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Artificial intelligence in orthodontics

Evaluation of a fully automated cephalometric analysis using a customized convolutional neural network

Künstliche Intelligenz in der Kieferorthopädie

Evaluierung einer vollständig automatisierten Fernröntgenseitenanalyse unter Anwendung eines individualisierten „convolutional neural network“

  • Arnold-Biber Research Award
  • Published:
Journal of Orofacial Orthopedics / Fortschritte der Kieferorthopädie Aims and scope Submit manuscript

Abstract

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.

Zusammenfassung

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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Authors and Affiliations

  1. Poliklinik für Kieferorthopädie, Universitätsklinikum Würzburg, Pleicherwall 2, 97070, Würzburg, Germany

    Felix Kunz &  Angelika Stellzig-Eisenhauer

  2. Zentrum für Klinische Studien, Universitätsklinikum Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany

    Florian Zeman M.Sc.

  3. Poliklinik für Zahnärztliche Prothetik, Universitätsklinikum Würzburg, Pleicherwall 2, 97070, Würzburg, Germany

    Julian Boldt

Authors
  1. Felix Kunz
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  2. Angelika Stellzig-Eisenhauer
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  3. Florian Zeman M.Sc.
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  4. Julian Boldt
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Corresponding author

Correspondence to Felix Kunz.

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Conflict of interest

F. Kunz, A. Stellzig-Eisenhauer, F. Zeman and J. Boldt declare that they have no competing interests.

Additional information

This paper received the Arnold-Biber Research Award of the German Orthodontic Society for the year 2019.

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Kunz, F., Stellzig-Eisenhauer, A., Zeman, F. et al. Artificial intelligence in orthodontics. J Orofac Orthop 81, 52–68 (2020). https://doi.org/10.1007/s00056-019-00203-8

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  • DOI: https://doi.org/10.1007/s00056-019-00203-8

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