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

Fleitmann, Marja; Uzunova, Hristina; Stroth, A; Gerlach, J; Fürschke, Alexander; Barkhausen, Jörg; Bischof, Arpad; Handels, Heinz

doi:10.1007/978-3-030-70569-5_20

Cited by 1 publication

(2 citation statements)

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“…Additionally, a feature transformation was implemented for which a RNN was trained that performed favorably in our previous work. 23 As input for the feature transformation the optimized parameter sets as well as the entire set as comparison was used.…”

Section: Methods and Experimentsmentioning

confidence: 99%

“…Deep learning methods like autoencoders are often used for feature transformation or compression and have the disadvantage of not considering the problem-specific properties of the task during training. 23 To avoid this, RNNs have already been considered as a task-specific means for feature transformation. 28 The basic structure of the neural networks is formed by successive layers that contain weights that are applied to their respective input until a task-specific loss function is calculated in the last layer.…”

Section: Regression Neural Networkmentioning

confidence: 99%

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Artificial Intelligence-Based Prediction of Contrast Medium Doses for Computed Tomography Angiography Using Optimized Clinical Parameter Sets

Fleitmann,

Uzunova,

Pallenberg

et al. 2024

Methods Inf Med

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Objectives In this paper, an artificial intelligence-based algorithm for predicting the optimal contrast medium dose for computed tomography (CT) angiography of the aorta is presented and evaluated in a clinical study. The prediction of the contrast dose reduction is modelled as a classification problem using the image contrast as the main feature. Methods This classification is performed by random decision forests (RDF) and k-nearest-neighbor methods (KNN). For the selection of optimal parameter subsets all possible combinations of the 22 clinical parameters (age, blood pressure, etc.) are considered using the classification accuracy and precision of the KNN classifier and RDF as quality criteria. Subsequently, the results of the evaluation were optimized by means of feature transformation using regression neural networks (RNN). These were used for a direct classification based on regressed Hounsfield units as well as preprocessing for a subsequent KNN classification. Results For feature selection, an RDF model achieved the highest accuracy of 84.42% and a KNN model achieved the best precision of 86.21%. The most important parameters include age, height, and hemoglobin. The feature transformation using an RNN considerably exceeded these values with an accuracy of 90.00% and a precision of 97.62% using all 22 parameters as input. However, also the feasibility of the parameter sets in routine clinical practice has to be considered, because some of the 22 parameters are not measured in routine clinical practice and additional measurement time of 15 to 20 minutes per patient is needed. Using the standard feature set available in clinical routine the best accuracy of 86.67% and precision of 93.18% was achieved by the RNN. Conclusion We developed a reliable hybrid system that helps radiologists determine the optimal contrast dose for CT angiography based on patient-specific parameters.

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Section: Methods and Experimentsmentioning

confidence: 99%

Section: Regression Neural Networkmentioning

confidence: 99%