VIRTUAL SURGERY APPLICATION

AIM

This course aims to provide students with the fundamental theoretical and practical tools used in virtual surgery applications. In particular, it will equip students with the competence to simulate surgical procedures in a virtual environment using mathematical modeling, finite element analysis (FEA), and artificial intelligence simulations. Using these methods, students will acquire skills that contribute to personalized surgical planning and outcome evaluation processes.

CONTENT

This course contains; Fundamentals of Mathematical Modeling and System Dynamics,Modeling of Physical Systems: Mechanical Systems,Differential Equations and Nonlinear Systems,Fundamentals of Finite Element Analysis (FEA): Structural Models,Finite Element Formulation for Structural Systems,Mathematical Modeling for Fluid Mechanics,Fluid-Structure Interaction (FSI) and Finite Element Approach,Modeling for Biomechanical Systems: Blood Flow,Numerical Solution Techniques and Finite Difference Method,Artificial Intelligence Applications in Engineering,Optimization in High-Dimensional Systems,Modeling for Surgical Simulations: Complex Systems,Learning Systems with AI: Simulations,Project Presentations and Discussion.

1. 1

   Students gain a deep understanding of the theoretical principles behind virtual surgery applications, including mathematical modeling, finite element analysis (FEA), and artificial intelligence simulations, enabling them to critically analyze and apply these concepts in a scientific context.

   Taught by: Self Study Method, Question - Answer Technique, Simulation Technique, Computer-Internet Supported Instruction

2. 2

   By the end of the course, students will be proficient in utilizing simulation tools and methodologies to model and solve complex surgical scenarios, emphasizing the accuracy and validity of the virtual outcomes compared to clinical data.

   Taught by: Problem Solving Method, Case Study Method, Computer-Internet Supported Instruction, Lecture Method

3. 3

   Students integrate knowledge from biomechanics, computational science, and artificial intelligence to address challenges in surgical planning and prediction, fostering a multidisciplinary approach to solving real-world problems in healthcare.

   Taught by: Problem Solving Method, Case Study Method, Self Study Method

4. 4

   Learners will demonstrate the ability to create individualized surgical plans by incorporating patient-specific data into virtual models, enhancing the precision and customization of surgical procedures.

   Taught by: Problem Baded Learning Model, Inquiry-Based Learning, Cooperative Learning · Assessed by: Homework, Quiz

WEEKLY PLAN

1. WEEK 1

   Fundamentals of Mathematical Modeling and System Dynamics

   Preparation: Key concepts in mathematical modeling, differential equations

2. WEEK 2

   Modeling of Physical Systems: Mechanical Systems

   Preparation: Newtonian mechanics, equilibrium equations, mass-spring systems

3. WEEK 3

   Differential Equations and Nonlinear Systems

   Preparation: Linear and nonlinear differential equations, stability analysis

4. WEEK 4

   Fundamentals of Finite Element Analysis (FEA): Structural Models

   Preparation: Mathematical foundations of FEA, weak formulations

5. WEEK 5

   Finite Element Formulation for Structural Systems

   Preparation: Stiffness matrix, boundary conditions, finite element matrix equations

6. WEEK 6

   Mathematical Modeling for Fluid Mechanics

   Preparation: Navier-Stokes equations, fluid dynamics theory

7. WEEK 7

   Fluid-Structure Interaction (FSI) and Finite Element Approach

   Preparation: FSI principles, coupled equations

8. WEEK 8

   Modeling for Biomechanical Systems: Blood Flow

   Preparation: Blood flow modeling, hemorheological models

9. WEEK 9

   Numerical Solution Techniques and Finite Difference Method

   Preparation: Finite difference method, time-stepping techniques

10. WEEK 10

    Artificial Intelligence Applications in Engineering

    Preparation: Artificial neural networks, regression and classification algorithms

11. WEEK 11

    Optimization in High-Dimensional Systems

    Preparation: Gradient-based optimization methods, Hessian matrices

12. WEEK 12

    Modeling for Surgical Simulations: Complex Systems

    Preparation: Modeling multi-system dynamics, challenge and uncertainty analysis

13. WEEK 13

    Learning Systems with AI: Simulations

    Preparation: Deep learning and optimization, simulation with AI

14. WEEK 14

    Project Presentations and Discussion

    Preparation: Presentation and analysis of applied models

ASSESSMENT

• Rate of Midterm Exam to Success50%
• Rate of Final Exam to Success50%

WORKLOAD

READING

• Bathe, K.-J. Finite Element Procedures Chandra, R., Bedi, S. Artificial Intelligence in Surgery: Applications and Research
• ANSYS Student, Fusion 360, Simscale, COMSOL

TEACHING STAFF

• Assist.Prof. Kevser Banu KÖSECOORDINATOR
• Assist.Prof. Kevser Banu KÖSE