人工智能助力的儿科骨骼分类红外成像：综述与未来展望
Infrared Imaging Empowered by Artificial Intelligence for Pediatric Skeletal Triage: A Narrative Review and Future Perspectives
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中文总结 AI 辅助
该研究旨在构建结合广谱红外成像与深度学习的混合框架用于儿科骨骼分类。回顾相关光谱窗口及采集方式，总结图像转换和特征匹配算法。指出儿科解剖利于红外穿透，集成多光谱红外+人工智能成像是无辐射补充，但面临多方面障碍。
中文摘要 AI 辅助
背景：儿科肌肉骨骼创伤占儿科急诊就诊的18%，但诊断仍依赖电离辐射成像。早期累积低剂量辐射会增加白血病和脑恶性肿瘤风险，促使寻找无辐射分类替代方法。目的：综合证据构建一个混合框架，将广谱红外成像与深度学习跨模态转换相结合，从非电离数据生成临床可解释的合成射线照片重建。方法：回顾了650纳米至1毫米的五个红外光谱窗口，以及双几何采集如何利用波长特定的组织深度和生化敏感性。总结了用于将红外数据对齐并融合成射线照片等效重建的图像到图像转换网络和特征匹配算法。意义：儿科解剖结构利于红外穿透，可行性基于近红外光谱和经颅光生物调节证据。关键障碍包括配对红外/ X光数据集构建、人工智能作为医疗设备的监管途径、不同体型和皮肤色素沉着的通用性以及采集协议标准化。结论：集成多光谱红外+人工智能成像是儿科骨骼放射成像有前景的无辐射补充。进展需要多中心配对数据集、外部验证模型以及符合IEC 60825-1标准的红外源安全认证。
英文摘要
Background. Pediatric musculoskeletal trauma represents up to 18% of pediatric ED visits, yet diagnosis still depends on ionizing radiography. Cumulative low-dose radiation in early life raises lifetime leukemia and brain malignancy risk, motivating radiation-free triage alternatives. Objective. To synthesize evidence for a hybrid framework coupling broad-spectrum infrared (IR) imaging with deep-learning cross-modal translation to generate clinically interpretable synthetic-radiograph reconstructions from non-ionizing data. Approach. We review five IR spectral windows spanning 650 nm to 1 mm - NIR-I, NIR-II, SWIR, MIR/LWIR, and THz - and how dual-geometry (transmission/reflection) acquisition exploits wavelength-specific tissue depth and biochemical sensitivity. We summarize image-to-image translation networks (Pix2Pix, CycleGAN, Swin-Unet) and feature-matching algorithms (SuperPoint, SuperGlue, ALIKED, LightGlue) used to align and fuse IR data into radiograph-equivalent reconstructions. Implications. Pediatric anatomy - smaller cross-sections, thinner cortical bone - favors IR penetration, enabling compact, portable, non-ionizing triage hardware. Feasibility is grounded in fNIRS and transcranial photobiomodulation evidence: near-infrared light passes through skin, skull, and cortex with sufficient signal for hemodynamic monitoring - a longer, more attenuating path than through a pediatric forearm or distal leg. Key barriers: paired IR/X-ray dataset construction, AI-as-medical-device regulatory pathways, generalization across body habitus and skin pigmentation, and acquisition-protocol standardization. Conclusions. Integrated multi-spectral IR+AI imaging is a promising radiation-free complement to pediatric skeletal radiography. Progress requires multi-center paired datasets, externally validated models, and IR source safety qualification under IEC 60825-1.
