Efficient quality control of platelet-rich plasma preparation using computer vision and deep learning.

Abstract

Significance: Platelet-rich plasma (PRP) is a critical component in regenerative medicine, with applications in tissue repair and inflammation regulation. Consistent preparation quality is essential for therapeutic efficacy, but traditional quality control (QC) methods are labor-intensive, slow, and prone to variability.

Aim: We introduce a computer vision-based automated PRP QC model using deep learning to improve the efficiency and accuracy of PRP preparation.

Approach: Blood samples were collected and processed in the laboratory to prepare PRP. Images of the samples were manually captured. Medical-grade QC evaluations determined sample quality, which was labeled for model training. The image data were preprocessed and analyzed using a ResNet18 convolutional neural network combined with a binary classifier to develop a PRP QC model. Training and testing were conducted using data from patients, and the model's accuracy was tested on the independent unavailable dataset.

Results: The PRP QC model achieved an average classification accuracy of 82.5% on unavailable datasets (previously unseen test samples), significantly reducing the time required for QC to under 1 min.

Conclusions: We demonstrate a nondestructive, real-time QC method for PRP preparation with computer vision and deep learning, offering a practical and scalable solution to improve clinical outcomes in regenerative medicine.