Wireless Mobile Communication and Healthcare. 9th EAI International Conference, MobiHealth 2020, Virtual Event, November 19, 2020, Proceedings

Research Article

Deep-Learning-Based Feature Encoding of Clinical Parameters for Patient Specific CTA Dose Optimization

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  • @INPROCEEDINGS{10.1007/978-3-030-70569-5_20,
        author={Marja Fleitmann and Hristina Uzunova and Andreas Martin Stroth and Jan Gerlach and Alexander F\'{y}rschke and J\o{}rg Barkhausen and Arpad Bischof and Heinz Handels},
        title={Deep-Learning-Based Feature Encoding of Clinical Parameters for Patient Specific CTA Dose Optimization},
        proceedings={Wireless Mobile Communication and Healthcare. 9th EAI International Conference, MobiHealth 2020, Virtual Event, November 19, 2020, Proceedings},
        proceedings_a={MOBIHEALTH},
        year={2021},
        month={7},
        keywords={Feature encoding Deep Learning Case-based reasoning Contrast agent},
        doi={10.1007/978-3-030-70569-5_20}
    }
    
  • Marja Fleitmann
    Hristina Uzunova
    Andreas Martin Stroth
    Jan Gerlach
    Alexander Fürschke
    Jörg Barkhausen
    Arpad Bischof
    Heinz Handels
    Year: 2021
    Deep-Learning-Based Feature Encoding of Clinical Parameters for Patient Specific CTA Dose Optimization
    MOBIHEALTH
    Springer
    DOI: 10.1007/978-3-030-70569-5_20
Marja Fleitmann1, Hristina Uzunova1, Andreas Martin Stroth2, Jan Gerlach2, Alexander Fürschke2, Jörg Barkhausen2, Arpad Bischof2, Heinz Handels1
  • 1: University of Lübeck
  • 2: UKSH Lübeck

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.