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

  • Leite, Andre Ferreira;
  • Van Gerven, Adriaan;
  • Willems, Holger;
  • Beznik, Thomas;
  • Lahoud, Pierre; 141971;
  • Gaeta-Araujo, Hugo;
  • Vranckx, Myrthel; 111660;
  • Jacobs, Reinhilde; 14109;
Publication Date
Aug 26, 2020
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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. / status: published

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