Evaluation of AI-Driven Automatic Segmentation Algorithms for Lung Shunt Fraction Estimation in ⁹⁰Y-Labeled Microsphere Therapy.

April 28, 2026

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Key Points

This research aims to assess the effectiveness of AI-driven automatic segmentation algorithms for estimating lung shunt fraction in radioembolization.
Evaluated AI algorithms for automatic segmentation during 90Y-labeled microsphere therapy for lung shunt fraction estimation.
Compared AI-driven results with traditional planar LSF estimation for validation.
Focus on accuracy and reliability of AI algorithms in clinical applications.
Findings suggest improved accuracy in lung shunt fraction estimation using AI algorithms compared to traditional methods.
Further validation is necessary before these AI tools can be adopted widely in clinical practice.

Evaluates AI algorithms for estimating lung shunt fraction in theranostic applications, highlighting potential advances in precision medicine.

Y radioembolization compared with planar LSF estimation, although further validation is warranted before widespread adoption.

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Heglin et al. (2026) studied this question.

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