Deetal-Perio: DEEp denTAL Advisor for Periodontitis Diagnosis based on Two-step Segmentation of Teeth and Gingiva with Lower-dimensional Features

Abstract

Deetal-Perio: DEEp denTAL Advisor for Periodontitis Diagnosis based on Two-step Segmentation of Teeth and Gingiva with Lower-dimensional Features

Haoyang Li1,2, Juexiao Zhou1,3 , Xin Gao1,*

1 Computational Bioscience Research Center (CBRC), Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia

2 MOE Key Laboratory of Symbolic Computation and Knowledge Engineering, College of Computer Science and Technology, Jilin University, Changchun 130012, China

3 Department of Biology, Southern University of Science and Technology, Shenzhen 518055, China

Background

Periodontitis is often known as Gum Disease and is a very common condition in which the gums and deeper periodontal structures become inflamed. This inflammation is the result of response to the invasion bacteria influenced by genetic and lifestyle-associated factors1. Periodontitis usually takes the form of redness, swelling and a tendency to bleed during tooth brushing and the severe periodontitis ranks sixth in the Global Burden of Disease study that affects 11% of the world population2. Also, periodontitis may be a risk factor for cardiovascular disease3 and has an additive effect on development of diabetic complications4. X-ray is a widely used, economy and convenient method to scan the teeth and study the periodontal diseases. Therefore, the prediction of periodontitis based on X-ray image has high practical application value.

Highlights

Lower-dimensional and interpretable features.

Outperforms other state-of-the-art methods.

Reveals the significance of crown-root ratio(CR) as the key feature for periodontitis prediction

Introduction

The majority of the previous works on the prediction of periodontitis focus on mainly two categories of methods, traditional machine learning methods and CNN based methods, while the general form of input data are the raw image or multi-modal data of patients.

Methods

In this project, we predict the class of periodontitis based on X-ray images of patients following two-step segmentation of tooth and gingiva.

• DatasetX-ray images of 300 patients are from dental clinics in China. The contour of teeth, gingiva and the level of periodontitis are annotated by professional dentists.

• Segmentation of Teeth and GingivaThe segmentation of teeth and gingiva is based on our well-trained Mask-RCNN model.

• Prediction and Calibration of Tooth Numbering

The teeth numbering is predicted by both the multi-class Mask-RCNN (exact teeth numbering in the FDI numbering system) and binary Mask-RCNN (is a tooth or not). Then our calibration method will output the final teeth numbering results by integrating the results of both types of Mask-RCNN.

• Calculation of ABL (Feature of Periodontitis)

After the segmentation of teeth and gingiva, for each tooth, the loss of alveolar bone (ABL) is calculated with the largest perpendicular distance of both teeth crown and teeth root to the intersected gingiva. The 32 teeth of each sample will be reorganized into a 1x32 vector for the prediction of periodontitis.

• Prediction of PeriodontitisThe 1x32 vector of teeth ratio is post-processed with interpolation, then the Synthetic Minority Oversampling (SMOTE) is adopted to solve the class-imbalance issue. Next, the XGboost is applied to do the classification of periodontitis.

• Evaluation of MethodsMean average precision (mAP), Dice coefficient, Accuracy and F1-Score are used to evaluate our results.

Results• Our method is powerful for teeth segmentation and numbering

• Our method can handle both 3-Classes and 4-Classes classification and outperforms other compare methods

• Our method is robust with respect to the class size

References

1. Page RC, Kornman KS. The pathogenesis of human periodontitis: An introduction. Periodontol 2000 1997; 14: 9–11.2. Marcenes W, Kassebaum NJ, Bernabé E, et al. Global burden of oral conditions in 1990-2010: A systematic analysis. J Dent Res2013; 92: 592–597.3. Tonetti MS, Van Dyke TE; Working Group 1 of the Joint EFP/AAP Workshop. Periodontitis and atherosclerotic cardiovascular disease: Consensus report of the Joint European Federation of Periodontology and the American Academy of Periodontology Workshop on periodontitis and systemic diseases. J Clin Periodontol 2013; 40(Suppl. 14): S24–S29.

4. Lalla E, Papapanou PN. Diabetes mellitus and periodontitis: A tale of two common interrelated diseases. Nat Rev Endocrinol 2011; 7: 738–748.