Abstract
A constant blood supply to the brain is required for mental function. Research with Doppler ultrasonography has important clinical value and burgeoning potential with machine learning applications in studies predicting gestational age and vascular aging. Critically, studies on ultrasound metrics in school-age children are sparse and no machine learning study to date has used color duplex ultrasonography to predict age and classify age-group. The purpose of our study is two-fold: first to document cerebrovascular hemodynamics considering age, gender, and hemisphere in three arteries; and second to construct machine learning models that can predict and classify the age and age-group of a participant using ultrasonography metrics. We record peak systolic, end-diastolic, and time-averaged maximum velocities bilaterally in internal carotid, vertebral, and middle cerebral arteries from 821 participants. Results confirm that ultrasonography values decrease with age and reveal that gender and hemispheres show more similarities than differences, which depend on age, artery, and metric. Machine learning algorithms predict age and classifier models distinguish cerebrovascular hemodynamics between children and adults. Blood velocities, rather than blood vessel diameters, are more important for classifier models, and common and distinct variables contribute to age classification models for males and females.
Highlights
Cerebrovascular function has a measurable impact on health and cognitive outcomes
As average developmental scores and predictions derived using machine learning algorithms can benefit clinical practice and future research, the purpose of this study is to examine the effects of age, gender, and hemisphere on ultrasound metrics from three major blood vessels in a large sample of children and adults, and model using machine learning approach, find the features that better predict age and classify age-group
Negative relations indicate a decrease in velocities as a function of age, with shared variance ranging from 4.49% in the vertebral arteries to 13.6% in the internal carotid arteries
Summary
Cerebrovascular function has a measurable impact on health and cognitive outcomes. Cerebrovascular hemodynamics rely on various measurements such as vessel diameter (i.e., arterial stenosis), and blood flow velocities (i.e., arterial pressure) [1]. Doppler ultrasonography provides non-invasive, rapid, and real-time values associated with cerebrovascular function and has established utility in clinical practice and research applications [2]. Medical conditions such as hypoxic-ischemic encephalopathy [3] and sickle cell disease. Gender, and hemisphere: Developmental scores and machine learning classifiers additional external funding was received for this study
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