An End-to-end Entangled Segmentation and Classification Convolutional Neural Network for Periodontitis Stage Grading from Periapical Radiographic Images
Publication Date
1-1-2021
Document Type
Conference Proceeding
Publication Title
Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
DOI
10.1109/BIBM52615.2021.9669422
First Page
1370
Last Page
1375
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 similarity coefficient (DSC) 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.
Funding Number
2027790
Funding Sponsor
National Science Foundation
Keywords
Classification, Computer Aided Diagnostics, Deep Learning, Periapical Radiographs, Periodontitis, Radiographic Bone Loss, Segmentation
Department
Applied Data Science
Recommended Citation
Tanjida Kabir, Chun Teh Lee, Jiman Nelson, Sally Sheng, Hsiu Wan Meng, Luyao Chen, Muhammad F. Walji, Xioaqian Jiang, and Shayan Shams. "An End-to-end Entangled Segmentation and Classification Convolutional Neural Network for Periodontitis Stage Grading from Periapical Radiographic Images" Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 (2021): 1370-1375. https://doi.org/10.1109/BIBM52615.2021.9669422
