Low Cost Platelet-Rich Plasma Facilities Creation With Ai-Driven Cardiovascular Disease Assessment Using Raman Spectroscopy

Athanasios Delis, Kris Koutsi, Panayiotis Tsanakas

Abstract: This paper presents a novel, low-cost approach for establishing a platelet-rich plasma (PRP) facility that integrates affordable Raman spectroscopy with advanced machine learning techniques for automated cardiovascular disease diagnosis. By reviewing cost-effective PRP production protocols, we demonstrate that high-quality PRP can be prepared using standard laboratory equipment at significantly reduced costs, bringing the per-session expenses below USD5. Furthermore, by incorporating a low-cost Raman spectrometer ranging from USD3,000-USD3,800 into the facility, real-time analysis of PRP samples is enabled. Surface-enhanced Raman spectroscopy (SERS) data of platelets are processed with deep learning algorithms to detect subtle biomarkers associated with cardiovascular pathology. Experimental evaluation revealed that the utilized deep learning models achieved consistently high 100 percent test accuracy under optimal training conditions, thereby validating the system's reliability and potential for early, automated cardiovascular diagnosis along with other PRP-related procedures. The preliminary results indicate that this integrated infrastructure holds significant promise for early, automated cardiovascular screening while maintaining overall low-cost operations.

External IDs:dblp:conf/mocast/DelisKT25