Ultrafast Intelligent Sensor for Integrated Biological Fluorescence Imaging and Recognition

Abstract

Fluorescence imaging and recognition are core technologies in targeted medicine, pathological surgery, and biomedicine. However, current imaging and recognition systems are separate, requiring repeated data transfers for imaging and recognition that lead to delays and inefficiency, hindering the capture of rapidly changing physiological processes and biological phenomena. To address these problems, we propose an integrated intelligent sensor for biological fluorescence imaging and ultrafast recognition. This sensor integrates an imaging system based on a photodetector array and a recognition system based on neural networks on a single chip, featuring a highly compact structure, a continuously adjustable optical response, and reconfigurable electrical performance. The unified architecture of the imaging and recognition systems enables ultrafast recognition (19.63 μs) of tumor margins. Additionally, the special organic materials and bulk heterojunction structure endow the photodetector array with strong wavelength dependence, achieving high specific detectivity (3.06 × 10¹² Jones) in the narrowband near-infrared range commonly used in biomedical imaging (600–800 nm). After training, the sensor can accurately recognize biological fluorescence edges in real time, even under interference from other colored light noise. Benefiting from its rapidity and high accuracy, we demonstrated a simulated surgical experiment showcasing tumor edge fluorescence imaging, recognition, and cutting. This integrated approach holds the potential to establish a novel paradigm for designing and manufacturing intelligent medical sensors.