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Related Experiment Video

Updated: May 17, 2025

Optimization of the Retinal Vein Occlusion Mouse Model to Limit Variability
07:23

Optimization of the Retinal Vein Occlusion Mouse Model to Limit Variability

Published on: August 6, 2021

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Minimizing Human-Induced Variability in Quantitative Angiography for Robust and Explainable AI-Based Occlusion

Parmita Mondal1,2, Mohammad Mahdi Shiraz Bhurwani3, Swetadri Vasan Setlur Nagesh2,4

  • 1Department of Biomedical Engineering, University at Buffalo, Buffalo, NY 14260.

Arxiv
|March 31, 2025
PubMed
Summary

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FRED flow diversion with LVIS protection of large posterior communicating artery aneurysm: the "FRELVIS" technique.

Neurosurgical focus: Video·2022
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Use of drug-eluting, balloon-expandable resolute onyx coronary stent as a novel treatment strategy for vertebral artery ostial stenosis: Case series.

Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences·2022
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Global Impact of the COVID-19 Pandemic on Stroke Volumes and Cerebrovascular Events: A 1-Year Follow-up.

Neurology·2022
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Carotid Artery Stenting Using the Walrus Balloon Guide Catheter With Flow Reversal for Proximal Embolic Protection: Technical Description and Single-Center Case Series.

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World neurosurgery·2022
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Triple therapy versus dual-antiplatelet therapy for dolichoectatic vertebrobasilar fusiform aneurysms treated with flow diverters.

Journal of neurointerventional surgery·2022

Bias in quantitative angiography (QA) can affect intracranial aneurysm (IA) occlusion prediction. Correcting this bias significantly improved deep neural network (DNN) accuracy for predicting IA occlusion after flow diverter treatment.

Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Medicine
  • Vascular Surgery

Background:

  • Contrast injection variability in quantitative angiography (QA) poses a significant challenge for accurate intracranial aneurysm (IA) occlusion prediction using deep neural networks (DNNs).
  • Existing methods struggle with inherent biases in imaging data, impacting the reliability of AI-driven outcome predictions.

Purpose of the Study:

  • To implement an injection bias removal algorithm to reduce QA variability.
  • To evaluate the impact of explainable AI (XAI) on the reliability and interpretability of DNN models for predicting IA occlusion post-flow diverter treatment.

Main Methods:

  • Angiograms from 458 patients with flow diverter-treated IAs were analyzed.
  • Injection variability was minimized by deconvolving and reconvolving the parent artery input with a standardized curve.
Keywords:
Injection Bias CorrectionIntracranial AneurysmOcclusionQuantitative Angiography

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Last Updated: May 17, 2025

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  • A DNN model was trained on QA biomarkers for six-month occlusion prediction, with Local Interpretable Model-Agnostic Explanations (LIME) used for feature identification.

    • Main Results:

    • The DNN model achieved an AUROC of 0.60±0.05 without bias correction.
    • Post-correction, the DNN's AUROC increased to 0.79±0.02, with accuracy rising from 0.58±0.03 to 0.73±0.01.
    • Sensitivity and specificity were 67.61±1.93% and 76.19±1.12%, respectively, with LIME enhancing model interpretability.

    Conclusions:

    • Standardizing QA parameters through injection bias correction enhances the accuracy of IA occlusion prediction in flow diverter-treated cases.
    • Integrating XAI methods like LIME improves the transparency and clinical relevance of AI models for outcome prediction.
    • This study demonstrates the feasibility of developing clinically interpretable AI solutions for predicting treatment outcomes in neurovascular interventions.