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Publication

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

Marja Fleitmann; Hristina Uzunova; Andreas Martin Stroth; Jan Gerlach; Alexander Fürschke; Jörg Barkhausen; Arpad Bischof; Heinz Handels
In: Juan Ye; Michael J. O'Grady; Gabriele Civitarese; Kristina Yordanova (Hrsg.). Proceedings of the 10th EAI International Conference on Wireless Mobile Communication and Healthcare. International Conference on Wireless Mobile Communication and Healthcare (MobiHealth-2021), November 13-14, Chongqing/Virtual, China, Pages 315-322, ISBN 978-3-030-70569-5, Springer International Publishing, 2021.

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.