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

TL;DR

This paper presents a deep learning framework that automates SOS fusion staging for skeletal maturity assessment, eliminating the need for manual cropping or segmentation, and improves diagnostic accuracy and efficiency.

Contribution

It introduces a dual-model knowledge distillation approach with a novel loss function for fully automated SOS staging from full images.

Findings

Achieved high diagnostic accuracy in SOS fusion staging.

Eliminated need for external cropping or segmentation tools.

Streamlined the pipeline for clinical deployment.

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.