A recent review published in Engineering highlights particle vision analysis (PVA), a rapidly developing field at the intersection of artificial intelligence (AI) and microscopic imaging. The review emphasizes the potential of PVA to accelerate discovery, strengthen quality control, and promote sustainable production across nanomanufacturing, biomanufacturing, and pharmaceuticals.
Particles underpin materials and processes across sectors, and their microscopic behaviors determine performance, safety, and sustainability. To address long-standing bottlenecks in image analysis, this review surveys advances in AI-based PVA, which integrates state-of-the-art methods for classification, detection, segmentation, tracking, and super-resolution. These approaches are increasingly applied in laboratories and production lines that use electron and optical microscopy, with applications spanning nanomanufacturing, biomanufacturing, pharmaceutical quality control, and environmental monitoring.
At the center of the framework is a practice-oriented map of PVA that aligns core computer vision tasks with microscopy workflows. The review organizes existing methods, highlights open-source tools and containers, and explains how these capabilities can be deployed in laboratory settings or integrated into inline inspection systems to provide real-time feedback for process optimization.
The study surveys representative applications, including automated defect inspection in pharmaceutical ampoules, online particle size analysis on conveyor belts, and hyperspectral microplastic detection for rapid environmental assessment. Together, these examples illustrate how AI-assisted microscopy can raise precision, reduce waste, and shorten development cycles from nanoscale discovery to large-scale production.
Technically, the review discusses advances that reduce data and annotation requirements while improving performance. These include prompt-based segmentation with segment anything model (SAM), open-vocabulary detection using contrastive language-image pretraining (CLIP) and Grounding DINO, fast detectors such as You Only Look Once (YOLO) and mask region-based convolutional neural network (Mask R-CNN), and imaging methods like enhanced super-resolution generative adversarial network (ESRGAN) and diffusion-based super-resolution. A notable example is a zero-shot deconvolution model that improves fluorescence microscopy resolution by more than 1.5-fold without the need for extensive training data. The outlook emphasizes the promise of large pretrained models, few-shot learning, and retrieval-augmented generation for scientific analysis.
The authors also describe a "discovery-to-optimization" loop for smart manufacturing, beginning with exploratory tasks such as drug or material screening, moving through imaging and AI analysis, and closing the cycle with feedback that adjusts experiments or production conditions. This vision connects microscopic understanding to macroscopic gains in efficiency and sustainability.
Finally, the paper acknowledges key challenges, such as particle diversity, noisy environments, and the computational demands of high-resolution imaging. It outlines priorities including standardized tools, efficient computation, and robust cross-modality adaptation, with transfer learning and few-shot methods identified as near-term solutions. To support adoption, the authors provide a consolidated list of resources and a public code repository, giving researchers and practitioners a starting point to implement PVA in both experiments and industrial workflows.
The paper "Future Manufacturing with AI-Driven Particle Vision Analysis in the Microscopic World" authored by Guangyao Chen, Fengqi You. Full text of the open access paper: https://doi.org/10.1016/j.eng.2025.08.005
