Researchers at the University of Tokyo and the Innovation Center of NanoMedicine have successfully developed an artificial intelligence framework capable of identifying nanoparticle morphology directly from standard nanoparticle tracking analysis measurements, eliminating the need for specialized hardware. Published in ACS Applied Nano Materials, the study demonstrates that deep learning can extract previously underutilized optical and motion data from conventional instruments to classify non-spherical particles with high precision. Nanoparticle morphology fundamentally dictates diffusion rates, optical properties, and biological interactions, making accurate characterization essential for applications ranging from targeted drug delivery to environmental monitoring. While transmission electron microscopy offers detailed structural imaging, it requires sample drying or immobilization, rendering it unsuitable for rapid, high-throughput analysis in native liquid environments. Standard nanoparticle tracking analysis circumvents these limitations by recording the Brownian motion of individual particles, yet routine processing typically relies solely on trajectory data for size estimation, ignoring valuable intensity fluctuations in scattered light. The research team addressed this gap by engineering a deep learning architecture that jointly processes trajectory paths and temporal variations in scattered-light intensity. By integrating a one-dimensional convolutional neural network with a bidirectional long short-term memory model, the system simultaneously captures motion dynamics and optical signatures across time series. When tested on gold nanoparticles exhibiting spherical, rod-like, and plate-like structures, the model achieved classification accuracies exceeding 82 percent in binary tasks and maintained an average correctness rate of approximately 80 percent across all three categories. Remarkably, the framework sustained reliable performance using observation windows as brief as 0.2 seconds and under conditions with sparse particle densities. A defining advantage of this approach is its complete hardware independence. Rather than mandating new instrumentation, the methodology transforms existing nanoparticle tracking systems into advanced morphology scanners through software enhancement. This upgrade path preserves the accessibility and operational simplicity of established laboratory workflows while significantly expanding their analytical scope. The technique is particularly advantageous for scenarios involving limited sample volumes or stringent time constraints. Professor Takanori Ichiki, who led the project at the University of Tokyo and iCONM, emphasized the team objective to embed this algorithmic framework into next-generation analysis software. Widespread adoption could streamline quality control for nanomedicines, accelerate the characterization of extracellular vesicles, and improve monitoring of engineered colloidal materials. By unlocking hidden informational layers within conventional measurement data, the research illustrates how machine learning can materially advance nanoscale characterization without requiring costly infrastructure overhauls.