Utility of deep learning for degree calculation of aortic arch calcification in chest-X ray

Aortic arch calcification (AoAC) is commonly classified into four grades according to the percentage of calcification observed in clinical practice, and the interpretation is typically based on visual assessment by clinicians. However, this manual evaluation process is time-consuming and may fail to detect subtle calcifications, potentially leading to grading inaccuracies.

This study presents a transformer-based model, termed Multi-Attention with Transformer Model (MATM), to improve the accuracy of AoAC grade classification. The proposed framework integrates multiple attention modules to enhance the representation of spatial features. In addition, the transformer mechanism incorporates positional information together with a hierarchical 16-block representation of the aortic arch, enabling fine-grained analysis of calcification distribution.

The proposed method captures subtle calcification features and enables more accurate classification of AoAC grades. Experimental results demonstrate that the model can automatically estimate AoAC severity for both the traditional four-grade classification and the more detailed 16-grade classification, achieving an accuracy of up to 95.5%.

The proposed method can reduce interpretation time and improve grading consistency for clinicians by minimizing variability caused by individual experience. Such AI-assisted assessment has the potential to standardize AoAC evaluation in future clinical practice.