Feature Mixing Approach for Detecting Intraoperative Adverse Events in Laparoscopic Roux-en-Y Gastric Bypass Surgery
Journal:
arXiv
Published Date:
Apr 23, 2025
Abstract
Intraoperative adverse events (IAEs), such as bleeding or thermal injury, can
lead to severe postoperative complications if undetected. However, their rarity
results in highly imbalanced datasets, posing challenges for AI-based detection
and severity quantification. We propose BetaMixer, a novel deep learning model
that addresses these challenges through a Beta distribution-based mixing
approach, converting discrete IAE severity scores into continuous values for
precise severity regression (0-5 scale). BetaMixer employs Beta
distribution-based sampling to enhance underrepresented classes and regularizes
intermediate embeddings to maintain a structured feature space. A generative
approach aligns the feature space with sampled IAE severity, enabling robust
classification and severity regression via a transformer. Evaluated on the
MultiBypass140 dataset, which we extended with IAE labels, BetaMixer achieves a
weighted F1 score of 0.76, recall of 0.81, PPV of 0.73, and NPV of 0.84,
demonstrating strong performance on imbalanced data. By integrating Beta
distribution-based sampling, feature mixing, and generative modeling, BetaMixer
offers a robust solution for IAE detection and quantification in clinical
settings.
