AI-Assisted Second Harmonic Generation Microscopy for Detecting Corneal Edema

Many individuals lose their sight and have corneal difficulties due to corneal edema, which causes fluid buildup in the cornea. Early detection is crucial for successful therapy, but most diagnostic approaches require subjective clinical views and intrusive techniques, making them less reliable and beneficial. We use deep learning to examine images from Second Harmonic Generation (SHG) microscopes, enabling the automatic identification of ocular edema. Non-invasive SHG microscopy provides precise corneal structural information. We made a deep learning algorithm that can find changes in the architecture of the cornea caused by edema, such as stromal swelling and collagen disarray, in SHG microscopy images. The model extracts features using CNNs. Next, its categorization layers distinguish healthy and swollen corneas. Our system provides accurate, fast, and consistent evaluations to overcome human judgment issues. We tested the recommended approach on several SHG images and found it to be sensitive and precise. This study suggests that deep learning and SHG imaging may help clinicians make more accurate diagnoses, speeding up therapy and improving patient outcomes. Technology also decreases physician workload and tracks eye health in real time.