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

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.