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Related Concept Videos

Cranial Bones: Lateral View01:27

Cranial Bones: Lateral View

The lateral view of the cranium is dominated by temporal, sphenoid, and ethmoid bones.
The temporal bone forms the lower lateral side of the skull. The temporal bone is subdivided into several regions. The flattened upper portion is the squamous portion of the temporal bone. Below this area and projecting anteriorly is the zygomatic process of the temporal bone, which forms the posterior portion of the zygomatic arch. Posteriorly is the mastoid portion of the temporal bone. Projecting...
Sutures of the Skull01:22

Sutures of the Skull

The human skull is composed of several bones that come together to protect the brain and support the structures of the face. The junctions where these bones meet are called sutures.
Sutures are immobile joints between adjacent bones of the skull. The narrow gap between the bones is filled with dense, fibrous connective tissue that unites the bones. The long sutures located between the skull bones are not straight but instead follow irregular, tightly twisting paths. These twisting lines tightly...

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Related Experiment Video

Updated: Jun 16, 2026

How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index
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Quantifying Sagittal Craniosynostosis Severity: A Machine Learning Approach With CranioRate.

Wenzheng Tao1, Tobi J Somorin2, Janina Kueper2

  • 1Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA.

The Cleft Palate-Craniofacial Journal : Official Publication of the American Cleft Palate-Craniofacial Association
|June 27, 2025
PubMed
Summary

Machine learning models accurately quantify sagittal craniosynostosis (SCS) severity, improving clinical assessment. The Sagittal Severity Score (SSS) and Cranial Morphology Deviation (CMD) offer objective, data-driven tools for enhanced patient care.

Keywords:
artificial intelligencecraniofacial morphologycraniofacial surgerycraniosynostosisimaging

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Pediatric Surgery

Background:

  • Sagittal craniosynostosis (SCS) requires objective severity quantification for effective management.
  • Current assessment methods may lack comprehensive objectivity and consistency.
  • Machine learning (ML) offers potential for advanced diagnostic tools.

Purpose of the Study:

  • To develop and validate ML models for objective SCS severity quantification.
  • To enhance clinical assessment, management, and research in craniosynostosis.
  • To compare novel ML-based scores against traditional metrics.

Main Methods:

  • Cross-sectional study combining CT scan analysis and expert surgeon ratings.
  • Development of the Sagittal Severity Score (SSS) and unsupervised Cranial Morphology Deviation (CMD) model.
  • Comparison of SSS and CMD with cephalic index (CI) and expert Likert ratings.

Main Results:

  • SSS demonstrated significantly higher accuracy than CI in predicting expert ratings.
  • Skin-based landmarks showed equivalent predictive power to skull-based landmarks.
  • CMD strongly correlated with SSS, offering a rating-free alternative.

Conclusions:

  • SSS and CMD provide accurate, consistent, and comprehensive SCS severity quantification.
  • Data-driven ML models can standardize craniosynostosis assessments.
  • Implementation of these models promises enhanced precision and informed surgical planning.