Fast and decentralized quantification of viral load profiles in contaminated sufferers is significant for assessing medical severity and tailoring applicable therapeutic methods. Though microscopic imaging provides potential for label-free and amplification-free quantitative diagnostics, the small dimension (?100 nm in diameter) and low refractive index (n ?1.5) of bioparticles current challenges in reaching correct estimations, consequently rising the restrict of detection (LoD). On this examine, we current a novel synergistic biosensing method, DeepGT, combining Gires-Tournois (GT) sensing platforms with deep studying algorithms to boost nanoscale bioparticle counting accuracy. The GT sensing platform serves as a photonic resonator, rising bioparticle visibility in bright-field microscopy and maximizing chromatic distinction. By using a back-end with a dilated convolutional neural community structure, DeepGT successfully refines artifacts and shade deviations, considerably bettering particle estimation accuracy (MAE ?2.37 throughout 1596 photographs) in comparison with rule-based algorithms (MAE ? 13.47). Notably, the improved accuracy in detecting invisible particles (e.g., two- or three-particles) allows an LoD of 138 pg ml?1, facilitating a dynamic linear correlation at low viral focus ranges inside the medical spectrum of an infection, from asymptomatic to extreme instances. Leveraging switch studying, DeepGT, which depends on a chromatometry-based technique as a substitute of a spatial decision method, displays distinctive precision when analyzing particles of various dimensions smaller than the microscopy system’s minimal diffraction restrict in seen mild (< 258 nm). The DeepGT method holds promise for early screening and triage of rising viruses, lowering prices and time necessities in diagnostics.