Ragda Abdalla-Aslan1, Talia Yeshua2, Daniel Kabla3, Isaac Leichter4, Chen Nadler5. 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.
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.
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.
Authors: Naseer Ahmed; Maria Shakoor Abbasi; Filza Zuberi; Warisha Qamar; Mohamad Syahrizal Bin Halim; Afsheen Maqsood; Mohammad Khursheed Alam Journal: Biomed Res Int Date: 2021-06-22 Impact factor: 3.411
Authors: Ryan Richard Ruff; Bidisha Paul; Maria A Sierra; Fangxi Xu; Xin Li; Yasmi O Crystal; Deepak Saxena Journal: Front Oral Health Date: 2021-07-26