Extracellular vesicles naturally occur in biological fluids. These vesicles and their cargo are a promising source of biomarkers for personalized healthcare, including early cancer detection, health monitoring, and infectious disease. However, current methods for isolating and analyzing these particles are derived from cellular analysis methods, such as size-based sorting, biological targeting, or density-based isolation. For vesicles, these methods are often costly, time consuming, and produce inconsistent results. A key limitation is the poor understanding of vesicle mechanical properties, such as stiffness. Due to their small size and low concentration in biofluids, it is difficult to evaluate mechanical responses such as deformation during analysis. As a result, these mechanical properties are difficult to measure. Their small size also makes it difficult to directly observe individual vesicles using conventional optical microscopy. In this project, new approaches will be developed to quantify the mechanical properties of extracellular vesicles. This work will enable faster and more reliable manipulation of these particles. The project will support applications such as biomarker discovery. The methods in this project combine artificial intelligence–enabled models with state-of-the-art electron microscopy and super-resolution fluorescence microscopy. Together, these tools will provide meaningful measurements of mechanical properties. Additionally, this project will train personnel in advanced biomedical techniques. The trained workforce will support broader impact goals to sustain American leadership in biotechnology. This project will establish a physics-informed artificial intelligence assisted computational framework by utilizing high-resolution experimental imaging data to quantify the mechanical properties of extracellular vesicles. A structural representation based on implicit functions will be developed to capture vesicle geometry and deformation. A red