Predictive YOLO V7 Model of Dental Implant for Radiographic Images

Authors

  • Ashwini D. Khairkar Ph.d Scholar , Department of Computer Engineering, SKNCOE,research centre, pune , Dept. of IT,Asst. Prof., Bharati Vidyapeeth College of Engg. for Women,Pune https://orcid.org/0000-0003-4553-3532
  • Sonali Kadam Associate Professor, Department of Computer Engineering, Bharati Vidyapeeth's College of Engineering for Women, Pune, Maharashtra, India https://orcid.org/0000-0002-1306-4113
  • Kanchan Warke Department of Computer Engineering, Bharati Vidyapeeth's College of Engineering for Women, Pune, Maharashtra, India
  • Wagisha Raj Department of Computer Engineering, Bharati Vidyapeeth's College of Engineering for Women, Pune, Maharashtra, India

Keywords:

Artificial Intelligence, Deep Learning, Dental Implants, Prosthodontics, Radiographic Images, YOLOv7

Abstract

Artificial intelligence (AI) has become integral in prosthodontics, revolutionizing patient diagnoses and communication between prosthodontists and patients. AI systems exhibit remarkable efficiency, often rivaling or surpassing expert prosthodontists. This study focuses on utilizing YOLOv7, an advanced YOLO algorithm, for real-time object detection in dental radiographic images. The algorithm, known for its speed and accuracy, proves valuable in identifying missing teeth and determining dental implant types and dimensions. Leveraging a dataset of 4,000 annotated dental radiographic scans, the YOLOv7 model is trained, customized for direct prediction of dental implant dimensions, and adapted loss functions to ensure accuracy. Additionally, it classifies dental implant types, facilitating precise placement within a patient's oral cavity. Evaluation metrics include an impressive overall accuracy of approximately 89%, with a recall of 0.61, precision of 1 at a confidence of 0.895, and an F1-score of 0.35 at a confidence of 0.292. This underscores the model's efficacy in prosthodontic imaging. The study highlights AI's contemporary role in dental prosthetics, emphasizing its effectiveness in diagnosing conditions and creating customized dental prostheses. Ultimately, the integration of AI, exemplified by YOLOv7, contributes significantly to advancing prosthodontic care. The model's architecture, tailored for dental implant prediction and type classification, presents promising results, and further optimization holds the potential for increased precision and reliability in clinical applications.

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References

Basu K, Sinha R, “Artificial Intelligence: How is It Changing Medical Sciences and Its Future?” Indian J Dermatol., Sep-Oct. 2020

Mitali Pareek, “Artificial intelligence in prosthodontics: a scoping paper on current applications and future possibilities”. International Journal of Advances in Medicine, Vol 9, Issue 3, March. 2022

Aviwe Kohlakala, “Deep learning-based dental implant recognition using synthetic X-ray images”, International Federation for Medical and Biological Engineering 2022, August. 2022

Raikar S, “Factors Affecting the Survival Rate of Dental Implants: A Retrospective Study.”, J Int Soc Prev Community Dent., 351-355, Dec. 2017

Revilla-León, “Artificial intelligence applications in implant dentistry: A systematic paper.”, Journal of Prosthetic Dentistry., 2021

Sukegawa S, “Multi-Task Deep Learning Model for Classification of Dental Implant Brand and Treatment Stage Using Dental Panoramic Radiograph Images.”, Biomolecules., May. 2021

Alharbi, “Prediction of Dental Implants Using Machine Learning Algorithms”, Journal of Healthcare Engineering, 2022

Kim HS, “Transfer learning in a deep convolutional neural network for implant fixture classification: A pilot study”, Imaging Sci Dent., 2022

Kurt Bayrakdar, “A deep learning approach for dental implant planning in cone-beam computed tomography images”, BMC Med Imaging 21, 2021

Lee DW, “Artificial Intelligence in Fractured Dental Implant Detection and Classification: Evaluation Using Dataset from Two Dental Hospitals”, Diagnostics (Basel), Feb. 2021

Zhang C, “ Deep learning based dental implant failure prediction from periapical and panoramic films”, Quant Imaging Med Surg, 2023

Lee JH, “Deep learning improves implant classification by dental professionals: a multi-center evaluation of accuracy and efficiency”, J Periodontal Implant Sci., Jun. 2022

Mostafa Sabzekar, “Dental implants success prediction by classifier ensemble on imbalanced data”, Computer Methods and Programs in Biomedicine Update, 2021

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Published

24.03.2024

How to Cite

Khairkar, A. D. ., Kadam, S. ., Warke, K. ., & Raj, W. . (2024). Predictive YOLO V7 Model of Dental Implant for Radiographic Images. International Journal of Intelligent Systems and Applications in Engineering, 12(18s), 656–661. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5015

Issue

Vol. 12 No. 18s (2024)

Section

Research Article

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