Validation of an automated machine learning algorithm for the detection and analysis of cerebral aneurysms

Marco Colasurdo, Daphna Shalev, Ariadna Robledo, Viren Vasandani, Zean Aaron Luna, Abhijit S. Rao, Roberto Garcia, Gautam Edhayan, Visish M. Srinivasan, Sunil A. Sheth, Yoni Donner, Orin Bibas, Nicole Limzider, Hashem Shaltoni, Peter Kan

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

OBJECTIVE: Machine learning algorithms have shown groundbreaking results in neuroimaging. The authors herein evaluated the performance of a newly developed convolutional neural network (CNN) to detect and analyze intracranial aneurysms (IAs) on CTA. METHODS: Consecutive patients with CTA studies between January 2015 and July 2021 at a single center were identified. The ground truth determination of cerebral aneurysm presence or absence was made from the neuroradiology report. The primary outcome was the performance of the CNN in detecting IAs in an external validation set, measured using area under the receiver operating characteristic curve statistics. Secondary outcomes included accuracy for location and size measurement. RESULTS: The independent validation imaging data set consisted of 400 patients with CTA studies, median age 40 years (IQR 34 years) and 141 (35.3%) of whom were male; 193 patients (48.3%) had a diagnosis of IA on neuroradiologist evaluation. The median maximum IA diameter was 3.7 mm (IQR 2.5 mm). In the independent validation imaging data set, the CNN performed well with 93.8% sensitivity (95% CI 0.87-0.98), 94.2% specificity (95% CI 0.90-0.97), and a positive predictive value of 88.2% (95% CI 0.80-0.94) in the subgroup with an IA diameter ≥ 4 mm. CONCLUSIONS: The described Viz.ai Aneurysm CNN performed well in identifying the presence or absence of IAs in an independent validation imaging set. Further studies are necessary to investigate the impact of the software on detection rates in a real-world setting.

Original languageEnglish (US)
Pages (from-to)1002-1009
Number of pages8
JournalJournal of neurosurgery
Volume139
Issue number4
DOIs
StatePublished - Oct 1 2023
Externally publishedYes

Keywords

  • CT angiography
  • artificial intelligence
  • cerebral aneurysm
  • diagnostic technique
  • endovascular neurosurgery
  • technology
  • vascular disorders

ASJC Scopus subject areas

  • Surgery
  • Clinical Neurology

Fingerprint

Dive into the research topics of 'Validation of an automated machine learning algorithm for the detection and analysis of cerebral aneurysms'. Together they form a unique fingerprint.

Cite this