Insights into Predicting Tooth Extraction from Panoramic Dental Images: Artificial Intelligence vs. Dentists.
Ila Motmaen, Kunpeng Xie, Leon Schönbrunn, Jeff Berens, Kim Grunert, Anna Maria Plum, Johannes Raufeisen, André Ferreira, Alexander Hermans, Jan Egger, Frank Hölzle, Daniel Truhn, Behrus Puladi
Author Information
Ila Motmaen: Department of Oral and Maxillofacial Surgery, University Hospital Knappschaftskrankenhaus Bochum, 44892, Bochum, Germany. ORCID
Kunpeng Xie: Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany. ORCID
Leon Schönbrunn: Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany. ORCID
Jeff Berens: Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany. ORCID
Kim Grunert: Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany. ORCID
Anna Maria Plum: Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany. ORCID
Johannes Raufeisen: Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany. ORCID
André Ferreira: Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany. ORCID
Alexander Hermans: Visual Computing Institute, Computer Science and Natural Sciences, RWTH Aachen University, 52074, Aachen, Germany. ORCID
Jan Egger: Institute for Artificial Intelligence in Medicine, Essen University Hospital, 45147, Essen, Germany. ORCID
Frank Hölzle: Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany. ORCID
Daniel Truhn: Department of Diagnostic and Interventional Radiology, RWTH Aachen University, 52074, Aachen, Germany. ORCID
Behrus Puladi: Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany. bpuladi@ukaachen.de. ORCID
OBJECTIVES: Tooth extraction is one of the most frequently performed medical procedures. The indication is based on the combination of clinical and radiological examination and individual patient parameters and should be made with great care. However, determining whether a tooth should be extracted is not always a straightforward decision. Moreover, visual and cognitive pitfalls in the analysis of radiographs may lead to incorrect decisions. Artificial intelligence (AI) could be used as a decision support tool to provide a score of tooth extractability. MATERIAL AND METHODS: Using 26,956 single teeth images from 1,184 panoramic radiographs (PANs), we trained a ResNet50 network to classify teeth as either extraction-worthy or preservable. For this purpose, teeth were cropped with different margins from PANs and annotated. The usefulness of the AI-based classification as well that of dentists was evaluated on a test dataset. In addition, the explainability of the best AI model was visualized via a class activation mapping using CAMERAS. RESULTS: The ROC-AUC for the best AI model to discriminate teeth worthy of preservation was 0.901 with 2% margin on dental images. In contrast, the average ROC-AUC for dentists was only 0.797. With a 19.1% tooth extractions prevalence, the AI model's PR-AUC was 0.749, while the dentist evaluation only reached 0.589. CONCLUSION: AI models outperform dentists/specialists in predicting tooth extraction based solely on X-ray images, while the AI performance improves with increasing contextual information. CLINICAL RELEVANCE: AI could help monitor at-risk teeth and reduce errors in indications for extractions.
