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Imaging Studies VII: Vascular Imaging01:19

Imaging Studies VII: Vascular Imaging

DefinitionRenal angiography, also known as renal arteriography, is an imaging technique used to obtain a comprehensive view of blood flow and the vascular structure of blood vessels in the kidneys and surrounding areas.PurposeRenal angiography detects blood vessel abnormalities in the kidneys, such as aneurysms, stenosis, thrombosis, vascular tumors, and renal artery stenosis. It evaluates kidney function and guides interventional treatments like angioplasty or stent placement.Pre-Procedure...

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Assessing Machine Learning Models for Predicting Age with Intracranial Vessel Tortuosity and Thickness Information.

Hoon-Seok Yoon1, Jeongmin Oh1, Yoon-Chul Kim1

  • 1Division of Digital Healthcare, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju 26493, Republic of Korea.

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|November 25, 2023
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Summary

Machine learning models predict age using brain vessel features from MRA scans. While showing modest correlation, further studies are needed for clinical application in patients with intracranial vessel diseases.

Keywords:
age predictionfeature extractionintracranial arterymachine learningmagnetic resonance angiographymedical image analysis

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Area of Science:

  • Neuroimaging
  • Medical Artificial Intelligence
  • Biomedical Engineering

Background:

  • Cerebrovascular health assessment is crucial for diagnosing and managing neurological conditions.
  • Aging is associated with changes in intracranial vessel morphology, including tortuosity and diameter.
  • Magnetic Resonance Angiography (MRA) provides detailed 3D visualization of cerebral vasculature.

Purpose of the Study:

  • To develop and validate machine learning (ML) models for predicting chronological age.
  • To investigate the utility of intracranial vessel tortuosity and diameter features derived from MRA for age prediction.
  • To identify key vascular features that contribute to age prediction accuracy.

Main Methods:

  • Extraction of tortuosity (sum of angle metrics, triangular index, relative length, product of angle distance) and diameter features from 3D time-of-flight MRA data of 171 subjects.
  • Training and validation of six ML regression models (including Random Forest and Linear Regression) using features from internal carotid arteries (ICA) and basilar arteries.
  • Four-fold cross-validation was employed to assess model performance.

Main Results:

  • The Random Forest regression model achieved the lowest root mean square error (14.9 years) and highest coefficient of determination (0.186).
  • The Linear Regression model demonstrated the lowest mean absolute percentage error (MAPE) and highest Pearson correlation coefficient (0.532).
  • Mean diameter of the right ICA vessel segment emerged as the most significant feature in two regression models.

Conclusions:

  • ML models utilizing MRA-derived tortuosity and diameter features show a modest correlation between predicted and actual age.
  • The findings suggest potential for non-invasive age estimation using vascular imaging biomarkers.
  • Further research is necessary to evaluate model performance in patient populations with intracranial vessel diseases.