Deep Learning Approach for Dental Anomalies X-ray Imaging using YOLOv8

Amelia Ritahani Ismail; Md Salim Sadman Taseen

doi:10.17977/um018v7i22024p164-175

Deep Learning Approach for Dental Anomalies X-ray Imaging using YOLOv8

Amelia Ritahani Ismail, Md Salim Sadman Taseen

Abstract

Dental X-ray imaging is a critical diagnostic tool for identifying various dental anomalies. However, manual interpretation is time-consuming, prone to human error, and requires specialized expertise. Deep learning models, particularly object detection frameworks like YOLO, have demonstrated promising results in automating medical image analysis. This study aims to develop and evaluate a YOLOv8-based deep learning model for automated detection and classification of 14 dental anomaly categories, including Caries, Crowns, Fillings, Implants, and Periapical lesions. The proposed approach addresses limitations in previous YOLO versions by leveraging anchor-free detection and enhanced feature extraction for improved accuracy. The model was trained on a dataset of annotated dental X-ray images and preprocessed with data augmentation techniques to improve generalization. Performance was evaluated using Precision, Recall, F1-score, and Mean Average Precision (mAP). Additional insights were obtained from confusion matrices, precision-recall curves, and training-validation loss curves. The model achieved high precision in detecting Implants (0.90), Crowns (0.89), and Root Canal Treatment (0.69), demonstrating strong potential for clinical applications. However, Caries (0.30) and Periapical lesions (0.15) were detected with lower accuracy, indicating the need for further optimization. Analysis of training loss curves and label distributions suggested that class imbalance and anomaly co-occurrence influenced detection performance. YOLOv8 presents a promising AI-based solution for dental anomaly detection, capable of improving diagnostic efficiency and accuracy in clinical practice. The model’s integration into dental healthcare systems can reduce radiologists' workload and enhance early disease detection, particularly in resource-limited settings.

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References

A. Kumar, H. S. Bhadauria, and A. Singh, “Descriptive analysis of dental X-ray images using various practical methods: A review,” PeerJ Comput. Sci., vol. 7, p. e620, Sep. 2021.

Y.-C. Chen et al., “Improving Dental Implant Outcomes: CNN-Based System Accurately Measures Degree of Peri-Implantitis Damage on Periapical Film,” Bioengineering, vol. 10, no. 6, p. 640, May 2023.

M. Yazdanian et al., “Dental Radiographic/Digital Radiography Technology along with Biological Agents in Human Identification,” Scanning, vol. 2022, pp. 1–30, Jan. 2022.

K.-C. Li et al., “Detection of Tooth Position by YOLOv4 and Various Dental Problems Based on CNN With Bitewing Radiograph,” IEEE Access, vol. 12, pp. 11822–11835, 2024.

B. Beser et al., “YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition,” BMC Med. Imaging, vol. 24, no. 1, p. 172, Jul. 2024.

M. Bayati, B. Alizadeh Savareh, H. Ahmadinejad, and F. Mosavat, “Advanced AI-driven detection of interproximal caries in bitewing radiographs using YOLOv8,” Sci. Rep., vol. 15, no. 1, p. 4641, Feb. 2025.

F. Soheili et al., “Toward Digital Periodontal Health: Recent Advances and Future Perspectives,” Bioengineering, vol. 11, no. 9, p. 937, Sep. 2024.

W. Chen, J. Luo, F. Zhang, and Z. Tian, “A review of object detection: Datasets, performance evaluation, architecture, applications and current trends,” Multimed. Tools Appl., vol. 83, no. 24, pp. 65603–65661, Jan. 2024.

C. Wang, Y. Zhang, Y. Zhou, S. Sun, H. Zhang, and Y. Wang, “Automatic detection of indoor occupancy based on improved YOLOv5 model,” Neural Comput. Appl., vol. 35, no. 3, pp. 2575–2599, Jan. 2023.

L. Xiao, W. Li, S. Yao, H. Liu, and D. Ren, “High-precision and lightweight small-target detection algorithm for low-cost edge intelligence,” Sci. Rep., vol. 14, no. 1, p. 23542, Oct. 2024.

E. Goceri, “Medical image data augmentation: techniques, comparisons and interpretations,” Artif. Intell. Rev., vol. 56, no. 11, pp. 12561–12605, Nov. 2023.

S. Gangwar, R. Devi, and N. A. Mat Isa, “Optimized exposer region-based modified adaptive histogram equalization method for contrast enhancement in CXR imaging,” Sci. Rep., vol. 15, no. 1, p. 6693, Feb. 2025.

A. Abdusalomov et al., “Lightweight Super-Resolution Techniques in Medical Imaging: Bridging Quality and Computational Efficiency,” Bioengineering, vol. 11, no. 12, p. 1179, Nov. 2024.

J. Terven, D. M. Córdova-Esparza, and J. A. Romero-González, “A comprehensive review of yolo architectures in computer vision: From yolov1 to yolov8 and yolo-nas,” Mach. Learn. Knowl. Extr., vol. 5, no. 4, pp. 1680–1716, 2023.

M. Hussain, “YOLOv1 to v8: Unveiling Each Variant–A Comprehensive Review of YOLO,” IEEE Access, vol. 12, pp. 42816–42833, 2024.

Y. Hua, R. Chen, and H. Qin, “YOLO-DentSeg: A Lightweight Real-Time Model for Accurate Detection and Segmentation of Oral Diseases in Panoramic Radiographs,” Electronics, vol. 14, no. 4, p. 805, Feb. 2025.

F. R. S. Teles et al., “Tooth Detection and Numbering in Panoramic Radiographs Using YOLOv8-Based Approach,” in International Conference on Wireless Mobile Communication and Healthcare, 2023, pp. 239–253.

Z. J. Khow, Y.-F. Tan, H. A. Karim, and H. A. A. Rashid, “Improved YOLOv8 Model for a Comprehensive Approach to Object Detection and Distance Estimation,” IEEE Access, vol. 12, pp. 63754–63767, 2024.

N. A. M. Ariff and A. R. Ismail, “Study of adam and adamax optimizers on alexnet architecture for voice biometric authentication system,” in 2023 17th International Conference on Ubiquitous Information Management and Communication (IMCOM), 2023, pp. 1–4.

H. Rezatofighi, N. Tsoi, J. Gwak, A. Sadeghian, I. Reid, and S. Savarese, “Generalized intersection over union: A metric and a loss for bounding box regression,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 658–666.

L. Ruff et al., “A Unifying Review of Deep and Shallow Anomaly Detection,” Proc. IEEE, vol. 109, no. 5, pp. 756–795, May 2021.

P. Bergmann, K. Batzner, M. Fauser, D. Sattlegger, and C. Steger, “The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection,” Int. J. Comput. Vis., vol. 129, no. 4, pp. 1038–1059, Apr. 2021.

A. Chahal, P. Gulia, N. S. Gill, and J. M. Chatterjee, “Performance Analysis of an Optimized ANN Model to Predict the Stability of Smart Grid,” Complexity, vol. 2022, no. 1, Jan. 2022.

G. Alfonso-Francia et al., “Performance Evaluation of Different Object Detection Models for the Segmentation of Optical Cups and Discs,” Diagnostics, vol. 12, no. 12, p. 3031, Dec. 2022.

J. Zheng, H. Li, Q. Wen, Y. Fu, J. Wu, and H. Chen, “Artificial intelligent recognition for multiple supernumerary teeth in periapical radiographs based on faster R-CNN and YOLOv8,” J. Stomatol. Oral Maxillofac. Surg., p. 102293, Feb. 2025.

T. Zirek, M. Ü. Öziç, and M. Tassoker, “AI-Driven localization of all impacted teeth and prediction of winter angulation for third molars on panoramic radiographs: Clinical user interface design,” Comput. Biol. Med., vol. 178, p. 108755, Aug. 2024.

DOI: http://dx.doi.org/10.17977/um018v7i22024p164-175