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

Area of Science:

• Regenerative Medicine
• Biotechnology
• Medical Imaging

Background:

• Platelet-rich plasma (PRP) is vital for tissue repair and inflammation control in regenerative medicine.
• Ensuring consistent PRP preparation quality is crucial for therapeutic success.
• Current quality control (QC) methods for PRP are inefficient, time-consuming, and variable.

Purpose of the Study:

• To develop an automated, computer vision-based quality control (QC) model for platelet-rich plasma (PRP) preparation.
• To enhance the efficiency and accuracy of PRP QC using deep learning techniques.
• To provide a reliable method for assessing PRP quality in real-time.

Main Methods:

• A deep learning model, specifically a ResNet18 convolutional neural network with a binary classifier, was developed for PRP QC.
• Blood samples were processed to prepare PRP, and images were captured for analysis.
• The model was trained and validated using patient data, with performance assessed on unseen datasets.

Main Results:

• The automated PRP QC model achieved an average classification accuracy of 82.5% on independent, unseen datasets.
• The model significantly reduced the time required for PRP quality control to under one minute.
• This demonstrates a substantial improvement in efficiency compared to traditional QC methods.

Conclusions:

• A nondestructive, real-time quality control method for PRP preparation has been successfully developed using computer vision and deep learning.
• This automated approach offers a practical and scalable solution for improving PRP preparation.
• The findings have the potential to enhance clinical outcomes in regenerative medicine through more reliable PRP therapies.