Artificial Intelligence in Radiography

At the end of this course, the students should be able to: 1. explain the concept of artificial intelligence in assisting processes and procedures; 2. describe the scenarios that can be automated in Radiography; and 3. articulate clearly how AI can be used in patient documentation, image acquisition, processing, display and extraction of diagnostic information.

Introduction to artificial intelligence: understanding natural languages, knowledge representation, expert systems (CT Scan, MRI, Ultrasonography and many others, Pattern recognition, Medical Image Analysis, image Segmentation, registration, visualisation, computing. Deterministic versus statistical models, global versus local representations of appearances, Neural networks and texture analysis. Principles of mathematical modelling of biological systems, computer algorithms and extraction of qualitative information/automations of systems and processes Deep learning and Machine learning in imaging. Applications of AI in Radiography as it affects patient documentation and data management, Image acquisition, processing, interpretation and storage/retrieval.