Advances in Portable Optical Microscopy Using Cloud Technologies and Artificial Intelligence for Medical Applications

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 20; p. 6682
• Main Authors: Molani, Alessandro, Pennati, Francesca, Ravazzani, Samuele, Scarpellini, Andrea, Storti, Federica Maria, Vegetali, Gabriele, Paganelli, Chiara, Aliverti, Andrea
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 17.10.2024; MDPI
• Subjects: Algorithms; Artificial Intelligence; Cloud Computing; Data mining; Deep Learning; Design; Energy consumption; Humans; Image processing; Image Processing, Computer-Assisted - methods; Internet of Things; lens-less microscopy; Light; Microscope and microscopy; Microscopes; Microscopy; Microscopy - instrumentation; Microscopy - methods; photoacoustic microscopy; portable microscopy; Review; Sensors; Smartphone; smartphone microscopy; Smartphones; United Kingdom; deep learning; photoacoustic microscopy; point-of-care diagnostics; Internet of Things; cloud computing; lens-less microscopy; portable microscopy; smartphone microscopy; artificial intelligence
• ISSN: 1424-8220; 1424-8220
• 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.