Knowledge Distillation Approach for SOS Fusion Staging: Towards Fully Automated Skeletal Maturity Assessment

• URL: http://arxiv.org/abs/2505.21561v1
• Date: Tue, 27 May 2025 02:01:45 GMT
• Title: Knowledge Distillation Approach for SOS Fusion Staging: Towards Fully Automated Skeletal Maturity Assessment
• Authors: Omid Halimi Milani, Amanda Nikho, Marouane Tliba, Lauren Mills, Ahmet Enis Cetin, Mohammed H Elnagar,
• Abstract summary: We introduce a novel deep learning framework for the automated staging of spheno-occipital synchondrosis (SOS) fusion.<n>Our framework attains robust diagnostic accuracy, culminating in a clinically viable end-to-end pipeline.
• Score: 1.0208529247755187
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a novel deep learning framework for the automated staging of spheno-occipital synchondrosis (SOS) fusion, a critical diagnostic marker in both orthodontics and forensic anthropology. Our approach leverages a dual-model architecture wherein a teacher model, trained on manually cropped images, transfers its precise spatial understanding to a student model that operates on full, uncropped images. This knowledge distillation is facilitated by a newly formulated loss function that aligns spatial logits as well as incorporates gradient-based attention spatial mapping, ensuring that the student model internalizes the anatomically relevant features without relying on external cropping or YOLO-based segmentation. By leveraging expert-curated data and feedback at each step, our framework attains robust diagnostic accuracy, culminating in a clinically viable end-to-end pipeline. This streamlined approach obviates the need for additional pre-processing tools and accelerates deployment, thereby enhancing both the efficiency and consistency of skeletal maturation assessment in diverse clinical settings.

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