DeepGT: Deep learning-based quantification of nanosized bioparticles in bright-field micrographs of Gires-Tournois biosensor

Jin-Hwi Park, Jiwon Kang, Young Jin Yoo, Harald A. Stenmark, Stefan G. Stanciu, Abdullah Al Mahmud, Hae-Gon Jeon, Young Min Song

Abstract: Rapid and decentralized quantification of viral load profiles in infected patients is vital for assessing clinical severity and tailoring appropriate therapeutic strategies. Although microscopic imaging offers potential for label-free and amplification-free quantitative diagnostics, the small size (~100 nm in diameter) and low refractive index (n ~1.5) of bioparticles present challenges in achieving accurate estimations, consequently increasing the limit of detection (LoD). In this study, we present a novel synergistic biosensing approach, DeepGT, combining Gires-Tournois (GT) sensing platforms with deep learning algorithms to enhance nanoscale bioparticle counting accuracy. The GT sensing platform serves as a photonic resonator, increasing bioparticle visibility in bright-field microscopy and maximizing chromatic contrast. By employing a back-end with a dilated convolutional neural network architecture, DeepGT effectively refines artifacts and color deviations, significantly improving particle estimation accuracy (MAE ~2.37 across 1596 images) compared to rule-based algorithms (MAE ~ 13.47). Notably, the enhanced accuracy in detecting invisible particles (e.g., two- or three-particles) enables an LoD of 138 pg ml− 1 , facilitating a dynamic linear correlation at low viral concentration ranges within the clinical spectrum of infection, from asymptomatic to severe cases. Leveraging transfer learning, DeepGT, which relies on a chromatometry-based strategy instead of a spatial resolution approach, exhibits exceptional precision when analyzing particles of diverse dimensions smaller than the microscopy system’s minimum diffraction limit in visible light (< 258 nm). The DeepGT approach holds promise for early screening and triage of emerging viruses, reducing costs and time requirements in diagnostics.