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Quantifying the Severity of Metopic Craniosynostosis: A Pilot Study Application of Machine Learning in Craniofacial Surgery.
Bhalodia, Riddhish; Dvoracek, Lucas A; Ayyash, Ali M; Kavan, Ladislav; Whitaker, Ross; Goldstein, Jesse A.
Afiliación
  • Bhalodia R; School of Computing, University of Utah, Salt Lake City, UT.
  • Dvoracek LA; Department of Plastic Surgery, UPMC Children's Hospital, University of Pittsburgh Medical Center, Pittsburgh, PA.
  • Ayyash AM; Department of Plastic Surgery, UPMC Children's Hospital, University of Pittsburgh Medical Center, Pittsburgh, PA.
  • Kavan L; School of Computing, University of Utah, Salt Lake City, UT.
  • Whitaker R; School of Computing, University of Utah, Salt Lake City, UT.
  • Goldstein JA; Department of Plastic Surgery, UPMC Children's Hospital, University of Pittsburgh Medical Center, Pittsburgh, PA.
J Craniofac Surg ; 31(3): 697-701, 2020.
Article en En | MEDLINE | ID: mdl-32011542
ABSTRACT
The standard for diagnosing metopic craniosynostosis (CS) utilizes computed tomography (CT) imaging and physical exam, but there is no standardized method for determining disease severity. Previous studies using interfrontal angles have evaluated differences in specific skull landmarks; however, these measurements are difficult to readily ascertain in clinical practice and fail to assess the complete skull contour. This pilot project employs machine learning algorithms to combine statistical shape information with expert ratings to generate a novel objective method of measuring the severity of metopic CS.Expert ratings of normal and metopic skull CT images were collected. Skull-shape analysis was conducted using ShapeWorks software. Machine-learning was used to combine the expert ratings with our shape analysis model to predict the severity of metopic CS using CT images. Our model was then compared to the gold standard using interfrontal angles.Seventeen metopic skull CT images of patients 5 to 15 months old were assigned a severity by 18 craniofacial surgeons, and 65 nonaffected controls were included with a 0 severity. Our model accurately correlated the level of skull deformity with severity (P < 0.10) and predicted the severity of metopic CS more often than models using interfrontal angles (χ = 5.46, P = 0.019).This is the first study that combines shape information with expert ratings to generate an objective measure of severity for metopic CS. This method may help clinicians easily quantify the severity and perform robust longitudinal assessments of the condition.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Cráneo / Craneosinostosis / Cara Tipo de estudio: Prognostic_studies Límite: Humans / Infant Idioma: En Revista: J Craniofac Surg Asunto de la revista: ODONTOLOGIA Año: 2020 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Cráneo / Craneosinostosis / Cara Tipo de estudio: Prognostic_studies Límite: Humans / Infant Idioma: En Revista: J Craniofac Surg Asunto de la revista: ODONTOLOGIA Año: 2020 Tipo del documento: Article
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