Literature DB >> 26710024

What's in a Name? Accurately Diagnosing Metopic Craniosynostosis Using a Computational Approach.

Benjamin C Wood1, Carlos S Mendoza, Albert K Oh, Emmarie Myers, Nabile Safdar, Marius G Linguraru, Gary F Rogers.   

Abstract

BACKGROUND: The metopic suture is unlike other cranial sutures in that it normally closes in infancy. Consequently, the diagnosis of metopic synostosis depends primarily on a subjective assessment of cranial shape. The purpose of this study was to create a simple, reproducible radiographic method to quantify forehead shape and distinguish trigonocephaly from normal cranial shape variation.
METHODS: Computed tomography scans were acquired for 92 control patients (mean age, 4.2 ± 3.3 months) and 18 patients (mean age, 6.2 ± 3.3 months) with a diagnosis of metopic synostosis. A statistical model of the normal cranial shape was constructed, and deformation fields were calculated for patients with metopic synostosis. Optimal and divergence (simplified) interfrontal angles (IFA) were defined based on the three points of maximum average deformation on the frontal bones and metopic suture, respectively. Statistical analysis was performed to assess the accuracy and reliability of the diagnostic procedure.
RESULTS: The optimal interfrontal angle was found to be significantly different between the synostosis (116.5 ± 5.8 degrees; minimum, 106.8 degrees; maximum, 126.6 degrees) and control (136.7 ± 6.2 degrees; minimum, 123.8 degrees; maximum, 169.3 degrees) groups (p < 0.001). Divergence interfrontal angles were also significantly different between groups. Accuracy, in terms of available clinical diagnosis, for the optimal and divergent angles, was 0.981 and 0.954, respectively.
CONCLUSIONS: Cranial shape analysis provides an objective and extremely accurate measure by which to diagnose abnormal interfrontal narrowing, the hallmark of metopic synostosis. The simple planar angle measurement proposed is reproducible and accurate, and can eliminate diagnostic subjectivity in this disorder. CLINICAL QUESTION/LEVEL OF EVIDENCE: Diagnostic, IV.

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Year:  2016        PMID: 26710024     DOI: 10.1097/PRS.0000000000001938

Source DB:  PubMed          Journal:  Plast Reconstr Surg        ISSN: 0032-1052            Impact factor:   4.730


  13 in total

1.  Personalized Optimal Planning for the Surgical Correction of Metopic Craniosynostosis.

Authors:  Antonio R Porras; D Zukic; A Equobahrie; Gary F Rogers; Marius George Linguraru
Journal:  Clin Image Based Proced       Date:  2016-09-21

2.  Locally affine diffeomorphic surface registration for planning of metopic craniosynostosis surgery.

Authors:  Antonio R Porras; Beatriz Paniagua; Andinet Enquobahrie; Scott Ensel; Hina Shah; Robert Keating; Gary F Rogers; Marius George Linguraru
Journal:  Med Image Comput Comput Assist Interv       Date:  2017-09-04

3.  Premature Fusion of the Sagittal Suture as an Incidental Radiographic Finding in Young Children.

Authors:  Monica Manrique; Esperanza Mantilla-Rivas; Antonio R Porras Perez; Justin R Bryant; Md Sohel Rana; Liyun Tu; Robert F Keating; Albert K Oh; Marius G Linguraru; Gary F Rogers
Journal:  Plast Reconstr Surg       Date:  2021-10-01       Impact factor: 5.169

4.  Occult Scaphocephaly: A Forme Fruste Phenotype of Sagittal Craniosynostosis.

Authors:  Esperanza Mantilla-Rivas; Liyun Tu; Agnes Goldrich; Monica Manrique; Antonio R Porras; Robert F Keating; Albert K Oh; Marius George Linguraru; Gary F Rogers
Journal:  J Craniofac Surg       Date:  2020 Jul-Aug       Impact factor: 1.046

5.  Locally Affine Diffeomorphic Surface Registration and Its Application to Surgical Planning of Fronto-Orbital Advancement.

Authors:  Antonio R Porras; Beatriz Paniagua; Scott Ensel; Robert Keating; Gary F Rogers; Andinet Enquobahrie; Marius George Linguraru
Journal:  IEEE Trans Med Imaging       Date:  2018-07       Impact factor: 10.048

6.  Predictive Statistical Model of Early Cranial Development.

Authors:  Antonio Reyes PorrasPerez; Robert Keating; Janice Lee; Marius George Linguraru
Journal:  IEEE Trans Biomed Eng       Date:  2022-01-20       Impact factor: 4.538

7.  Self-Supervised Discovery of Anatomical Shape Landmarks.

Authors:  Riddhish Bhalodia; Ladislav Kavan; Ross T Whitaker
Journal:  Med Image Comput Comput Assist Interv       Date:  2020-09-29

8.  Quantifying the Severity of Metopic Craniosynostosis: A Pilot Study Application of Machine Learning in Craniofacial Surgery.

Authors:  Riddhish Bhalodia; Lucas A Dvoracek; Ali M Ayyash; Ladislav Kavan; Ross Whitaker; Jesse A Goldstein
Journal:  J Craniofac Surg       Date:  2020 May/Jun       Impact factor: 1.172

9.  Statistical shape modelling to aid surgical planning: associations between surgical parameters and head shapes following spring-assisted cranioplasty.

Authors:  Naiara Rodriguez-Florez; Jan L Bruse; Alessandro Borghi; Herman Vercruysse; Juling Ong; Greg James; Xavier Pennec; David J Dunaway; N U Owase Jeelani; Silvia Schievano
Journal:  Int J Comput Assist Radiol Surg       Date:  2017-05-26       Impact factor: 2.924

10.  Practical Computed Tomography Scan Findings for Distinguishing Metopic Craniosynostosis from Metopic Ridging.

Authors:  Craig B Birgfeld; Carrie L Heike; Faisal Al-Mufarrej; Adam Oppenheimer; Shawn E Kamps; Widya Adidharma; Babette Siebold
Journal:  Plast Reconstr Surg Glob Open       Date:  2019-03-14
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