Primary open-angle glaucoma (POAG) is one of the most common neurodegenerative diseases that cause irreversible optic nerve damage. POAG diagnosis requires multimodal assessments; however, current methods like intraocular pressure (IOP) and optical coherence tomography (OCT) imaging suffer from low sensitivity and inter-patient variability, respectively. Liquid biopsy provides objective molecular signatures, offering a robust alternative to overcome limitations of conventional clinical hallmarks. Here, we present an efficient and highly sensitive diagnostic platform operating on spectral profiles from aqueous humor (AH)-derived exosomes. Following morphological and compositional validation of exosome presence in AH samples, antibody-functionalized substrates were fabricated for selective capture. Immobilized AH-derived exosomes are then coated with gold nanoparticles to generate molecular fingerprint Raman signals. A total of 7600 spectra are acquired and used to train and evaluate the convolutional neural network (CNN) model for binary classification between healthy controls and glaucoma patients. The trained CNN model achieved an AUC of 0.96 and a prediction accuracy of 91%. The system enables rapid, individualized diagnosis, overcoming sample volume limitations via integrated immunoassay-based isolation and artificial intelligence (AI)-driven classification, highlighting the potential of ocular fluid-derived EVs in the diagnosis of neurodegenerative diseases.
Sun et al. (Wed,) studied this question.