用红外图像做乳腺密度评估，无辐射且效果稳定。
DensiThAI, A Multi-View Deep Learning Framework for Breast Density Estimation using Infrared Images
- 多视角深度学习框架，从红外图像识别乳腺密度。
- 在3500例数据上平均AUROC达0.73，各年龄组表现一致。
- 适合关注无辐射筛查、临床流程优化的研究者。
乳腺组织密度是乳腺癌风险的重要生物标志物，也是影响钼靶敏感性的关键因素。然而，当前密度评估几乎完全依赖具有电离辐射的X射线钼靶成像。本研究探索利用人工智能分析红外热成像进行乳腺密度估计的可行性，提供一种非电离成像方法。其核心假设是纤维腺性和脂肪组织具有不同的热物理与生理特性，导致乳房表面存在细微但空间连贯的温度差异。本文提出DensiThAI——一种基于多视角深度学习的乳腺密度分类框架。该框架在包含3,500名女性的多中心数据集上进行评估，以钼靶导出的密度标签为参考。使用五种标准热成像视图，DensiThAI在10次随机划分中平均AUROC达到0.73，所有划分中密度类别间均表现出统计显著分离（p << 0.05）。跨年龄组的一致性表现支持热成像作为非电离乳腺密度评估手段的潜力，对提升患者体验和优化临床流程具有重要意义。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。