# Tongue image analysis for accurate prediction of nutritional risk screening in cancer patients during radiochemotherapy: a feature selection network and aliasing attention mechanism approach

## TL;DR

This paper introduces a non-invasive AI system using tongue images to accurately predict nutritional risk in cancer patients undergoing treatment.

## Contribution

A novel system combining a feature selection network and aliasing attention mechanism for nutritional risk screening using tongue images.

## Key findings

- The system achieved high performance with AUC of 0.919, ACC of 0.927, and Recall of 0.888.
- The SelectNet module improved ACC and AUC by 17.25% and 16.14%, respectively.
- The shuttle attention mechanism further boosted AUC and ACC by 3.89% and 1.94%.

## Abstract

The Nutritional Risk Screening 2002 (NRS2002) is a widely adopted tool for assessing nutritional risk in patients. This study introduces a novel, non-invasive, efficient, and accurate screening approach to complement the traditional NRS2002 assessment, addressing its inherent limitations.

A dataset comprising 672 tongue images from 470 tumor patients was collected. A new predictive analysis system for NRS2002 was developed by integrating two model branches. Machine learning was employed for risk prediction, and a ResNet50 neural network was utilized to extract high-dimensional features from tongue images. This architecture was enhanced with Shuttle Attention mechanism. The final predictions were derived through the fusion of both model branches.

The fusion of the two branches significantly improved the model’s ability to capture complex features. For both at-risk and risk-free cohorts, the system demonstrated optimal classification performance across three key metrics: AUC = 0.919, ACC = 0.927, and Recall = 0.888. In ablation studies, the SelectNet module improved ACC and AUC by 17.25 and 16.14%, respectively. Furthermore, the integration of the shuttle attention mechanism led to additional gains, with AUC and ACC increasing by 3.89 and 1.94%, respectively.

We successfully developed and validated an NRS2002 nutritional risk prediction model based on tongue image characteristics. This tool has the potential to minimize human error, improve dynamic performance, and provide non-invasive, accurate nutritional risk screening. It represents a step forward in personalized medicine and holds substantial clinical value.