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

Shi, Jia; Guo, Zekang; Chen, Hongli; Xiao, Zhitao; Bai, Hua; Li, Xiuyan; Niu, Pingjuan; Yao, Jianquan

doi:10.3390/bios12100826

Cited by 6 publications

(3 citation statements)

References 58 publications

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“…Another study utilized AI algorithms and terahertz imaging technology to determine the ideal laser therapy light formula for acute lung injury. [ 63 ] The authors employ AI algorithms to assess and categorize terahertz images of various light formulas administered to biological tissues based on their therapeutic efficacy. Using the voting classifier, the accuracy value was yielded as 91.22% by offering a more rapid and label‐free method to uncover effective light formulations, which highlights the importance of AI in enhancing laser therapy.…”

Section: Ai In Basic Medical Researchmentioning

confidence: 99%

Plasma medicine: The era of artificial intelligence

Ercan

Özdemir

et al. 2023

Plasma Processes & Polymers

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The current trends that incorporate artificial intelligence (AI) and medicine have created new opportunities for improvement in both early diagnosis and treatment of diseases. In this framework, AI might also have the potential to significantly revolutionize the way we approach the field of plasma medicine, an area that is quickly growing and uses cold atmospheric plasma (CAP) to address a variety of medical conditions. Plasma medicine offers promising therapeutic alternatives for conditions widely ranging from cancer treatment to wound healing and antimicrobial applications, but the complexity of the plasma sources and the huge number of parameters may be overwhelming for the determination of underlying mechanisms and for understanding the effect of the source. This is where AI steps in, to provide strong tools for modeling, evaluating, and controlling CAPs. By harnessing the power of AI, researchers in the plasma medicine area, are now able to evaluate massive volumes of data, enhance their treatment protocols, and predict treatment results with a level of precision never possible before. Hereby, we emphasized the potential and further utilization of AI in plasma medicine in light of the fascinating and recent developments in this cooperation. New opportunities are encouraging, but potential limitations, ethical issues, model transparency, and generalizability should be considered. Regardless, the possibilities are endless, and the future of plasma medicine is looking brighter than ever with the implementation of AI.

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Section: Ai In Basic Medical Researchmentioning

confidence: 99%

Plasma medicine: The era of artificial intelligence

Ercan

Özdemir

et al. 2023

Plasma Processes & Polymers

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“…THz technology can also be very useful in combination with artificial intelligence (AI) [188,189] and machine learning (ML) [190][191][192]. AI and ML can predict some mechanical, thermal, and other properties of bioplastics and biopolymers based on their chemical composition and structure.…”

Section: Machine Learning and Deep Learning Techniques For Thz Signal...mentioning

confidence: 99%

Review of Bioplastics Characterisation by Terahertz Techniques in the View of Ensuring a Circular Economy

Abina,

Korošec,

Puc

et al. 2023

Photonics

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The increasing scarcity of natural resources, worsening global climate change, environmental degradation, and rising demand for food are forcing the biotechnology and plastics industries to seek and apply circular economy models that would lead to a sustainable transition in the production and use of bioplastics. Circular economy models can improve the economic productivity of bio-based plastics and have a positive impact on the environment by reducing conventional plastic waste and the consumption of petrochemical feedstocks for plastic production. In addition, some agricultural wastes that have the potential to be used as bioplastics can be reused. Terahertz (THz) systems are already used in the plastics and rubber industries for non-destructive testing, detection, imaging, and quality control. Several reports have highlighted the potential applications of THz spectroscopy and imaging in polymer analysis and plastics characterisation. This potential is even greater with chemometric methods and artificial intelligence algorithms. In this review, we focus on applications that support the transformation of the biotechnology sector to the circular economy, particularly via the transition from conventional plastics to bioplastics. In this review, we discuss the potential of THz systems for the characterisation and analysis of bioplastics and biopolymers. The results of previous studies on biopolymers in the THz frequency range are summarised. Furthermore, the potential of using artificial intelligence approaches such as machine learning as advanced analytical methods in THz spectroscopy and imaging, in addition to the conventionally used chemometric methods, is discussed. The results of this review highlight that THz technology can contribute to closed technological circles in important areas of biotechnology and the related plastics and rubber industries.

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“…This process generates reactive oxygen species (ROS) that ultimately induce cancer cell death [11,12]. To evaluate the therapeutic efficacy of PDT, it is crucial to monitor the dynamic response of cancer cells [13,14].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Insights into the Dynamic Effects of Photodynamic Therapy on Cancer Cells

Rahman,

Yan,

et al. 2024

Pharmaceutics

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Photodynamic therapy (PDT) shows promise in tumor treatment, particularly when combined with nanotechnology. This study examines the impact of deep learning, particularly the Cellpose algorithm, on the comprehension of cancer cell responses to PDT. The Cellpose algorithm enables robust morphological analysis of cancer cells, while logistic growth modelling predicts cellular behavior post-PDT. Rigorous model validation ensures the accuracy of the findings. Cellpose demonstrates significant morphological changes after PDT, affecting cellular proliferation and survival. The reliability of the findings is confirmed by model validation. This deep learning tool enhances our understanding of cancer cell dynamics after PDT. Advanced analytical techniques, such as morphological analysis and growth modeling, provide insights into the effects of PDT on hepatocellular carcinoma (HCC) cells, which could potentially improve cancer treatment efficacy. In summary, the research examines the role of deep learning in optimizing PDT parameters to personalize oncology treatment and improve efficacy.

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

Cited by 6 publications

References 58 publications

Plasma medicine: The era of artificial intelligence

Plasma medicine: The era of artificial intelligence

Review of Bioplastics Characterisation by Terahertz Techniques in the View of Ensuring a Circular Economy

Deep Learning Insights into the Dynamic Effects of Photodynamic Therapy on Cancer Cells

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