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人工智能辅助太赫兹成像技术在激光治疗中快速、无标记地识别高效光配方

Artificial Intelligence-Assisted Terahertz Imaging for Rapid and Label-Free Identification of Efficient Light Formula in Laser Therapy.

机构信息

Tianjin Key Laboratory of Optoelectronic Detection Technology and System, School of Electronic and Information Engineering, Tiangong University, Tianjin 300387, China.

Key Laboratory of Opto-Electronics Information Technology (Ministry of Education), School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, China.

出版信息

Biosensors (Basel). 2022 Oct 5;12(10):826. doi: 10.3390/bios12100826.

DOI:10.3390/bios12100826
PMID:36290963
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9599775/
Abstract

Photodynamic therapy (PDT) is considered a promising noninvasive therapeutic strategy in biomedicine, especially by utilizing low-level laser therapy (LLLT) in visible and near-infrared spectra to trigger biological responses. The major challenge of PDT in applications is the complicated and time-consuming biological methodological measurements in identification of light formulas for different diseases. Here, we demonstrate a rapid and label-free identification method based on artificial intelligence (AI)-assisted terahertz imaging for efficient light formulas in LLLT of acute lung injury (ALI). The gray histogram of terahertz images is developed as the biophysical characteristics to identify the therapeutic effect. Label-free terahertz imaging is sequentially performed using rapid super-resolution imaging reconstruction and automatic identification algorithm based on a voting classifier. The results indicate that the therapeutic effect of LLLT with different light wavelengths and irradiation times for ALI can be identified using this method with a high accuracy of 91.22% in 33 s, which is more than 400 times faster than the biological methodology and more than 200 times faster than the scanning terahertz imaging technology. It may serve as a new tool for the development and application of PDT.

摘要

光动力疗法(PDT)被认为是生物医学中一种有前途的非侵入性治疗策略,特别是利用可见和近红外光谱中的低水平激光疗法(LLLT)来触发生物反应。在应用中,PDT 的主要挑战是在识别不同疾病的光公式时,需要进行复杂且耗时的生物方法学测量。在这里,我们展示了一种基于人工智能(AI)辅助太赫兹成像的快速、无标记识别方法,用于高效地进行急性肺损伤(ALI)的 LLLT 光公式。太赫兹图像的灰度直方图被开发为生物物理特征,以识别治疗效果。使用快速超分辨率成像重建和基于投票分类器的自动识别算法,依次进行无标记太赫兹成像。结果表明,使用该方法可以以 91.22%的高精度在 33 秒内识别出不同波长和辐照时间的 LLLT 对 ALI 的治疗效果,比生物方法快 400 多倍,比太赫兹扫描成像技术快 200 多倍。它可能成为 PDT 开发和应用的新工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ddce/9599775/1db2a42cbd2f/biosensors-12-00826-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ddce/9599775/d40e4de1fefe/biosensors-12-00826-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ddce/9599775/7b0686bd8a56/biosensors-12-00826-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ddce/9599775/83cd07d13e32/biosensors-12-00826-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ddce/9599775/057f5a705377/biosensors-12-00826-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ddce/9599775/1db2a42cbd2f/biosensors-12-00826-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ddce/9599775/d40e4de1fefe/biosensors-12-00826-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ddce/9599775/7b0686bd8a56/biosensors-12-00826-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ddce/9599775/83cd07d13e32/biosensors-12-00826-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ddce/9599775/057f5a705377/biosensors-12-00826-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ddce/9599775/1db2a42cbd2f/biosensors-12-00826-g005.jpg

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