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Ultrafast Intelligent Sensor for Integrated Biological Fluorescence Imaging and Recognition
Yuqing Jian1, Wei Gao1, Yue Qin1
1State Key Laboratory of Dynamic Measurement Technology, Shanxi Province Key Laboratory of Quantum Sensing and Precision Measurement, North University of China, Taiyuan 030051, China.
ACS Sensors
|December 5, 2024
Summary
This study introduces an integrated intelligent sensor for biological fluorescence imaging and ultrafast recognition, combining imaging and neural network systems on a single chip. It achieves rapid tumor margin recognition (19.63 μs) and high detectivity for biomedical applications.
Area of Science:
- Biomedical engineering
- Medical imaging
- Sensor technology
Background:
- Current fluorescence imaging and recognition systems are separate, causing delays and inefficiency.
- This separation hinders the capture of rapidly changing physiological processes.
- There is a need for integrated systems for real-time biological analysis.
Purpose of the Study:
- To propose an integrated intelligent sensor for biological fluorescence imaging and ultrafast recognition.
- To overcome the limitations of separate imaging and recognition systems.
- To develop a novel paradigm for intelligent medical sensor design.
Main Methods:
- Integration of a photodetector array imaging system and a neural network recognition system on a single chip.
- Utilizing special organic materials and a bulk heterojunction structure for the photodetector array.
- Developing a unified architecture for ultrafast imaging and recognition.
Main Results:
- Achieved ultrafast recognition of tumor margins in 19.63 μs.
- The sensor exhibits high specific detectivity (3.06 × 10^12 Jones) in the 600-800 nm near-infrared range.
- Demonstrated real-time, accurate recognition of biological fluorescence edges, even with colored light noise.
Conclusions:
- The integrated intelligent sensor offers a compact, efficient solution for biological fluorescence imaging and recognition.
- This technology enables ultrafast and accurate identification of biological features, such as tumor margins.
- The developed sensor has the potential to revolutionize intelligent medical sensor design and manufacturing for applications like pathological surgery.
