⚡ Quick Summary
This study developed a deep learning (DL) model to automate the quality evaluation of dental panoramic radiographs, achieving an impressive average accuracy of 81.4% in classifying images as clinically acceptable or unacceptable. The findings suggest that integrating AI into clinical workflows could enhance diagnostic efficiency and reduce variability in assessments.
🔍 Key Details
- 📊 Dataset: 1,000 panoramic images collected from 2018 to 2023
- 🧩 Features used: Grading criteria for contrast/density, artifacts, coverage area, patient positioning, and overall quality
- ⚙️ Technology: YOLOv8 classification models
- 🏆 Performance: Classification accuracies of 87.2%, 74.1%, 77.3%, 97.9%, and 79.3% for various quality criteria
🔑 Key Takeaways
- 🤖 AI integration in dental radiography can significantly enhance quality assessments.
- 📈 High accuracy of the YOLOv8 models indicates strong potential for clinical application.
- 🧑⚕️ Expert assessment is time-consuming and inconsistent, highlighting the need for automated solutions.
- 🌟 Educational tool: The model could aid dental students in improving radiographic techniques.
- 🔍 Study conducted by a team of trained dentists and researchers.
- 📅 Publication: Imaging Sci Dent, 2025; 55:175-188.
- 💡 Future implications include reducing unnecessary retakes and improving diagnostic accuracy.
📚 Background
Dental panoramic radiographs are crucial for diagnosing various dental conditions. However, issues such as contrast, artifacts, and patient positioning can compromise their quality, leading to potential misdiagnoses. Traditionally, expert assessment has been the gold standard, but it is often subjective and inconsistent. The advent of artificial intelligence presents an opportunity to automate this evaluation process, enhancing both efficiency and accuracy.
🗒️ Study
The study aimed to develop a deep learning-based model for evaluating the quality of dental panoramic radiographs. A dataset of 1,000 images was meticulously graded by two trained dentists using specific criteria. These expert-annotated scores served as the foundation for training and validating five YOLOv8 models, each focusing on a distinct quality aspect.
📈 Results
The YOLOv8 models demonstrated remarkable performance, achieving classification accuracies of 87.2% for artifact detection, 74.1% for coverage area, 77.3% for patient positioning, 97.9% for contrast/density, and 79.3% for overall image quality. The model designed to classify images as clinically acceptable or unacceptable reached an average accuracy of 81.4%, indicating its practical applicability in real-world settings.
🌍 Impact and Implications
The findings from this study underscore the potential of deep learning in revolutionizing the quality assessment of dental radiographs. By automating this process, practitioners can enhance their diagnostic capabilities, reduce inter-rater variability, and ultimately improve patient care. Furthermore, the model could serve as a valuable educational resource for dental students, fostering better understanding and techniques in radiography.
🔮 Conclusion
This research highlights the promising role of artificial intelligence in dental diagnostics, particularly in the automated evaluation of radiograph quality. The high accuracy of the proposed models suggests that they could be seamlessly integrated into clinical workflows, enhancing both efficiency and educational outcomes. As we look to the future, further exploration of AI applications in dentistry is essential for continued improvement in patient care.
💬 Your comments
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Automated quality evaluation of dental panoramic radiographs using deep learning.
Abstract
PURPOSE: Panoramic radiographs are instrumental in dental diagnosis but face quality issues related to contrast, artifacts, positioning, and coverage, which can impact diagnostic accuracy. Although expert assessment is the accepted standard, it is time-consuming and prone to inconsistency. Artificial intelligence offers an automated, objective solution for evaluating radiograph quality, increasing efficiency and reducing inter-rater variability.
MATERIALS AND METHODS: This study aimed to develop a deep learning (DL)-based model for evaluating the quality of dental panoramic radiographs. A dataset of 1,000 panoramic images, collected from 2018 to 2023, was assessed by 2 trained dentists using predefined grading criteria for contrast/density, artifact presence, coverage area, patient positioning, and overall quality. These expert-annotated scores were used as the ground truth to train and validate 5 YOLOv8 classification models, each targeting a specific quality criterion. The models’ performance was evaluated on a separate test set using performance metrics.
RESULTS: The YOLOv8 models achieved classification accuracies of 87.2%, 74.1%, 77.3%, 97.9%, and 79.3% for artifact detection, coverage area, patient positioning, contrast/density, and overall image quality, respectively. The model used to classify images as clinically acceptable or unacceptable exhibited an average accuracy of 81.4%, demonstrating its potential for real-world application.
CONCLUSION: These findings highlight the feasibility of DL-based automated image quality assessment for panoramic radiographs. The high accuracy of the proposed model suggests its potential integration into clinical workflows to assist practitioners in efficiently evaluating radiograph quality. Additionally, such a model could represent an educational tool for dental students, improving radiographic techniques and reducing unnecessary retakes.
Author: [‘Ameli N’, ‘Miri Moghaddam M’, ‘Lai H’, ‘Pacheco-Pereira C’]
Journal: Imaging Sci Dent
Citation: Ameli N, et al. Automated quality evaluation of dental panoramic radiographs using deep learning. Automated quality evaluation of dental panoramic radiographs using deep learning. 2025; 55:175-188. doi: 10.5624/isd.20240232