This four-hour intensive training course is designed to provide dentists with an understanding and practical experience in applying Machine Learning (ML) and Deep Learning (DL) techniques in Dentistry. The course will focus on Computer Vision applications, specifically semantic and instance segmentation, object detection, and classification of dental images. Participants will gain hands-on experience with Convolutional Neural Networks (CNNs), including advanced CNN architectures like YOLOv5 and YOLOv8, using tools such as Roboflow and Google Colab.

By the end of this course, participants will be able to:

1. Understand the basics of Machine Learning and Deep Learning.

2. Understand Convolutional Neural Networks (CNNs) for Computer Vision tasks.

3. Comprehend the role of Computer Vision in dental image analysis.

4. Utilize enhanced CNNs, specifically YOLOv5 and YOLOv8.

5. Conduct hands-on demo using Roboflow and Google Colab.

Ahmed A. Torky, an Assistant Professor of Engineering at The British University in Egypt (BUE), combines expertise in AI and structural engineering with a deep passion for software development and deep learning. His groundbreaking research enhances structural analysis models and implements AI to tackle engineering and medical challenges. Dr. Torky has authored numerous papers on BIM limitations, high-performance computing, and deep learning for optimization. At BUE, he inspires students through professional courses in artificial intelligence and Python programming, bridging the gap between theory and practical application.

• Basic understanding of dentistry and dental radiographs.

• Familiarity with computers and general software usage.

• Laptop (Windows, Mac, or Linux).

• A dataset of dental radiographs for hands-on practice.

Hour 1: Introduction to Machine Learning and Deep Learning

• 9:00 AM - 9:30 AM: Overview of Machine Learning and Deep Learning.

  • Definitions, concepts, and differences.

  • Importance of ML and DL in healthcare and dentistry.

• 9:30 AM - 10:00 AM: Fundamentals of Neural Networks.

  • Basic structure and functioning.

  • Introduction to Convolutional Neural Networks (CNNs).

Hour 2: Computer Vision in Dentistry

• 10:00 AM - 10:30 AM: Introduction to Computer Vision.

  • Applications in semantic and instance segmentation.

  • Object detection and classification.

• 10:30 AM - 11:00 AM: Practical examples of Computer Vision in Dentistry.

  • Analyzing dental radiographs.

  • Identifying dental conditions and features.

Hour 3: Deep Dive into Convolutional Neural Networks

• 11:00 AM - 11:30 AM: Detailed structure of CNNs.

  • Layers, filters, and feature maps.

  • How CNNs work with images.

• 11:30 AM - 12:00 PM: Enhanced CNNs: YOLOv5 and YOLOv8.

  • Overview of YOLO (You Only Look Once) architecture.

  • Differences and improvements from YOLOv5 to YOLOv8.

Hour 4: Hands-on Practice with Roboflow and Google Colab

• 12:00 PM - 12:30 PM: Introduction to Roboflow.

  • Dataset preparation and augmentation.

  • Creating annotations for dental images.

• 12:30 PM - 1:00 PM: Practical session on Google Colab.

  • Setting up the environment.

  • Running pre-trained models and training custom models.

  • Evaluating model performance.

Note: Participants are encouraged to bring their own datasets of dental radiographs for the hands-on session. This will allow them to directly apply what they've learned to their specific areas of interest and gain more practical insights.

• National dentists: 2000 EGP

• International dentists: 150 USD