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Evaluating the Performance of a Convolutional Neural Network Algorithm for Measuring Thoracic Aortic Diameters in a

Caterina B Monti1, Marly van Assen1, Arthur E Stillman1

  • 1Division of Cardiothoracic Imaging, Nuclear Medicine and Molecular Imaging, Department of Radiology and Imaging Sciences, Emory University Hospital, 1364 Clifton Rd NE, Atlanta, GA 30322 (C.B.M., M.v.A., A.E.S., S.J.L., C.N.D.C.); Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy (C.B.M., F. Secchi, F. Sardanelli); Digital Health Imaging Decision Support, Siemens Healthineers, Princeton, NJ (P.H.); Computed Tomography, Siemens Healthineers, Malvern, Pa (G.S.K.F.); and Unit of Radiology, Istituto di Ricovero e Cura a Carattere Scientifico Policlinico San Donato, San Donato Milanese, Italy (F. Secchi, F. Sardanelli).

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A convolutional neural network (CNN) accurately measures thoracic aortic diameters in diverse CT scans. This AI tool shows good performance across various patient conditions and imaging equipment.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Cardiovascular Imaging

Background:

  • Accurate thoracic aorta measurements are crucial for diagnosing and managing cardiovascular diseases.
  • Manual measurements can be time-consuming and subject to inter-observer variability.
  • Automated measurement tools using artificial intelligence show promise in improving efficiency and consistency.

Purpose of the Study:

  • To evaluate the performance of a convolutional neural network (CNN) for automated thoracic aortic diameter measurements.
  • To assess the accuracy and reproducibility of the CNN compared to manual measurements in a heterogeneous population.
  • To identify factors influencing the performance of the automated measurement system.

Main Methods:

  • Retrospective analysis of 233 chest CT scans from a heterogeneous population.
  • Manual and CNN-based (Artificial Intelligence Rad Companion Chest CT prototype, Siemens Healthineers) measurements of aortic diameters at nine locations and maximum diameter.
  • Bland-Altman analysis to assess agreement between manual and automatic measurements.

Main Results:

  • No significant difference was observed in maximum aortic diameter between manual and automatic measurements (P = .48).
  • Overall measurements showed a bias of -1.5 mm and a coefficient of repeatability of 8.0 mm.
  • Factors such as contrast enhancement, aortic pathology (dissection, repair), and positioning affected reproducibility.

Conclusions:

  • The CNN demonstrated good performance in measuring thoracic aortic diameters within a heterogeneous, multivendor CT dataset.
  • Automated measurements offer a viable alternative to manual measurements, with potential for improved efficiency.
  • Further refinement may be needed to address variability in specific patient subgroups and imaging conditions.