement was observed between the AI and radiologists (ICC ≥ 0.961) and between radiologists (ICC ≥ 0.879).

Conclusions:

  • The AI-based algorithm demonstrates high accuracy and reliability in measuring TA diameters, comparable to expert radiologists.
  • The algorithm's performance is independent of contrast use or underlying pathology, highlighting its robustness.
  • This AI tool shows substantial potential for efficient and accurate clinical assessment of aortic diseases.