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 net

SUBMITTER: Mai W 

PROVIDER: S-EPMC12322746 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature