Autodetect extracranial and intracranial artery stenosis by machine learning using ultrasound

Kai Cheng Hsu, Ching Heng Lin, Kory R. Johnson, Chi Hung Liu, Ting Yu Chang, Kuo Lun Huang, Yang Cheng Fann*, Tsong Hai Lee

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

14 Scopus citations

Abstract

Background: and Purpose: This study proposed a machine learning method for identifying ≥50% stenosis of the extracranial and intracranial arteries. Patients and methods: A total of 8211 patients with both carotid ultrasound and cerebral angiography were enrolled. Support vector machine (SVM) was employed as the machine learning classifier. Carotid Doppler parameters and transcranial Doppler parameters were used as the input features. Feature selection was performed using the Extra-Trees (extremely randomized trees) method. Results: For the machine learning method, the sensitivities and specificities of identifying stenosis of the extracranial arteries were 88.5%–100% and 96.0%–100%, respectively. The sensitivities and specificities of identifying stenosis of the intracranial arteries were 71.7%–100% and 88.9%–100%, respectively. Conclusions: The SVM classifier with feature selection is an efficient method for identifying the stenosis of both intracranial and extracranial arteries. Comparing with traditional Doppler criteria, this machine learning method achieves up to 20% higher in accuracy and 45% in sensitivity, respectively.

Original languageEnglish
Article number103569
JournalComputers in Biology and Medicine
Volume116
DOIs
StatePublished - 01 2020

Bibliographical note

Publisher Copyright:
© 2019

Keywords

  • Angiography
  • Carotid ultrasound
  • Intracranial artery stenosis
  • Machine learning

Fingerprint

Dive into the research topics of 'Autodetect extracranial and intracranial artery stenosis by machine learning using ultrasound'. Together they form a unique fingerprint.

Cite this