Artificial Intelligence (AI) in Imaging (RAK_027)
About Study Course
Objective
The study course "Artificial intelligence in imaging" is intended for in-depth understanding of the versatile application of radiology data in clinical medicine using digitization tools.
With the development of technology, the volume of radiology images and data has grown rapidly, increasing the workload of radiologists and requiring more detailed solutions and innovative approaches to solutions for various clinical needs, including the speed of data circulation. In this context, artificial intelligence (AI), which is increasingly integrated into daily practice in today's radiology, offers ample opportunities to improve the diagnostic process of radiology. AI can help prioritize patients with more severe and acute pathologies for faster diagnosis, choose appropriate image acquisition protocols, automate various measurements, image analysis and interpretation, compare current and previous examination images, automate examination description with voice-to-text conversion programs and optimize conclusion standardization, thereby reducing the consumption of resources and the time until the diagnosis is obtained and, therefore, the timely initiation of therapy through a multifaceted approach. This allows radiologists to pay attention to the most complex cases earlier and to facilitate and speed up the diagnostic process, thus improving the quality of patient care. Visual information modeling for individual needs is also needed in stomatology, rehabilitation and traumatology-orthopedics, as well as in other sectors, and AI solutions are becoming more relevant in the evaluation of implants and biomechanics.
Prerequisites
Informatics, Anatomy.
Learning outcomes
1. Students should be able to critically evaluate AI claims and understand the connection between models and clinical realities.
2. Students have a robust conceptual understanding of AI and the structure of clinical data science.
1. Students have been involved in hands-on workshops with the focus - recognizing appropriate potential applications of AI to health data.
2. Understanding how to discern between different methods that can be applied to data (e.g. the distinction between prediction and causal inference approaches).
1. Uses and adapts algorithms for segmentation of imaging data, correction of results obtained by automated programs, choosing the most appropriate program for the task/body part (3D slicer, Lunit, Gleamer), classifies and knows how to apply data types and recommend new solutions for the basic principles of annotation.
2. Describes the most common investigation workflow problems that can be solved with artificial intelligence (list of cases, prioritization features, post-processing algorithm solutions). Offers strategies for how AI can be applied in health data processing - image diagnostics and creating standardized conclusions.
3. Analyzes pathologies and structures in DICOM format that are diagnosed with the help of AI software.
4. Apply Data Security regulations to a certain clinical situations.
Study course planning
| Study programme | Study semester | Program level | Study course category | Lecturers | Schedule |
|---|---|---|---|---|---|
| Medicine, MF | 8 | Master’s | Limited choice | Edgars Edelmers, Maija Radziņa, Nauris Zdanovskis, Laura Saule, Juergen Biederer , Dāvis Sīmanis Putriņš | |
| Medicine, SSNMF | 8 | Master’s | Limited choice | Edgars Edelmers, Maija Radziņa, Nauris Zdanovskis |
| Study programme | Study semester | Program level | Study course category | Lecturers | Schedule |
|---|---|---|---|---|---|
| Medicine, SSNMFz | 8 | Master’s | Limited choice |
