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The Development of a European Registry for Facial Dysostosis Syndromes: A Delphi-Guided Approach
Victor L van Roey1,2, Saranda Ombashi1,2, Irene M J Mathijssen1,2
1European Reference Network for Rare and/or Complex Craniofacial Anomalies and Ear, Nose, and Throat Disorders, Rotterdam, The Netherlands.
A new international dataset for facial dysostosis syndromes (FDS) was created to standardize monitoring and improve care. This registry facilitates research and collaboration for rare craniofacial conditions.
Area of Science:
- Genetics and Rare Diseases
- Craniofacial Anomalies
- Clinical Data Management
Background:
- Facial dysostosis syndromes (FDS) are rare congenital conditions with significant functional and aesthetic impacts.
- Lack of standardized monitoring protocols hinders FDS research and evidence-based clinical practice.
- The European Reference Network for Rare and Complex Craniofacial Anomalies (ERN CRANIO) initiated the development of a comprehensive dataset.
Purpose of the Study:
- To develop the first international registry dataset for facial dysostosis syndromes.
- To establish standardized data collection elements for FDS research and clinical care.
- To facilitate collaborative research and cross-center comparisons for rare craniofacial anomalies.
Main Methods:
- Systematic literature review (1985-2024) to identify candidate data elements.
- Supplementation with existing ERN CRANIO data and expert panel input.
- A Delphi survey involving 61 clinicians and 3 patient representatives, followed by a hybrid consensus meeting.
Main Results:
- A total of 110 data elements were included in the final FDS dataset.
- Elements are organized into Level 1 (patient-reported outcomes) and Level 2 (clinical characteristics, treatment, outcomes, diagnostics).
- 98 out of 200 initial elements were strongly recommended following the Delphi survey.
Conclusions:
- The developed dataset represents the first international registry for facial dysostosis syndromes.
- This registry holds significant potential for advancing collaborative research and improving patient care globally.
- Real-world implementation is crucial for evaluating feasibility and guiding future dataset refinements.
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