Journal of Clinical Medicine
Automated Regional Detection of Intervortex Venous Anastomosis on Ultra-Widefield Indocyanine Green Angiography Using a Two-Stage Deep Learning Framework
Par Areum Jeong et Min Sagong.
Contenu en anglais
Résumé
Objectives
This study aimed to develop and validate a deep learning–based system for automated detection of intervortex venous anastomosis (IVA) on ultra-widefield indocyanine green angiography (UWF-ICGA).
Methods
A total of 183 UWF-ICGA images with an original resolution of 3900 × 3072 pixels were included. A two-stage deep learning framework was developed. In the first stage, an object detection model automatically localized four predefined quadrants within each UWF-ICGA image. In the second stage, each extracted quadrant was classified according to the presence or absence of IVA. Quadrants demonstrating definite venous connections between adjacent vortex vein drainage territories were defined as IVA-positive, whereas quadrants without identifiable intervortex venous connections were defined as IVA-negative. Brightness normalization was applied before analysis, and images were resized to 512 × 512 pixels using linear interpolation. The object detection model was trained using 155 images and tested using 28 images. The 183 original images generated 732 quadrant images, of which 622 were used for training and 110 for testing of the classification model. Model performance was evaluated using accuracy, precision, recall, F1 score, and confusion matrices.