A U-Net Approach to Apical Lesion Segmentation onPanoramic Radiographs

Introduction:
- Discusses the use of artificial intelligence (AI) algorithms in medical imaging, specifically in dental radiology.
- Highlights the advantages of panoramic radiography over other imaging techniques in terms of radiation dose and patient comfort.
Materials and Methods:
- Radiographic Data Preparation:
  - Describes the source of the 470 panoramic radiographs used in the study.
  - Mentions the exclusion criteria for images with artifacts.
- Image Annotation:
  - Details the process of ground truth image annotation by experienced dental radiologists.
- Deep CNN Architecture:
  - Explains the use of a U-Net model implemented with PyTorch for semantic segmentation tasks.
  - Provides an overview of the U-Net architecture with encoding and decoding sections, convolutional filters, and skip connections.
Model Pipeline:
- Model Development:
  - Utilizes the PyTorch library for model development in Python.
  - Describes the AI model (CranioCatch) developed for automatic segmentation of apical lesions on panoramic radiographs.
- Data Split and Augmentation:
  - Divides the 470 panoramic radiographs into training, validation, and test groups.
  - Augments the training data set using horizontal and vertical flips.
- Cropping (Preprocessing Step):
  - Divides all images into four parts for further processing.
Results:
- Segmentation Performance:
  - Reports the segmentation results of the AI model on the test data set.
  - Provides sensitivity, precision, and F1-score values for the segmentation of periapical lesions.
Discussion:
- Clinical Implications:
  - Discusses the potential of AI systems to aid in the assessment of periapical pathology based on panoramic radiographs.
- Comparison with Other Studies:
  - Mentions the success of deep learning networks, including CNNs, in medical imaging applications.
Conclusion:
- Summarizes the study's findings regarding the U-Net approach for apical lesion segmentation on panoramic radiographs.

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CranioCatch is a global leader in dental medical technology that improves oral care in the field of dentistry. With AI-supported clinical, educational, and labeling solutions, we provide significant improvements in the diagnosis and treatment of dental diseases using contemporary approaches in advanced machine learning technology.

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