AI-Enhanced Smart Medical Needles with Resonant Metasurfaces and Acoustic Barcode Signatures for High-Contrast, Angle-Robust Ultrasound Guidance

ltrasound-guided interventions are limited by poor needle visibility: metal shafts appear bright only when aligned with the probe and fade at oblique angles, creating blind spots that hinder tip localization and contribute to non-diagnostic biopsies, tumor under-treatment, and nerve damage. When ultrasound fails, clinicians often switch to fluoroscopy, CT, or MRI, raising cost, procedure time, and radiation exposure. Commercial “echogenic” needles use dimpling or surface roughening, but their scattering is broadband and nondirectional, making brightness angle-dependent and generating random speckle that cannot be reliably decoded by machines. We propose a novel class of AI-enhanced smart needles that integrate resonant acoustic metasurfaces with acoustic barcodes for the first time. Discrete slit resonators, patterned as rings or helices, act as unit cells and when placed in arrays they form directional gratings that retroreflect energy to the probe, boosting SNR and brightness across wide angles. Selectively omitting slits encodes a repeatable barcode that AI models (segmentation + 1-D decoders) read to localize the tip, converting weak-signal tracking into deterministic recognition. The work will deliver prototypes with 2–3× CNR across ±45°; co-optimized barcodes and AI for sub-millimeter tip localization; and sterilizable, biocompatible integration. This positions HBKU and Qatar to lead next-generation device innovation for precise medicine and future medical robotics.

Hamad Bin Khalifa University (HBKU)