  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.
