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Program Structure and Responsibility in Short-Term Cleft Surgery Programs: The SCALE Framework.

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Computer Vision for Preoperative Soft-Tissue Landmarking in Cleft Lip Repair: A Systematic Review.

Callum Munns1, Frank Bevacqua2, Tara Najibi2

  • 1Monash Health.

The Journal of Craniofacial Surgery
|February 17, 2026
PubMed
Summary
This summary is machine-generated.

Computer vision, using artificial intelligence (AI), shows promise for improving preoperative planning in cleft lip repair by accurately assessing soft tissues. This technology can enhance surgical consistency and outcomes.

Keywords:
Artificial intelligencecleft lip and palatecomputer visionconvolutional neural networks

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Area of Science:

  • Medical technology
  • Computer vision
  • Artificial intelligence

Background:

  • Cleft lip repair demands meticulous preoperative planning.
  • AI-powered computer vision offers potential for enhanced surgical assessments.
  • This review examines AI's role in preoperative soft-tissue landmarking for cleft lip repair.

Purpose of the Study:

  • To systematically review computer vision applications in preoperative cleft lip repair.
  • To define the scope, methods, and performance of these AI technologies.

Main Methods:

  • Systematic literature search of Medline and Embase databases.
  • Inclusion of studies utilizing computer vision for automated pre-operative soft-tissue analysis.
  • Appraisal of selected studies based on scope, design, and performance metrics.

Main Results:

  • Six studies met inclusion criteria, employing Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and 3D shape analysis.
  • AI models demonstrated high accuracy and efficiency for surgical marking, severity scoring, and symmetry assessment compared to manual methods.
  • Computer vision shows potential as a tool to transfer surgical expertise virtually.

Conclusions:

  • Computer vision holds significant potential for standardizing and improving preoperative planning in cleft lip surgery.
  • Future integration requires focus on ethical design, validation, transparency, and equitable access.