An artificial intelligence system using machine-learning for automatic detection and classification of dental restorations in panoramic radiography

Ragda Abdalla-Aslan; Talia Yeshua; Daniel Kabla; Isaac Leichter; Chen Nadler

doi:10.1016/j.oooo.2020.05.012

An artificial intelligence system using machine-learning for automatic detection and classification of dental restorations in panoramic radiography

Oral Surg Oral Med Oral Pathol Oral Radiol. 2020 Nov;130(5):593-602. doi: 10.1016/j.oooo.2020.05.012. Epub 2020 Jun 3.

Authors

Ragda Abdalla-Aslan¹, Talia Yeshua², Daniel Kabla³, Isaac Leichter⁴, Chen Nadler⁵

Affiliations

¹ Researcher, Attending Physician, Department of Oral and Maxillofacial Surgery, Rambam Health Care Campus, Haifa, Israel.
² Lecturer, Department of Applied Physics/Electro-optics Engineering, The Jerusalem College of Technology, Jerusalem, Israel.
³ Department of Electrical and Electronics Engineering, The Jerusalem College of Technology, Jerusalem, Israel.
⁴ Professor Emeritus, Department of Applied Physics/Electro-optics Engineering, The Jerusalem College of Technology, Jerusalem, Israel.
⁵ Lecturer, Oral Maxillofacial Imaging Unit, Oral Medicine Department, the Hebrew University, Hadassah School of Dental Medicine, Ein Kerem, Hadassah Medical Center Jerusalem, Israel. Electronic address: Nadler@hadassah.org.il.

PMID: 32646672
DOI: 10.1016/j.oooo.2020.05.012

Abstract

Objectives: The aim of this study was to develop a computer vision algorithm based on artificial intelligence, designed to automatically detect and classify various dental restorations on panoramic radiographs.

Study design: A total of 738 dental restorations in 83 anonymized panoramic images were analyzed. Images were automatically cropped to obtain the region of interest containing maxillary and mandibular alveolar ridges. Subsequently, the restorations were segmented by using a local adaptive threshold. The segmented restorations were classified into 11 categories, and the algorithm was trained to classify them. Numerical features based on the shape and distribution of gray level values extracted by the algorithm were used for classifying the restorations into different categories. Finally, a Cubic Support Vector Machine algorithm with Error-Correcting Output Codes was used with a cross-validation approach for the multiclass classification of the restorations according to these features.

Results: The algorithm detected 94.6% of the restorations. Classification eliminated all erroneous marks, and ultimately, 90.5% of the restorations were marked on the image. The overall accuracy of the classification stage in discriminating between the true restoration categories was 93.6%.

Conclusions: This machine-learning algorithm demonstrated excellent performance in detecting and classifying dental restorations on panoramic images.

MeSH terms

Algorithms
Artificial Intelligence*
Machine Learning*
Radiography, Panoramic
Support Vector Machine