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Related Concept Videos

Blood Flow01:29

Blood Flow

Blood is pumped by the heart into the aorta, the largest artery in the body, and then into increasingly smaller arteries, arterioles, and capillaries. The velocity of blood flow decreases with increased cross-sectional blood vessel area. As blood returns to the heart through venules and veins, its velocity increases. The movement of blood is encouraged by smooth muscle in the vessel walls, the movement of skeletal muscle surrounding the vessels, and one-way valves that prevent backflow.
Equipments Used To Measure Blood Pressure01:30

Equipments Used To Measure Blood Pressure

Direct Method
This invasive approach involves cannulating a peripheral artery. During each cardiac contraction, pressure generates mechanical motion within the catheter, transmitted through rigid, fluid-filled tubing to a transducer. This transducer converts mechanical motion into electrical signals displayed as waveforms on a monitor. An automatic flushing system prevents blood backflow. Due to the potential risk of unexpected arterial blood loss, this method is primarily used in intensive...

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Automated Measurement of Microcirculatory Blood Flow Velocity in Pulmonary Metastases of Rats
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Can Vascular Vertigo Be Recognized by Artificial Intelligence Methods?

Aslihan Taskıran-Sag1, Hilal Arslan2, Hare Yazgı3

  • 1Department of Neurology, Faculty of Medicine, TOBB University of Economics and Technology, Ankara, Türkiye.

Noro Psikiyatri Arsivi
|March 4, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning models can predict vascular vertigo early using patient data like age and blood results. This aids diagnosis, improving patient outcomes and reducing healthcare costs.

Keywords:
Artificial intelligencedizzinessmachine learningstrokevertigo

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Area of Science:

  • Medical Informatics
  • Neurology
  • Machine Learning

Background:

  • Dizziness diagnosis is challenging due to diverse causes, leading to delays and increased healthcare costs.
  • Early recognition of vascular vertigo is crucial for timely intervention and preventing neurological emergencies.

Purpose of the Study:

  • To develop and evaluate a machine learning-based method for early prediction of vascular vertigo.
  • To identify key clinical and laboratory features predictive of vascular vertigo.

Main Methods:

  • Utilized patient data including age, gender, symptoms, comorbidities, and blood parameters.
  • Applied various machine learning algorithms (logistic regression, decision trees, SVM, KNN, MLP, ensemble methods) for classification.
  • Identified significant predictive features through statistical analysis.

Main Results:

  • Age, serum albumin, headache, hypertension, and diabetes were identified as crucial features for classifying vascular vertigo.
  • Logistic regression achieved the highest accuracy (86%), with other models ranging from 81.7% to 85.5%.
  • The developed models demonstrate reliable performance in predicting vascular vertigo cases.

Conclusions:

  • Machine learning models show promise for the early prediction of vascular vertigo before hospital admission.
  • Further research is needed to validate and enhance the accuracy of these predictive models.
  • The model can assist healthcare professionals, including ambulance personnel and specialists, in managing complex dizziness cases.