Effectiveness of Automatic Planning of Fronto-orbital Advancement for the Surgical Correction of Metopic Craniosynostosis.


Journal

Plastic and reconstructive surgery. Global open
ISSN: 2169-7574
Titre abrégé: Plast Reconstr Surg Glob Open
Pays: United States
ID NLM: 101622231

Informations de publication

Date de publication:
Nov 2021
Historique:
received: 22 07 2021
accepted: 15 09 2021
entrez: 17 11 2021
pubmed: 18 11 2021
medline: 18 11 2021
Statut: epublish

Résumé

The surgical correction of metopic craniosynostosis usually relies on the subjective judgment of surgeons to determine the configuration of the cranial bone fragments and the degree of overcorrection. This study evaluates the effectiveness of a new approach for automatic planning of fronto-orbital advancement based on statistical shape models and including overcorrection. This study presents a planning software to automatically estimate osteotomies in the fronto-orbital region and calculate the optimal configuration of the bone fragments required to achieve an optimal postoperative shape. The optimal cranial shape is obtained using a statistical head shape model built from 201 healthy subjects (age 23 ± 20 months; 89 girls). Automatic virtual plans were computed for nine patients (age 10.68 ± 1.73 months; four girls) with different degrees of overcorrection, and compared with manual plans designed by experienced surgeons. Postoperative cranial shapes generated by automatic interventional plans present accurate matching with normative morphology and enable to reduce the malformations in the fronto-orbital region by 82.01 ± 6.07%. The system took on average 19.22 seconds to provide the automatic plan, and allows for personalized levels of overcorrection. The automatic plans with an overcorrection of 7 mm in minimal frontal breadth provided the closest match (no significant difference) to the manual plans. The automatic software technology effectively achieves correct cranial morphometrics and volumetrics with respect to normative cranial shapes. The automatic approach has the potential to reduce the duration of preoperative planning, reduce inter-surgeon variability, and provide consistent surgical outcomes.

Sections du résumé

BACKGROUND BACKGROUND
The surgical correction of metopic craniosynostosis usually relies on the subjective judgment of surgeons to determine the configuration of the cranial bone fragments and the degree of overcorrection. This study evaluates the effectiveness of a new approach for automatic planning of fronto-orbital advancement based on statistical shape models and including overcorrection.
METHODS METHODS
This study presents a planning software to automatically estimate osteotomies in the fronto-orbital region and calculate the optimal configuration of the bone fragments required to achieve an optimal postoperative shape. The optimal cranial shape is obtained using a statistical head shape model built from 201 healthy subjects (age 23 ± 20 months; 89 girls). Automatic virtual plans were computed for nine patients (age 10.68 ± 1.73 months; four girls) with different degrees of overcorrection, and compared with manual plans designed by experienced surgeons.
RESULTS RESULTS
Postoperative cranial shapes generated by automatic interventional plans present accurate matching with normative morphology and enable to reduce the malformations in the fronto-orbital region by 82.01 ± 6.07%. The system took on average 19.22 seconds to provide the automatic plan, and allows for personalized levels of overcorrection. The automatic plans with an overcorrection of 7 mm in minimal frontal breadth provided the closest match (no significant difference) to the manual plans.
CONCLUSIONS CONCLUSIONS
The automatic software technology effectively achieves correct cranial morphometrics and volumetrics with respect to normative cranial shapes. The automatic approach has the potential to reduce the duration of preoperative planning, reduce inter-surgeon variability, and provide consistent surgical outcomes.

Identifiants

pubmed: 34786322
doi: 10.1097/GOX.0000000000003937
pmc: PMC8589244
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e3937

Subventions

Organisme : NIDCR NIH HHS
ID : K99 DE027993
Pays : United States
Organisme : NICHD NIH HHS
ID : R42 HD081712
Pays : United States

Informations de copyright

Copyright © 2021 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of The American Society of Plastic Surgeons.

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Auteurs

David García-Mato (D)

Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain.
Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain.

Antonio R Porras (AR)

Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, D.C.
Department of Biostatistics and Informatics - Colorado School of Public Health, Department of Pediatrics - School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colo.

Santiago Ochandiano (S)

Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain.
Servicio de Cirugía Oral y Maxilofacial, Hospital General Universitario Gregorio Marañón, Madrid, Spain.

Gary F Rogers (GF)

Division of Plastic and Reconstructive Surgery, Children's National Hospital, Washington, D.C.

Roberto García-Leal (R)

Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain.
Servicio de Neurocirugía, Hospital General Universitario Gregorio Marañón, Madrid, Spain.

José I Salmerón (JI)

Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain.
Servicio de Cirugía Oral y Maxilofacial, Hospital General Universitario Gregorio Marañón, Madrid, Spain.

Javier Pascau (J)

Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain.
Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain.

Marius George Linguraru (MG)

Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, D.C.
School of Medicine and Health Sciences, George Washington University, Washington, D.C.

Classifications MeSH