Imagine you’re a detective trying to solve a complex case. You have different pieces of evidence scattered throughout the city, each holding a clue to unlock the mystery. Now, think of retinal OCT images as the evidence and doctors as the detectives. Optical Coherence Tomography (OCT) is like a high-tech magnifying glass that allows doctors to examine the eye’s layers, helping them detect diseases like retinopathy. However, not all doctors have the same level of expertise, and some regions may lack medical resources. That’s where AI comes in! Researchers have developed a powerful algorithm called FN-OCT (Fusion Network for OCT) that uses artificial intelligence to analyze retinal OCT images with speed and accuracy. By combining three deep learning algorithms and using fusion strategies, FN-OCT improves the adaptability and precision of traditional classification algorithms. The results are impressive – FN-OCT achieved a 5.3% increase in prediction accuracy compared to previous methods. In addition, it accurately classified common retinal OCT diseases with an impressive accuracy rate of 92%. Gradient-weighted class activation mapping (Grad-CAM) was used to visualize the algorithm’s effectiveness. This breakthrough offers doctors a valuable tool and theoretical support to assist in the diagnosis of retinal OCT diseases. Check out the full research article to dive deeper into this amazing fusion of AI and healthcare!
