Advancing Automated Diagnosis of Knee Injuries with Deep Learning and MRI Data through Data-Driven Medical Image Analysis

The integration of data science in medical imaging has opened new avenues for improving diagnostic accuracy. In this study, we propose a deep learning model for the automated diagnosis of knee injuries using MRI data. Our model processes MRI scans and generates diagnostic reports for injuries to the Posterolateral Corner (PLC), Lateral Collateral Ligament (LCL), Posterior Cruciate Ligament (PCL), and knee cartilage. Preliminary results show promising accuracy in detecting these injuries, suggesting that this approach can enhance diagnostic efficiency and reduce human error in interpreting complex MRI data. This method could support clinicians by offering faster, more reliable insights into knee injury assessments.

کلیدواژه ها:

Medical Image Analysis ، Deep Learning ، MRI ، Knee Injuries ، Automated Diagnosis