gms | German Medical Science

22. Jahrestagung des Deutschen Netzwerks Evidenzbasierte Medizin e. V.

Deutsches Netzwerk Evidenzbasierte Medizin e. V.

24. - 26.02.2021, digital

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Artificial intelligence for dental image analysis: a guide for authors and reviewers

Meeting Abstract

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  • Falk Schwendicke - Charité – Universitätsmedizin Berlin, Oral Diagnostics & Digital Health & Health Services Research, Berlin, Germany; ITU/WHO Focus Group on artificial intelligence for health (FG-AI4H), Dental diagnostics and digital dentistry (TG-Dental), Switzerland
  • Akhilanand Chaurasia - King George’s Medical University, Department of Oral Medicine and Radiology, India; ITU/WHO Focus Group on artificial intelligence for health (FG-AI4H), Dental diagnostics and digital dentistry (TG-Dental), Switzerland
  • Tarry Singh - deepkapha, Netherlands ITU/WHO Focus Group on artificial intelligence for health (FG-AI4H); Dental diagnostics and digital dentistry (TG-Dental), Switzerland
  • Jae-Hong Lee - Wonkwang University College of Dentistry, Department of Periodontology, Korea, Republik; ITU/WHO Focus Group on artificial intelligence for health (FG-AI4H), Dental diagnostics and digital dentistry (TG-Dental), Switzerland
  • Robert Gaudin - Charité – Universitätsmedizin Berlin, Department of Maxillofacial Surgery, Berlin, Germany; ITU/WHO Focus Group on artificial intelligence for health (FG-AI4H), Dental diagnostics and digital dentistry (TG-Dental), Switzerland
  • Joachim Krois - Charité – Universitätsmedizin Berlin, Oral Diagnostics & Digital Health & Health Services Research, Berlin, Germany; ITU/WHO Focus Group on artificial intelligence for health (FG-AI4H), Dental diagnostics and digital dentistry (TG-Dental), Switzerland

Who cares? – EbM und Transformation im Gesundheitswesen. 22. Jahrestagung des Deutschen Netzwerks Evidenzbasierte Medizin. sine loco [digital], 24.-26.02.2021. Düsseldorf: German Medical Science GMS Publishing House; 2021. Doc21ebmPS-3-07

doi: 10.3205/21ebm070, urn:nbn:de:0183-21ebm0705

Veröffentlicht: 23. Februar 2021

© 2021 Schwendicke et al.
Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). Lizenz-Angaben siehe http://creativecommons.org/licenses/by/4.0/.

Gliederung

Text

Background/research question: The number of studies employing artificial intelligence (AI), specifically machine and deep learning, for dental image analysis is growing fast. The majority of studies suffer from limitations in planning, conduct and reporting, resulting in low robustness and applicability. We here present a non-authorative guide for authors and reviewers to be applied, discussed and further developed.

Methods: Lending from existing reviews in other fields and founded on the principles of evidence-based research practice, a set of guidance items are presented, assisting future scientists, reviewers and editors in planning, conducting, reporting and evaluating studies on AI in dental image analysis. The items have been derived on a discussion basis within the ITU/WHO focus group “Artificial Intelligence for Health (AI4H)”, and the topic group “Dental diagnostics and digital dentistry”, and should be rigorously appraised and adapted.

Results: Thirty-one items on planning, conducting and reporting studies were devised. These involve items on the study’s wider goal, focus, design and specific aims, data sampling and reporting, sample estimation, reference test construction, model parameters, training and evaluation, uncertainty and explainability, performance metrics and data partitions.

Conclusion: Scientists, reviewers and editors should consider this guide when planning, conducting, reporting and evaluating studies on AI for dental image analysis.

Competing interests: Prof Schwendicke, Dr Gaudin and Dr Krois are founder of a startup for dental image analysis.