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

Abstract

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.

Fig. 1

Application of the system before a computed tomography angiography examination. AI, artificial intelligence; GFR, glomerular filtration rate.

Fig. 2

Determine the quality of the image contrast through ROIs. HU, Hounsfield Unit; ROI, region of interest.

Fig. 3

Cumulative occurrence of every clinical parameter in the top 100 model parameter. A, age; ABI, ankle brachial index; BB, beta blocker; BMI, body mass index; BPD, blood pressure diastolic; BPD0, blood pressure diastolic, directly before contrast medium; BPD5, blood pressure diastolic, 5 min before contrast medium; BPS, blood pressure systolic; BPS0, blood pressure systolic, directly before contrast medium; BPS5, blood pressure systolic, 5 min before contrast medium; C, creatinine; G, gender; GFR, glomerular filtration rate; GGT, gamma-glutamyl transferase; H, height; HB, hemoglobin; HC, hematocrit; OS, oxygen saturation; P0, pulse directly before contrast medium; P5, pulse, 5 min before contrast medium; W, weight; WS, waist size.

Fig. 4

Impurity- and permutation-based feature importance for each clinical parameter. A, age; ABI, ankle brachial index; BB, beta blocker; BMI, body mass index; BPD, blood pressure diastolic; BPD0, blood pressure diastolic, directly before contrast medium; BPD5, blood pressure diastolic, 5 min before contrast medium; BPS, blood pressure systolic; BPS0, blood pressure systolic, directly before contrast medium; BPS5, blood pressure systolic, 5 min before contrast medium; C, creatinine; G, gender; GFR, glomerular filtration rate; GGT, gamma-glutamyl transferase; H, height; HB, hemoglobin; HC, hematocrit; OS, oxygen saturation; P0, pulse directly before contrast medium; P5, pulse, 5 min before contrast medium; W, weight; WS, waist size.