Affiliation:
1. Division of Digital Healthcare, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju 26493, Republic of Korea
Abstract
This study aimed to develop and validate machine learning (ML) models that predict age using intracranial vessels’ tortuosity and diameter features derived from magnetic resonance angiography (MRA) data. A total of 171 subjects’ three-dimensional (3D) time-of-flight MRA image data were considered for analysis. After annotations of two endpoints in each arterial segment, tortuosity features such as the sum of the angle metrics, triangular index, relative length, and product of the angle distance, as well as the vessels’ diameter features, were extracted and used to train and validate the ML models for age prediction. Features extracted from the right and left internal carotid arteries (ICA) and basilar arteries were considered as the inputs to train and validate six ML regression models with a four-fold cross validation. The random forest regression model resulted in the lowest root mean square error of 14.9 years and the highest average coefficient of determination of 0.186. The linear regression model showed the lowest average mean absolute percentage error (MAPE) and the highest average Pearson correlation coefficient (0.532). The mean diameter of the right ICA vessel segment was the most important feature contributing to prediction of age in two out of the four regression models considered. An ML of tortuosity descriptors and diameter features extracted from MRA data showed a modest correlation between real age and ML-predicted age. Further studies are warranted for the assessment of the model’s age predictions in patients with intracranial vessel diseases.
Funder
National Research Foundation of Korea
Reference47 articles.
1. Guillén, M.F. (2020). 2030: How Today’s Biggest Trends Will Collide and Reshape the Future of Everything, St. Martin’s Press.
2. Baecker, L., Garcia-Dias, R., Vieira, S., Scarpazza, C., and Mechelli, A. (2021). Machine learning for brain age prediction: Introduction to methods and clinical applications. eBioMedicine, 72.
3. MacDonald, M.E., and Pike, G.B. (2021). MRI of healthy brain aging: A review. NMR Biomed., 34.
4. Uncertainty estimation and explainability in deep learning-based age estimation of the human brain: Results from the German National Cohort MRI study;Hepp;Comput. Med. Imaging Graph.,2021
5. Age Prediction Based on Brain MRI Image: A Survey;Sajedi;J. Med. Syst.,2019