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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS DHF 4500 ARTIFICIAL INTELLIGENCE APPLICATIONS IN DENTISTRY ELECTIVE 1 0 0 1

Offered By

Dentist

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSISTANT PROFESSOR FATMA AKKOCA

Offered to

Dentist

Course Objective

Providing training on the use of artificial intelligence and deep learning methods in different areas of dentistry

Learning Outcomes of the Course Unit

1   Defines artificial intelligence, its historical development, and fundamental methods. 2   Explains artificial neural networks, machine learning, and deep learning types with examples. 3   Explains data collection, labeling, model training, and optimization processes in AI projects. 4   Describes performance metrics of AI models (accuracy, precision, recall, F1, AUC). 5   Explains the ethical aspects and potential risks of AI applications. 6   Explains the applications of AI in oral and maxillofacial radiology, periodontology, orthodontics, surgery, prosthodontics, and pedodontics. 7   Explains the contributions of forensic dentistry and Internet of Things (IoT) applications.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Introduction What is Artificial Intelligence 2 Artificial Neural Networks and Their Subtypes (ANN, CNN, RNN, DNN) 3 Types of Machine Learning (Supervised, Unsupervised, Reinforcement Learning) - Deep Learning and Its Historical Development 4 Artificial Intelligence and Ethics 5 General Importance and Application Areas of Artificial Intelligence in Dentistry 6 AI Models and the Training Process (Data Collection, Labeling, Model Training, Testing, Optimization) 7 Performance Measures of AI Models (Accuracy, Precision, Recall, F1, AUC) 8 AI Applications in Oral and Maxillofacial Radiology 9 AI Applications in Periodontics 10 AI Applications in Orthodontics 11 AI Applications in Oral and Maxillofacial Surgery 12 AI Applications in Prosthetic Dentistry 13 AI Applications in Endodontics, Restorative and Pediatric Dentistry 14 Forensic Dentistry and Internet of Things (IoT) Applications

Recomended or Required Reading

Mehrabanian, M. Artificial intelligence in dentistry. Br Dent J 235, 176 (2023). https://doi.org/10.1038/s41415-023-6183-0

Planned Learning Activities and Teaching Methods

1. Lesson 2. Project

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 FIN FINAL EXAM 3 FCG FINAL COURSE GRADE roundStudent.examMTE * 0.40 + Student.examFN * 0.60,0 4 RST RESIT 5 FCGR FINAL COURSE GRADE (RESIT) roundMTE * 0.40 + RST * 0.60,0

Further Notes About Assessment Methods

None

Assessment Criteria

1. Can promote artificial intelligence 2. Areas of use in artificial intelligence and dentistry can be listed. 3. Internet of things applications in dentistry can be listed 4. Explain what artificial intelligence and ethics are 5. Can explain artificial intelligence and its use in education

Language of Instruction

Turkish

Course Policies and Rules

Attendance at 70% of classes is mandatory.

Contact Details for the Lecturer(s)

fatma.akkoca[at]deu.edu.tr

Office Hours

Thursday 09.00-10.00

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 1 1 TOTAL WORKLOAD (hours) 0

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 PO.9 PO.10 LO.1 4 4 LO.2 4 2 3 4 LO.3 4 5 4 2 3 LO.4 3 3 3 3 3 3 3 3 3 3 LO.5 3 3 3 3 3 3 3 3 3 3 LO.6 3 3 3 3 3 3 3 3 3 3 LO.7 3 3 3 3 3 3 3 3 3 3