CC BY-NC-ND 4.0 · Methods Inf Med 2024; 63(01/02): 011-020
DOI: 10.1055/s-0044-1778694
Original Article

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

Marja Fleitmann
1   Artificial Intelligence in Medical Imaging, German Research Center for Artificial Intelligence, Kaiserslautern, Germany
,
Hristina Uzunova
1   Artificial Intelligence in Medical Imaging, German Research Center for Artificial Intelligence, Kaiserslautern, Germany
,
René Pallenberg
2   Institute for Signal Processing, University of Lübeck, Schleswig-Holstein, Germany
,
Andreas M. Stroth
3   Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein (UKSH) Lübeck, Lübeck, Germany
,
Jan Gerlach
3   Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein (UKSH) Lübeck, Lübeck, Germany
,
Alexander Fürschke
3   Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein (UKSH) Lübeck, Lübeck, Germany
,
Jörg Barkhausen
3   Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein (UKSH) Lübeck, Lübeck, Germany
,
Arpad Bischof
3   Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein (UKSH) Lübeck, Lübeck, Germany
4   IMAGE Information Systems Europe, Rostock, Germany
,
Heinz Handels
1   Artificial Intelligence in Medical Imaging, German Research Center for Artificial Intelligence, Kaiserslautern, Germany
5   Institute of Medical Informatics, University of Lübeck, Schleswig-Holstein, Germany
› Institutsangaben
Funding This work was funded by the German Federal Ministry of Education and Research.

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.



Publikationsverlauf

Eingereicht: 26. April 2022

Angenommen: 28. November 2023

Artikel online veröffentlicht:
23. Januar 2024

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 
  • References

  • 1 Middleton B, Sittig DF, Wright A. Clinical decision support: a 25 year retrospective and a 25 year vision. Yearb Med Inform 2016; 25 (Suppl. 01) S103-S116
  • 2 Peiffer-Smadja N, Rawson TM, Ahmad R. et al. Machine learning for clinical decision support in infectious diseases: a narrative review of current applications. Clin Microbiol Infect 2020; 26 (05) 584-595
  • 3 Li Z, Liu F, Yang W, Peng S, Zhou J. A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Trans Neural Netw Learn Syst 2021
  • 4 Abràmoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med 2018; 1 (01) 39
  • 5 Kingma DP, Welling M. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013.
  • 6 Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995; 20 (03) 273-297
  • 7 Tolles J, Meurer WJ. Logistic regression: relating patient characteristics to outcomes. JAMA 2016; 316 (05) 533-534
  • 8 Mani S, Ozdas A, Aliferis C. et al. Medical decision support using machine learning for early detection of late-onset neonatal sepsis. J Am Med Inform Assoc 2014; 21 (02) 326-336
  • 9 Pannu N, Wiebe N, Tonelli M. Alberta Kidney Disease Network. Prophylaxis strategies for contrast-induced nephropathy. JAMA 2006; 295 (23) 2765-2779
  • 10 Mourits MM, Nijhof WH, van Leuken MH, Jager GJ, Rutten MJ. Reducing contrast medium volume and tube voltage in CT angiography of the pulmonary artery. Clin Radiol 2016; 71 (06) 615.e7-615.e13
  • 11 Szucs-Farkas Z, Schibler F, Cullmann J. et al. Diagnostic accuracy of pulmonary CT angiography at low tube voltage: intraindividual comparison of a normal-dose protocol at 120 kVp and a low-dose protocol at 80 kVp using reduced amount of contrast medium in a simulation study. AJR Am J Roentgenol 2011; 197 (05) W852-9
  • 12 Seifarth H, Puesken M, Kalafut JF. et al. Introduction of an individually optimized protocol for the injection of contrast medium for coronary CT angiography. Eur Radiol 2009; 19 (10) 2373-2382
  • 13 Jin L, Gao Y, Sun Y. et al. Contrast medium administration with a body surface area protocol in step-and-shoot coronary computed tomography angiography with dual-source scanners. Sci Rep 2020; 10 (01) 16690
  • 14 Bae KT. Intravenous contrast medium administration and scan timing at CT: considerations and approaches. Radiology 2010; 256 (01) 32-61
  • 15 Pallenberg R, Fleitmann M, Soika K. et al. Automatic quality measurement of aortic contrast-enhanced CT angiographies for patient-specific dose optimization. Int J CARS 2020; 15 (10) 1611-1617
  • 16 Fleitmann M, Soika K, Stroth AM. et al. Computer-assisted quality assessment of aortic CT angiographies for patient-individual dose adjustment. Stud Health Technol Inform 2020; 270: 123-127
  • 17 Campillo-Gimenez B, Jouini W, Bayat S, Cuggia M. Improving case-based reasoning systems by combining k-nearest neighbour algorithm with logistic regression in the prediction of patients' registration on the renal transplant waiting list. PLoS One 2013; 8 (09) e71991
  • 18 Heo J, Yoon JG, Park H, Kim YD, Nam HS, Heo JH. Machine learning–based model for prediction of outcomes in acute stroke. Stroke 2019; 50 (05) 1263-1265
  • 19 Yang L, Wu H, Jin X. et al. Study of cardiovascular disease prediction model based on random forest in eastern China. Sci Rep 2020; 10 (01) 5245
  • 20 Kamel H, Navi BB, Parikh NS. et al. Machine learning prediction of stroke mechanism in embolic strokes of undetermined source. Stroke 2020; 51 (09) e203-e210
  • 21 Ganggayah MD, Taib NA, Har YC, Lio P, Dhillon SK. Predicting factors for survival of breast cancer patients using machine learning techniques. BMC Med Inform Decis Mak 2019; 19 (01) 48
  • 22 Wong NC, Lam C, Patterson L, Shayegan B. Use of machine learning to predict early biochemical recurrence after robot-assisted prostatectomy. BJU Int 2019; 123 (01) 51-57
  • 23 Fleitmann M, Uzunova H, Stroth AM. , et al. Deep-learning-based feature encoding of clinical parameters for patient specific CTA dose optimization. In: International Conference on Wireless Mobile Communication and Healthcare 2020; 315–322
  • 24 Behrendt FF, Rebière M, Goedicke A. et al. Contrast medium injection protocol adjusted for body surface area in combined PET/CT. Eur Radiol 2013; 23 (07) 1970-1977
  • 25 Breiman L. Random forests. Mach Learn 2001; 45: 5-32
  • 26 Shannon CE. A mathematical theory of communication. Bell Syst Tech J 1948; 27: 379-423
  • 27 Cover T, Hart P. Nearest neighbor pattern classification. IEEE Trans Inf Theory 1967; 13: 21-27
  • 28 Lai Z, Deng H. Medical image classification based on deep features extracted by deep model and statistic feature fusion with multilayer perceptron. Comput Intell Neurosci 2018; 2018: 2061516