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


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.

Original languageEnglish
Title of host publicationMobiHealth 2020: Wireless Mobile Communication and Healthcare
EditorsJuan Ye, Michael J. O'Grady , Gabriele Civitarese, Kristina Yordanova
Number of pages8
PublisherSpringer, Cham
Publication date2020
ISBN (Print)978-3-030-70568-8
ISBN (Electronic)978-3-030-70569-5
Publication statusPublished - 2020
Event9th EAI International Conference on Wireless Mobile Communication and Healthcare - Virtual Event
Duration: 19.11.202019.11.2020


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