DensiThAI, A Multi-View Deep Learning Framework for Breast Density Estimation using Infrared Images

TL;DR

This paper introduces DensiThAI, a deep learning framework that estimates breast density from infrared thermal images, providing a non-ionizing alternative to traditional mammography with promising accuracy.

Contribution

The study presents a novel multi-view deep learning method for breast density classification using thermal images, validated on a large multi-center dataset.

Findings

Achieved mean AUROC of 0.73 in density classification

Significant separation between density classes across all data splits

Consistent performance across different age groups

Abstract

Breast tissue density is a key biomarker of breast cancer risk and a major factor affecting mammographic sensitivity. However, density assessment currently relies almost exclusively on X-ray mammography, an ionizing imaging modality. This study investigates the feasibility of estimating breast density using artificial intelligence over infrared thermal images, offering a non-ionizing imaging approach. The underlying hypothesis is that fibroglandular and adipose tissues exhibit distinct thermophysical and physiological properties, leading to subtle but spatially coherent temperature variations on the breast surface. In this paper, we propose DensiThAI, a multi-view deep learning framework for breast density classification from thermal images. The framework was evaluated on a multi-center dataset of 3,500 women using mammography-derived density labels as reference. Using five standard thermal views, DensiThAI achieved a mean AUROC of 0.73 across 10 random splits, with statistically significant separation between density classes across all splits (p << 0.05). Consistent performance across age cohorts supports the potential of thermal imaging as a non-ionizing approach for breast density assessment with implications for improved patient experience and workflow optimization.