AI summaryⓘ
The authors explain that many children visit emergency rooms for bone injuries, but current diagnosis uses X-rays that expose kids to small amounts of harmful radiation. They review how different types of infrared light, which do not use radiation, can be combined with AI techniques to create images that look like X-rays. This approach might work better in children due to their smaller and thinner bones, allowing infrared light to penetrate more easily. The main challenges include collecting paired infrared and X-ray data and making sure the AI works well for different body types and skin colors. The authors suggest more research and validated studies are needed before this method can be used in hospitals.
pediatric musculoskeletal traumaionizing radiographyinfrared imagingdeep learningimage-to-image translationnon-ionizing radiationsynthetic radiographnear-infrared spectroscopyAI medical device regulationmulti-spectral imaging
Abstract
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.