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Introducing a new method for classifying skull shape abnormalities related to craniosynostosis
Otto D M Kronig1, Sophia A J Kronig2,3, Henri A Vrooman4,5
1Department of Plastic and Reconstructive Surgery and Hand Surgery, Dutch Craniofacial Centre, Erasmus MC - Sophia Children's Hospital, University Medical Centre Rotterdam, Rotterdam, The Netherlands.
Insights
This study introduces a new, objective method using CT scans to classify skull deformities in craniosynostosis. The technique provides reproducible characteristic curves for each deformity type, aiding diagnosis.
Area of Science:
- Medical Imaging
- Craniofacial Surgery
- Pediatric Radiology
Background:
- Craniosynostosis diagnosis is straightforward, but classification of specific skull deformities remains challenging.
- Current classification techniques for craniosynostosis lack broad applicability.
- Objective and reproducible methods are needed for precise craniosynostosis classification.
Purpose of the Study:
- To present a novel, outline-based classification technique for common craniosynostosis skull deformities.
- To establish an objective and reproducible methodology for analyzing craniosynostosis using CT scans.
- To demonstrate the potential application of this method to 3D photogrammetry.
Main Methods:
- Inclusion of 5 children per craniosynostosis group (scaphocephaly, brachycephaly, trigonocephaly, anterior plagiocephaly) and 5 controls.
- Utilizing OsiriX, MeVisLab, and Matlab for landmark identification, base plane creation, outline segmentation, and graph plotting.
- Measurement of repeatability and reproducibility with analysis of mean curves for each group.
Main Results:
- Excellent intraclass (0.994-1.000) and interclass (0.989-1.000) correlation scores for landmark identification.
- Characteristic forehead curve patterns identified: central peak for controls, scaphocephaly, trigonocephaly, brachycephaly; shifted peak for anterior plagiocephaly.
- Distinctive curve features noted: high forehead peaks (controls, scaphocephaly, trigonocephaly), lowest troughs (scaphocephaly), and highest width/frontal peak ratio (brachycephaly).
Conclusions:
- A preliminary, objective, and reproducible CT-based methodology for craniosynostosis analysis is presented.
- The method generates characteristic curves specific to each craniosynostosis type, aiding classification.
- This technique shows potential for application in 3D photogrammetry for craniofacial analysis.
Abstract:
We present a novel technique for classification of skull deformities due to most common craniosynostosis. We included 5 children of every group of the common craniosynostoses (scaphocephaly, brachycephaly, trigonocephaly, and right- and left-sided anterior plagiocephaly) and additionally 5 controls. Our outline-based classification method is described, using the software programs OsiriX, MeVisLab, and Matlab. These programs were used to identify chosen landmarks (porion and exocanthion), create a base plane and a plane at 4 cm, segment outlines, and plot resulting graphs. We measured repeatability and reproducibility, and mean curves of groups were analyzed. All raters achieved excellent intraclass correlation scores (0.994-1.000) and interclass correlation scores (0.989-1.000) for identifying the external landmarks. Controls, scaphocephaly, trigonocephaly, and brachycephaly all have the peak of the forehead in the middle of the curve (180°). In contrary, in anterior plagiocephaly, the peak is shifted (to the left of graph in right-sided and vice versa). Additionally, controls, scaphocephaly, and trigonocephaly have a high peak of the forehead; scaphocephaly has the lowest troughs; in brachycephaly, the width/frontal peak ratio has the highest value with a low frontal peak.Conclusion: We introduced a preliminary study showing an objective and reproducible methodology using CT scans for the analysis of craniosynostosis and potential application of our method to 3D photogrammetry. What is Known: • Diagnosis of craniosynostosis is relatively simple; however, classification of craniosynostosis is difficult and current techniques are not widely applicable. What is New: • We introduce a novel technique for classification of skull deformities due to craniosynostosis, an objective and reproducible methodology using CT scans resulting in characteristic curves. The method is applicable to all 3D-surface rendering techniques. • Using external landmarks and curve analysis, specific and characteristic curves for every type of craniosynostosis related to the specific skull deformities are found.
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