A Multimodal Approach for Fluid Overload Prediction: Integrating Lung Ultrasound and Clinical Data

TL;DR

This study introduces a multimodal deep learning framework combining lung ultrasound images and clinical data to improve fluid overload prediction in dialysis patients, outperforming single-modality models.

Contribution

The paper presents a novel multimodal approach with cross-domain attention and pseudo-sample generation for enhanced fluid overload classification.

Findings

Multimodal models outperform single-modality models.

Attention mechanisms improve prediction accuracy.

Pseudo-sample generation boosts model performance.

Abstract

Managing fluid balance in dialysis patients is crucial, as improper management can lead to severe complications. In this paper, we propose a multimodal approach that integrates visual features from lung ultrasound images with clinical data to enhance the prediction of excess body fluid. Our framework employs independent encoders to extract features for each modality and combines them through a cross-domain attention mechanism to capture complementary information. By framing the prediction as a classification task, the model achieves significantly better performance than regression. The results demonstrate that multimodal models consistently outperform single-modality models, particularly when attention mechanisms prioritize tabular data. Pseudo-sample generation further contributes to mitigating the imbalanced classification problem, achieving the highest accuracy of 88.31%. This study underscores the effectiveness of multimodal learning for fluid overload management in dialysis patients, offering valuable insights for improved clinical outcomes.