Comparison of an unsupervised machine learning algorithm and surgeon diagnosis in the clinical differentiation of metopic craniosynostosis and benign metopic ridge

Min Jeong Cho, Rami R. Hallac, Maleeh Effendi, James R. Seaward, Alex A. Kane

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Metopic suture closure can manifest as a benign metopic ridge (BMR), a variant of normal, to "true" metopic craniosynostosis (MCS), which is associated with severe trigonocephaly. Currently, there is no gold standard for how much associated orbitofrontal dysmorphology should trigger surgical intervention. In our study, we used three-dimensional (3D) curvature analysis to separate the phenotypes along the spectrum, and to compare surgeons' thresholds for operation. Three-dimensional curvature analyses on 43 subject patients revealed that the mean curvature of mid-forehead vertical ridge was higher for patients who underwent operation than those who did not undergo operation by 1.3 m-1 (p < 0.0001). In addition, these patients had more retruded supraorbital areas by -16.1 m-1 (p < 0.0001). K-means clustering classified patients into two different severity groups, and with the exception of 2 patients, the algorithm's classification of deformity completely agreed with the surgeons' decisions to offer either conservative or operative therapy (i.e. 96% agreement). The described methods are effective in classifying severity of deformity and in our experience closely approximate surgeon therapeutic decision making. These methods offer the possibility to consistently determine when surgical intervention may be beneficial and to avoid unnecessary surgeries on children with benign metopic ridge and associated minimal orbitofrontal deformity.

Original languageEnglish (US)
Article number6312
JournalScientific Reports
Volume8
Issue number1
DOIs
StatePublished - Dec 1 2018

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Unnecessary Procedures
Craniosynostoses
Forehead
Sutures
Cluster Analysis
Decision Making
Nonsyndromic Trigonocephaly
Unsupervised Machine Learning
Surgeons
Phenotype
Therapeutics

ASJC Scopus subject areas

  • General

Cite this

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title = "Comparison of an unsupervised machine learning algorithm and surgeon diagnosis in the clinical differentiation of metopic craniosynostosis and benign metopic ridge",
abstract = "Metopic suture closure can manifest as a benign metopic ridge (BMR), a variant of normal, to {"}true{"} metopic craniosynostosis (MCS), which is associated with severe trigonocephaly. Currently, there is no gold standard for how much associated orbitofrontal dysmorphology should trigger surgical intervention. In our study, we used three-dimensional (3D) curvature analysis to separate the phenotypes along the spectrum, and to compare surgeons' thresholds for operation. Three-dimensional curvature analyses on 43 subject patients revealed that the mean curvature of mid-forehead vertical ridge was higher for patients who underwent operation than those who did not undergo operation by 1.3 m-1 (p < 0.0001). In addition, these patients had more retruded supraorbital areas by -16.1 m-1 (p < 0.0001). K-means clustering classified patients into two different severity groups, and with the exception of 2 patients, the algorithm's classification of deformity completely agreed with the surgeons' decisions to offer either conservative or operative therapy (i.e. 96{\%} agreement). The described methods are effective in classifying severity of deformity and in our experience closely approximate surgeon therapeutic decision making. These methods offer the possibility to consistently determine when surgical intervention may be beneficial and to avoid unnecessary surgeries on children with benign metopic ridge and associated minimal orbitofrontal deformity.",
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AU - Kane, Alex A.

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