Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy.
Sensors (Basel). 2024 Oct 17;24(20):6682. doi: 10.3390/s24206682.
The need for faster and more accessible alternatives to laboratory microscopy is driving many innovations throughout the image and data acquisition chain in the biomedical field. Benchtop microscopes are bulky, lack communications capabilities, and require trained personnel for analysis. New technologies, such as compact 3D-printed devices integrated with the Internet of Things (IoT) for data sharing and cloud computing, as well as automated image processing using deep learning algorithms, can address these limitations and enhance the conventional imaging workflow. This review reports on recent advancements in microscope miniaturization, with a focus on emerging technologies such as photoacoustic microscopy and more established approaches like smartphone-based microscopy. The potential applications of IoT in microscopy are examined in detail. Furthermore, this review discusses the evolution of image processing in microscopy, transitioning from traditional to deep learning methods that facilitate image enhancement and data interpretation. Despite numerous advancements in the field, there is a noticeable lack of studies that holistically address the entire microscopy acquisition chain. This review aims to highlight the potential of IoT and artificial intelligence (AI) in combination with portable microscopy, emphasizing the importance of a comprehensive approach to the microscopy acquisition chain, from portability to image analysis.
对实验室显微镜的更快、更便捷替代方法的需求正在推动生物医学领域图像和数据采集链的许多创新。台式显微镜体积庞大,缺乏通信功能,并且需要经过培训的人员进行分析。新技术,如集成物联网的数据共享和云计算的紧凑型 3D 打印设备,以及使用深度学习算法的自动化图像处理,可以解决这些限制并增强传统的成像工作流程。本综述报告了显微镜小型化的最新进展,重点介绍了新兴技术,如光声显微镜,以及更成熟的方法,如基于智能手机的显微镜。详细研究了物联网在显微镜中的潜在应用。此外,本综述还讨论了显微镜图像处理的发展,从传统方法到深度学习方法的转变,促进了图像增强和数据解释。尽管该领域取得了许多进展,但很少有研究全面解决整个显微镜采集链的问题。本综述旨在强调物联网和人工智能 (AI) 与便携式显微镜相结合的潜力,强调从便携性到图像分析对显微镜采集链进行全面处理的重要性。