Abstract

The use of contrast agents in CT angiography examinations holds a potential health risk for the patient. Despite this, often unintentionally an excessive contrast agent dose is administered. Our goal is to provide a support system for the medical practitioner that advises to adjust an individually adapted dose. We propose a comparison between different means of feature encoding techniques to gain a higher accuracy when recommending the dose adjustment. We apply advanced deep learning approaches and standard methods like principle component analysis to encode high dimensional parameter vectors in a low dimensional feature space. Our experiments showed that features encoded by a regression neural network provided the best results. Especially with a focus on the 90% precision for the “excessive dose” class meaning that if our system classified a case as “excessive dose” the ground truth is most likely accordingly. With that in mind a recommendation for a lower dose could be administered without the risk of insufficient contrast and therefore a repetition of the CT angiography examination. In conclusion we showed that Deep-Learning-based feature encoding on clinical parameters is advantageous for our aim to prevent excessive contrast agent doses.
OriginalspracheDeutsch
TitelLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Seitenumfang8
Herausgeber (Verlag)Springer Science and Business Media Deutschland GmbH
Erscheinungsdatum2021
Seiten315-322
ISBN (Print)9783030705688
DOIs
PublikationsstatusVeröffentlicht - 2021

Strategische Forschungsbereiche und Zentren

  • Forschungsschwerpunkt: Biomedizintechnik
  • Zentren: Zentrum für Künstliche Intelligenz Lübeck (ZKIL)

DFG-Fachsystematik

  • 205-30 Radiologie, Nuklearmedizin, Strahlentherapie, Strahlenbiologie
  • 205-07 Medizininformatik und medizinische Bioinformatik
  • 205-32 Medizinische Physik, Biomedizinische Technik
  • 409-05 Interaktive und intelligente Systeme, Bild- und Sprachverarbeitung, Computergraphik und Visualisierung

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