Artificial intelligence in orthodontics

Purpose The aim of this investigation was to create an automated cephalometric X‑ray analysis using a specialized artificial intelligence (AI) algorithm. We compared the accuracy of this analysis to the current gold standard (analyses performed by human experts) to evaluate precision and clinical application of such an approach in orthodontic routine. Methods For training of the network, 12 experienced examiners identified 18 landmarks on a total of 1792 cephalometric X‑rays. To evaluate quality of the predictions of the AI, both AI and each examiner analyzed 12 commonly used orthodontic parameters on a basis of 50 cephalometric X‑rays that were not part of the training data for the AI. Median values of the 12 examiners for each parameter were defined as humans’ gold standard and compared to the AI’s predictions. Results There were almost no statistically significant differences between humans’ gold standard and the AI’s predictions. Differences between the two analyses do not seem to be clinically relevant. Conclusions We created an AI algorithm able to analyze unknown cephalometric X‑rays at almost the same quality level as experienced human examiners (current gold standard). This study is one of the first to successfully enable implementation of AI into dentistry, in particular orthodontics, satisfying medical requirements. Ziel Ziel der vorliegenden Untersuchung war, eine vollständig automatisierte Fernröntgenseitenanalyse auf Basis eines spezialisierten künstlichen neuronalen Netzwerkes zu entwickeln. Die Genauigkeit dieser Analyse wurde mit der Auswertung menschlicher Experten, dem aktuellem Goldstandard, verglichen, um die Eignung eines solchen Systems zur Verwendung im klinischen Alltag zu überprüfen. Patienten und Methodik Für das Training des Netzwerkes wurden auf insgesamt 1792 Fernröntgenseitenbildern jeweils 18 kieferorthopädische Bezugspunkte durch 12 erfahrene Untersucher markiert. Zur Beurteilung der Auswertungsqualität des trainierten Netzwerkes wurden an 50 weiteren Fernröntgenseitenbildern, die nicht Teil der Trainingsdaten waren, sowohl durch die Künstliche Intelligenz (KI) als auch durch jeden der Untersucher 12 gängige kephalometrische Messungen durchgeführt. Als Goldstandard wurde für jeden Parameter der Medianwert der Untersucher definiert, welcher dann mit den Auswertungen der KI verglichen wurde. Ergebnisse Zwischen den Auswertungen der KI und dem menschlichen Goldstandard konnten nahezu keine statistisch signifikanten Unterschiede festgestellt werden. Sofern Unterschiede zwischen beiden Auswertungsmethoden bestanden, konnten diese als klinisch irrelevant betrachtet werden. Schlussfolgerungen Im Rahmen der vorliegenden Untersuchung war es möglich, ein künstliches neuronales Netzwerk darauf zu trainieren, unbekannte Fernröntgenseitenbilder in annähernd derselben Qualität wie der eines erfahrenen Klinikers auszuwerten. Diese Studie ist einer der ersten erfolgreichen Ansätze, künstliche neuronale Netzwerke in der Zahnmedizin, speziell in der Kieferorthopädie, zu integrieren.

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