Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs

• Published in: Clinical oral investigations Vol. 25; no. 4; pp. 2257 - 2267
• Main Authors: Leite, André Ferreira, Gerven, Adriaan Van, Willems, Holger, Beznik, Thomas, Lahoud, Pierre, Gaêta-Araujo, Hugo, Vranckx, Myrthel, Jacobs, Reinhilde
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-04-2021; Springer Nature B.V
• Subjects: Artificial Intelligence; Canine teeth; Dentistry; Medicine; Molar; Molars; Neural networks; Neural Networks, Computer; Original Article; Performance evaluation; Radiography; Radiography, Panoramic; Segmentation; Teeth; Tooth; Tooth; Artificial intelligence; Panoramic radiography; Machine learning; Classification
• ISSN: 1432-6981; 1436-3771
• DOI: 10.1007/s00784-020-03544-6

Objective
To evaluate the performance of a new artificial intelligence (AI)-driven tool for tooth detection and segmentation on panoramic radiographs.
Materials and methods
In total, 153 radiographs were collected. A dentomaxillofacial radiologist labeled and segmented each tooth, serving as the ground truth. Class-agnostic crops with one tooth resulted in 3576 training teeth. The AI-driven tool combined two deep convolutional neural networks with expert refinement. Accuracy of the system to detect and segment teeth was the primary outcome, time analysis secondary. The Kruskal-Wallis test was used to evaluate differences of performance metrics among teeth groups and different devices and chi-square test to verify associations among the amount of corrections, presence of false positive and false negative, and crown and root parts of teeth with potential AI misinterpretations.
Results
The system achieved a sensitivity of 98.9% and a precision of 99.6% for tooth detection. For segmenting teeth, lower canines presented best results with the following values for intersection over union, precision, recall, F1-score, and Hausdorff distances: 95.3%, 96.9%, 98.3%, 97.5%, and 7.9, respectively. Although still above 90%, segmentation results for both upper and lower molars were somewhat lower. The method showed a clinically significant reduction of 67% of the time consumed for the manual.
Conclusions
The AI tool yielded a highly accurate and fast performance for detecting and segmenting teeth, faster than the ground truth alone.
Clinical significance
An innovative clinical AI-driven tool showed a faster and more accurate performance to detect and segment teeth on panoramic radiographs compared with manual segmentation.