An End-to-end Entangled Segmentation and Classification Convolutional Neural Network for Periodontitis Stage Grading from Periapical Radiographic Images

TL;DR

This paper introduces HYNETS, an end-to-end deep learning model that combines segmentation and classification to accurately grade periodontitis stages from radiographic images, aiming to improve diagnostic consistency and efficiency.

Contribution

The novel HYNETS model integrates multi-task learning for simultaneous segmentation and classification, achieving high accuracy and interpretability in periodontitis staging from radiographs.

Findings

Achieved dice coefficient of 0.96 and 0.94 for segmentation tasks.

Achieved an AUC of 0.97 for stage classification.

Provides an interpretable, automated diagnosis support tool.

Abstract

Periodontitis is a biofilm-related chronic inflammatory disease characterized by gingivitis and bone loss in the teeth area. Approximately 61 million adults over 30 suffer from periodontitis (42.2%), with 7.8% having severe periodontitis in the United States. The measurement of radiographic bone loss (RBL) is necessary to make a correct periodontal diagnosis, especially if the comprehensive and longitudinal periodontal mapping is unavailable. However, doctors can interpret X-rays differently depending on their experience and knowledge. Computerized diagnosis support for doctors sheds light on making the diagnosis with high accuracy and consistency and drawing up an appropriate treatment plan for preventing or controlling periodontitis. We developed an end-to-end deep learning network HYNETS (Hybrid NETwork for pEriodoNTiTiS STagES from radiograpH) by integrating segmentation and classification tasks for grading periodontitis from periapical radiographic images. HYNETS leverages a multi-task learning strategy by combining a set of segmentation networks and a classification network to provide an end-to-end interpretable solution and highly accurate and consistent results. HYNETS achieved the average dice coefficient of 0.96 and 0.94 for the bone area and tooth segmentation and the average AUC of 0.97 for periodontitis stage assignment. Additionally, conventional image processing techniques provide RBL measurements and build transparency and trust in the model's prediction. HYNETS will potentially transform clinical diagnosis from a manual time-consuming, and error-prone task to an efficient and automated periodontitis stage assignment based on periapical radiographic images.