INTRODUCTION: Diagnosing suspected strokes requires imaging to rule out hemorrhagic stroke, delaying treatment—especially in rural or isolated areas. Even a 10-minute delay can worsen outcomes and increase mortality. Nanorobotic diagnosis offers a transformative solution, bypassing imaging delays through rapid, precise drug delivery, potentially saving lives and improving recovery. 

METHODS: A MATLAB model simulated Near-Infrared (NIR) signal transmission through biological media, considering variables like skin thickness, pigmentation, and hair density to ensure reliable performance across diverse populations. Nanobots were programmed to react to NIR signals, targeting ischemic regions for rapid diagnosis and therapy. Simulations incorporated tissue-specific absorption coefficients and scattering properties for biological realism. Anatomical layers—fat, bone, and brain—were modeled to reflect variability. Validation compared predicted attenuation profiles with published data, refining inputs to address discrepancies. 

RESULTS: NIR light attenuation, analyzed via the Beer-Lambert model, showed a final intensity of 0.73 mW/cm², a 99.27% reduction from an initial 100.00 mW/cm². Largest prediction errors occurred in fat (76.78%) and skin (71.73%), reflecting sensitivity to absorption coefficients and layer thickness. The model achieved a mean absolute error (MAE) of 44.05% and root mean square error (RMSE) of 28.15%. Results highlight the need for refined parameters to enhance nanobot communication accuracy under clinical conditions. 

CONCLUSION: Effective nanorobot communication requires precise intensity ranges for reliable data transfer. This innovation enables direct nanobot-to-practitioner communication, reducing time to intervention for stroke patients in rural and isolated areas. By supporting rapid, informed decision-making, this approach could significantly improve clinical outcomes in pre-hospital stroke care.