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Convolutional Neural Network Models for Automatic Preoperative Severity Assessment in Unilateral Cleft Lip
Meghan McCullough1, Steven Ly1, Allyn Auslander1
1From the Division of Plastic Surgery, Keck School of Medicine, and Department of Computer Science, University of Southern California; Division of Plastic Surgery, Children's Hospital of Los Angeles; Operation Smile; and Department of Plastic and Reconstructive Surgery, Shriners Hospital for Children.
Machine learning accurately measures facial landmarks and assigns severity grades for cleft lip morphology. This automated approach shows promise for clinical decision-making and patient counseling.
Area of Science:
- Medical imaging analysis
- Machine learning in healthcare
- Craniofacial anomaly assessment
Background:
- Lack of standardized scales for preoperative cleft lip severity.
- Machine learning (ML) has not been applied to cleft lip disease classification.
- Need for objective and automated methods for assessing cleft lip morphology.
Purpose of the Study:
- To develop and evaluate ML models for automated detection and measurement of facial landmarks in cleft lip patients.
- To assess the feasibility of using ML for assigning preoperative severity grades for cleft lip.
- To explore the potential of ML in standardizing cleft lip morphology classification.
Main Methods:
- Trained five convolutional neural network (CNN) models on 800 preoperative unilateral cleft lip images.
- Manually annotated images for cleft-specific landmarks and used expert ratings for severity.
- Calculated mean squared error and Pearson correlation for cleft width, nostril width, and severity grade assignment.
Main Results:
- All five CNN models demonstrated good performance in landmark detection and severity grading.
- The Residual Network model achieved the highest accuracy (severity correlation: 0.892).
- MobileNet showed high accuracy (severity correlation: 0.860) and is compatible with mobile devices.
Conclusions:
- ML models can accurately measure facial features and assign cleft lip severity grades.
- Automated ML approaches offer a promising solution for classifying cleft lip morphology.
- Potential for mobile applications to provide real-time clinical decision support and patient counseling.
