Veidlapa Nr. M-3 (8)
Study Course Description
Artificial Intelligence (AI) in Imaging
Course Code
RAK_027
Branch of Science
Clinical medicine; Roentgenology and Radiology
ECTS
3.00
Target Audience
Dentistry; Medical Technologies; Medicine; Public Health; Rehabilitation
LQF
Level 7
Study Type And Form
Full-Time
Study Course Implementer
Course Supervisor
Maija Radziņa
Structure Unit Manager
Ardis Platkājis
Structural Unit
Department of Radiology
Contacts
Riga, 2 Hipokrata Street, RSU Study Centre, Administration, rak@rsu.lv, +371 67547139
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.
Preliminary Knowledge
Informatics, Anatomy.
Learning Outcomes
Knowledge
1.1. 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.
Skills
1.1. 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).
Competences
1.1. 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.
Assessment
Individual work
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Title
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% from total grade
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Grade
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|---|---|---|
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1.
Individual work |
-
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-
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Independent work - know how to practically apply MI types in imaging diagnostics (examples from medical imaging data).
In order to evaluate the quality of the study course as a whole, the student must fill out the study course evaluation questionnaire on the Student Portal.
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Examination
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Title
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% from total grade
|
Grade
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|---|---|---|
|
1.
Examination |
-
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-
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Multiple choice test, H5P extension in interactive materials
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||
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2.
Examination |
-
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-
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Credited if the specified types of AI are used in the imaging example; exam.
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Study Course Theme Plan
FULL-TIME
Part 1
Total ECTS (Creditpoints):
1.50
Contact hours:
0 Academic Hours
Final Examination:
Test (Semester)
Part 2
-
Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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Off site
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E-Studies platform
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2
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Topics
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What is artificial intelligence?
Structural elements, metrics and terminology of artificial intelligence
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-
Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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Off site
|
E-Studies platform
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2
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Topics
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Data processing
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-
Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Computer room
|
2
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Topics
|
Practical seminar on data processing and artificial intelligence training I
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-
Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
|
Computer room
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2
|
Topics
|
Practical seminar on data processing and artificial intelligence training II
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-
Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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Off site
|
E-Studies platform
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2
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Topics
|
Intro into Technology, Radioanatomy and Pathology
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-
Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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Off site
|
E-Studies platform
|
2
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Topics
|
Application of artificial intelligence in work organization, image acquisition and analysis
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-
Lecture
|
Modality
|
Location
|
Contact hours
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|---|---|---|
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Off site
|
E-Studies platform
|
2
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Topics
|
Ethical aspects of AI
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
Off site
|
E-Studies platform
|
2
|
Topics
|
AI in medical imaging screening
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-
Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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Off site
|
E-Studies platform
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2
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Topics
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Structured descriptions in medicine, radiology and their importance for artificial intelligence
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-
Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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Off site
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E-Studies platform
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2
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Topics
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Welcoming AI
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-
Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
|
Specialized room
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2
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Topics
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Clinical cases, AI application I
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-
Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
|
Specialized room
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2
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Topics
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Clinical cases, AI application II
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-
Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
|
Specialized room
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2
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Topics
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Clinical cases, AI application III
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-
Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Specialized room
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2
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Topics
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Clinical cases, AI application IV
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-
Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Specialized room
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2
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Topics
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Clinical cases, AI application V
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-
Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
|
On site
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Specialized room
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2
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Topics
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Clinical cases, AI application VI
|
Total ECTS (Creditpoints):
1.50
Contact hours:
32 Academic Hours
Final Examination:
Exam
Bibliography
Required Reading
1.
Tang X. The role of artificial intelligence in medical imaging research. BJR Open. 2019, Nov 28;2(1): 20190031.
2.
Zenker S, Strech D, Ihrig K, et.al. Data protection-compliant broad consent for secondary use of health care data and human biosamples for (bio)medical research: Towards a new German national standard. Journal of Biomedical Informatics, 2022, 131:104096.
3.
Merel Huisman, Elmar Kotter, Peter M. A. van Ooijen Erik R. Ranschaert. Members: Tugba Akinci D’ Antonoli, Marcio Aloisio Bezzera Cavalcanti Rockenbach, Vera Cruz e Silva, Emmanouil Koltsakis, Pinar Yilmaz. The eBook for Undergraduate Education in Radiology. Chapter- AI in Radiology
4.
Hosny, A., Parmar, C., Quackenbush, J. et.al. Artificial intelligence in radiology. Nat Rev Cancer 18, 500–510. 2018
Additional Reading
1.
Geis, J.R., Brady, A., Wu, C.C., et.al. Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement. Insights Imaging 10, 101. 2019.
2.
Nobel, J.M., Kok, E.M. & Robben, S.G.F. Redefining the structure of structured reporting in radiology. Insights Imaging 11, 10. 2020.
3.
Strohm L, Hehakaya C, Ranschaert ER, et.al. Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors. Eur Radiol, 2020, 30:5525–5532.
4.
Simpson SA, Cook TS. Artificial Intelligence and the Trainee Experience in Radiology. Journal of the American College of Radiology, 2020, 17:1388–1393.
5.
Gabriel Chartrand, Phillip M. Cheng, Eugene Vorontsov, et.al. Deep Learning: A Primer for Radiologists. RadioGraphics, 2017 37:7, 2113-2131.
6.
Phillip M. Cheng, Emmanuel Montagnon, Rikiya Yamashita, Ian Pan, et.al. Deep Learning: An Update for Radiologists. RadioGraphics, 2021 41:5, 1427-1445.
7.
Bradley J. Erickson, Panagiotis Korfiatis, Zeynettin Akkus, and Timothy L. Kline. Machine Learning for Medical Imaging. RadioGraphics, 2017 37:2, 505-515.
8.
European Society of Radiology (ESR). The new EU General Data Protection Regulation: what the radiologist should know. Insights Imaging. 2017 Jun;8(3):295-299.