This research developed an algorithm to automatically detect meniscus tears in knee MRI scans, aiming to improve diagnostic access and radiologist workload efficiency. The study utilized a dataset of MRI scans with varying image qualities and employed several deep learning models (ResNet50, VGG16, and custom models based on AlexNet and LeCun's architecture), ultimately selecting a functional model for its precision and interpretability. The model's performance was evaluated using accuracy and precision metrics. Classification activation mapping (CAM) was used to enhance the model's explainability. The research concluded with recommendations for model refinement and external validation.

Automated Meniscus Tear Detection in Knee MRI Scans NotebookLM Podcast

Sook-Yee Chong