Work

Deep Learning Architecture to Infer Kennedy Classification of Partially Edentulous Arches Using Object Detection Techniques and Piecewise Annotations

Year: 2024

Type: article

Abstract: OBJECTIVES: Dental health is integral to overall well-being, with early detection of issues critical for prevention. This research work focuses on utilizing artificial intelligence and deep learning-based object detection techniques for automated detection of common dental issues in orthopantomography x-ray images, including broken roots, periodontally compromised teeth, and the Kennedy classification of partially edentulous arches. METHODS: An orthopantomography dataset has been used to train several models employing various object detection architectures, hyperparameters, and training techniques. The performance of these models was evaluated to select the one with the highest accuracy. This selected model was subsequently deployed for further testing and validation on unseen data to assess its real-world performance and potential for clinical application. RESULTS: The proposed model not only facilitates the classification of the Kennedy classification but also offers detailed information about the arch (maxillary or mandibular) and specifies the affected side of the arch (right or left). It can diagnose multiple dental issues simultaneously within an image, enhancing diagnostic capabilities for dental practitioners. CONCLUSIONS: Despite a small dataset, satisfactory results were achieved through tailored hyperparameters and a piecewise annotation scheme.

Source: International Dental Journal

DOI: 10.1016/j.identj.2024.11.005

Authors: Zohaib Khurshid, Maria Waqas, Shehzad Hasan, Shakeel Kazmi, Muhammad Faheemuddin

Institutions: Chulalongkorn University, King Faisal University, NED University of Engineering and Technology, Shaheed Zulfiqar Ali Bhutto Medical University, Islamabad

Language: English

Topic: Dental Radiography and Imaging

Subfield: Oral Surgery

Field: Dentistry

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,550