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Automated Workflow for Processing and Classifying Dental Radiographs: A Hands-On Approach.
Rajmohan Sivamani Chidambaram1, Ragavesh Dhandapani2, Sudha Rajmohan3
1Department of Prosthodontics, Oman Dental College, Muscat, OMN.
Cureus
|June 26, 2025
Summary
An automated workflow using Convolutional Neural Networks (CNNs) efficiently classifies large dental radiograph datasets. AlexNet achieved 95.98% detection, demonstrating improved accuracy and efficiency in dental imaging analysis.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Machine Learning
Background:
- Dental radiograph classification is crucial for data management, retrieval, and AI development.
- Current methods can be inefficient for large datasets.
- An automated workflow can streamline classification processes.
Purpose of the Study:
- To develop an automated workflow for classifying dental radiographs.
- To improve the efficiency and accuracy of dental radiograph classification using machine learning.
Main Methods:
- A cross-sectional machine learning study utilized 48,329 dental radiographs.
- A workflow was developed involving DICOM to JPEG conversion and image preprocessing.
- Convolutional Neural Networks (CNNs), including AlexNet and ResNet-50, were trained and tested.
Main Results:
- AlexNet achieved the highest performance with a 95.98% detection rate and no errors.
- ResNet-50 achieved 92.3% accuracy with 194 errors.
- A custom CNN model had a 77.25% detection rate with 1,623 errors.
Conclusions:
- An effective automated workflow for dental radiograph classification was established.
- CNN models significantly enhance the accuracy and efficiency of classifying dental radiographs.
- This automated approach supports clinical and research applications in dentistry.
