A team at University Hospital Regensburg implements an AI-based convolutional neural network to classify standard facial images, identifying synkinesis in patients with facial palsy. The network processes cropped and resized data through convolutional, activation, pooling, and normalization layers, delivering 98.6% test accuracy. Integrated into a lightweight web interface, this tool supports timely and objective patient triage.
Key points
- Convolutional neural network with multiple convolutional, ReLU, pooling, and batch normalization layers classifies facial synkinesis.
- Dataset of 385 images split into 285 training, 29 validation, and 71 test images ensures no patient overlap during evaluation.
- Model achieves 98.6% accuracy, 100% precision, and 96.9% recall with an average processing time of 24±11 ms per image.
Why it matters: This AI screening tool accelerates facial synkinesis diagnosis, reducing specialist referral delays and enabling earlier, objective intervention in facial palsy care.
Q&A
- What is facial synkinesis?
- How does a convolutional neural network (CNN) work?
- What do precision, recall, and F1-score indicate?
- Why is data standardization important in the study?
- How can clinicians use this web application?