RAD5101 · Essential principles of x-ray image interpretation

Overview

In this unit you will broaden your clinical knowledge and develop fundamental skills in the analysis of plain radiographic images. You will expand your critiquing of radiographic images through the application of a methodical approach when interpreting X-ray images. You will learn to apply principles of visual perception and pattern recognition and statistical skills to reduce interpretation errors. You will learn to discriminate abnormalities from the appearances of normal radiographic anatomy and anatomical variants on plain radiographic images of body regions. You will learn to apply clinical decision-making processes to diagnostic imaging pathways in the context of emergency care. This unit will enable you to construct effective interprofessional communication skills in the acute and non-acute care settings. You will be required to articulate professional knowledge in the appraisal of advances in clinical practice affected by the impact of artificial intelligence on computer-assisted image interpretation of radiographic images.

Offerings

Assessment

The Handbook lists an examination for this unit.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

The Handbook lists no prerequisite, corequisite or prohibition for this unit.

Learning outcomes

1. Analyse radiographic images through the incorporation of systematic and methodological strategies.
2. Distinguish between normal and anatomical variations on X-ray images as applicable to the different body regions.
3. Discriminate between normal and abnormal X-ray cases of various body regions by applying principles of visual perception, pattern recognition and statistical measures.
4. Appraise diagnostic imaging pathways in the context of emergency care.
5. Consolidate reflective skills in image interpretation to enhance effective interprofessional communication in the clinical setting.
6. Appraise advances in clinical practice through the impact of artificial intelligence on computer-assisted radiographic image interpretation.

Workload

6 hours of teacher directed study per week, including 3 hours of lectures and 3 hours of tutorials. 6 hours per week of self-directed study. Total per week = 12 hours

Student experience, not official rules. Nothing here changes what the Handbook says.