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Towards cleaner waters: Advancing pollutant detection with artificial intelligence-assisted digital in-line holographic microscopy
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This review examines how AI-assisted digital in-line holographic microscopy (DIHM) enables real-time detection of water pollutants including nano/microplastics, oil spills, and harmful algal blooms, comparing its capabilities against alternative detection techniques.
• Comprehensive review of AI-DIHM for real-time detection of water pollutants. • Detailed discussion on the principles and advancements in DIHM instrumentation. • Comparative analysis of DIHM features versus alternative detection techniques. • Real-time DIHM applications in nano/microplastics, oil spill, and suspended solids. • Role of DIHM in studying harmful algal blooms and phytoplankton dynamics. Rapid urbanization and population growth have raised significant concerns about water quality in the environment. This highlights the need for an efficient and user-friendly technique for real-time pollutant monitoring in aquatic environments. Digital In-line Holographic Microscopy (DIHM) enables the development of portable systems capable of selectively detecting and classifying pollutants in real-time. Although, artificial intelligence (AI) requires large datasets for optimal performance, it has revolutionized data analysis, as reported in various studies. AI-assisted DIHM has the potential to reduce costs while enhancing the accurate detection and classification of organic, inorganic, and biological contaminants in water. This review compiles various AI methodologies used for processing holograms of different pollutants in aqueous environments. Additionally, it highlights a critical research gap: the need for robust software packages or computational models to improve the image quality of detected targets.
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