At the University of Illinois at Urbana-Champaign, a team led by Han Lee integrates deep learning with Photonic Resonator Absorption Microscopy (PRAM) to create LOCA-PRAM. This system automatically identifies single biomarker molecules tagged with gold nanoparticles by analyzing red LED microscopy images and eliminating artifacts. By training the AI model with paired high-resolution SEM validation data, LOCA-PRAM delivers rapid, accurate molecular counts at the point of care for early disease diagnostics.
Key points
- LOCA-PRAM uses context-aware deep neural network to identify gold-nanoparticle–tagged biomarkers in PRAM images.
- Paired SEM imaging provides high-resolution ground truth for AI training, yielding >95% accuracy in nanoparticle localization.
- System achieves single-molecule sensitivity below 0.1 pM concentration with false-positive rates reduced by over 50% in point-of-care tests.
Why it matters: LOCA-PRAM ushers in accessible single-molecule diagnostics, enabling rapid, accurate disease detection at the patient’s side without expert intervention.
Q&A
- What is Photonic Resonator Absorption Microscopy?
- Why integrate machine learning with biosensors?
- How does SEM validation improve AI performance?
- What advantages do gold nanoparticles offer in biosensing?
