Artificial Intelligence-aimed Point-of-care Ultrasound Image Interpretation System

This proposal is for an one-year project. In this project, we aim to investigate the feasibility of using AI for sonographic image interpretation. The main project is responsible for coordination between the two sub-projects and the main project, providing image resources, and using U-Net (Convolutional Networks for Biomedical Image Segmentation) and Transfer Learning to build up the models for image recognition and validating the efficacy of the models. The purpose of Subproject 1 is to develop an image recognition system for dynamic images: pericardial effusion. After building up the model, validating the efficacy and future revision will be done. Subproject 2 comes out an image recognition system for static images: hydronephrosis. After building up the model, validating the efficacy and future revision will be done.