Automating Bone Loss Measurement on Periapical Radiographs for Predicting the Periodontitis Stage and Grade

Overview

Journal Front Dent Med

Date 2025 Feb 7

PMID 39917648

Authors

Nazila Ameli

Monica Prasad Gibson

Ida Kornerup

Manuel Lagravere

Mark Gierl

Hollis Lai

Affiliations

Soon will be listed here.

Abstract

Background: The aim of this study was to develop and evaluate an automated approach for segmenting bone loss (BL) on periapical (PA) radiographs and predicting the stage and grade of periodontitis.

Methods: One thousand PA radiographs obtained from 572 patients were utilized for training while a separate set of 1,582 images from 210 patients were used for testing. BL was segmented using a U-Net model, which was trained with augmented datasets to enhance generalizability. Apex detection was performed using YOLO-v9, focusing on identifying apexes of teeth to measure root length. Root length was calculated as the distance between the coordinates of detected apexes and center of cemento-enamel junction (CEJ), which was segmented utilizing a U-Net algorithm. BL percentage (ratio of BL to the root length) was used to predict the stage and grade of periodontitis. Evaluation metrics including accuracy, precision, recall, F1-score, Intersection over Union (IoU), mean absolute error (MAE), intraclass correlation coefficients (ICC), and root mean square error (RMSE) were used to evaluate the models' performance.

Results: The U-Net model achieved high accuracy in segmenting BL with 94.9%, 92.9%, and 95.62% on training, validation, and test datasets, respectively. The YOLO-v9 model exhibited a mean Average Precision (mAP) of 66.7% for apex detection, with a precision of 79.6% and recall of 62.4%. The BL percentage calculated from the segmented images and detected apexes demonstrated excellent agreement with clinical assessments, with ICC exceeding 0.94. Stage and grade prediction for periodontitis showed robust performance specifically for advanced stages (III/IV) and grades (C) with an F1-score of 0.945 and 0.83, respectively.

Conclusion: The integration of U-Net and YOLO-v9 models for BL segmentation and apex detection on PA radiographs proved effective in enhancing the accuracy and reliability of periodontitis diagnosis and grading.

References

Celik B, Celik M . Automated detection of dental restorations using deep learning on panoramic radiographs. Dentomaxillofac Radiol. 2022; 51(8):20220244. PMC: 9717396. DOI: 10.1259/dmfr.20220244. View

Lee J, Kim D, Jeong S, Choi S . Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent. 2018; 77:106-111. DOI: 10.1016/j.jdent.2018.07.015. View

Chicco D, Warrens M, Jurman G . The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Comput Sci. 2021; 7:e623. PMC: 8279135. DOI: 10.7717/peerj-cs.623. View

Oh S, Yang J, Kim Y . Discrepancies in periodontitis classification among dental practitioners with different educational backgrounds. BMC Oral Health. 2021; 21(1):39. PMC: 7821642. DOI: 10.1186/s12903-020-01371-5. View

Zhou Z, Siddiquee M, Tajbakhsh N, Liang J . UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation. IEEE Trans Med Imaging. 2019; 39(6):1856-1867. PMC: 7357299. DOI: 10.1109/TMI.2019.2959609. View

Kumar A, Bhadauria H, Singh A . Descriptive analysis of dental X-ray images using various practical methods: A review. PeerJ Comput Sci. 2021; 7:e620. PMC: 8459782. DOI: 10.7717/peerj-cs.620. View

Kaya E, Gunec H, Gokyay S, Kutal S, Gulum S, Ates H . Proposing a CNN Method for Primary and Permanent Tooth Detection and Enumeration on Pediatric Dental Radiographs. J Clin Pediatr Dent. 2022; 46(4):293-298. DOI: 10.22514/1053-4625-46.4.6. View

Shorten C, Khoshgoftaar T, Furht B . Text Data Augmentation for Deep Learning. J Big Data. 2021; 8(1):101. PMC: 8287113. DOI: 10.1186/s40537-021-00492-0. View

Jiang L, Chen D, Cao Z, Wu F, Zhu H, Zhu F . A two-stage deep learning architecture for radiographic staging of periodontal bone loss. BMC Oral Health. 2022; 22(1):106. PMC: 8973652. DOI: 10.1186/s12903-022-02119-z. View

10.

Kim J, Lee H, Song I, Jung K . DeNTNet: Deep Neural Transfer Network for the detection of periodontal bone loss using panoramic dental radiographs. Sci Rep. 2019; 9(1):17615. PMC: 6879527. DOI: 10.1038/s41598-019-53758-2. View

11.

Tonetti M, Greenwell H, Kornman K . Staging and grading of periodontitis: Framework and proposal of a new classification and case definition. J Periodontol. 2018; 89 Suppl 1:S159-S172. DOI: 10.1002/JPER.18-0006. View

12.

Graziani F, Music L, Bozic D, Tsakos G . Is periodontitis and its treatment capable of changing the quality of life of a patient?. Br Dent J. 2019; 227(7):621-625. DOI: 10.1038/s41415-019-0735-3. View

13.

Ertas K, Pence I, Cesmeli M, Ay Z . Determination of the stage and grade of periodontitis according to the current classification of periodontal and peri-implant diseases and conditions (2018) using machine learning algorithms. J Periodontal Implant Sci. 2022; 53(1):38-53. PMC: 9943704. DOI: 10.5051/jpis.2201060053. View

14.

Chen L, Bentley P, Mori K, Misawa K, Fujiwara M, Rueckert D . DRINet for Medical Image Segmentation. IEEE Trans Med Imaging. 2018; 37(11):2453-2462. DOI: 10.1109/TMI.2018.2835303. View

15.

Kim T, Obst C, Zehaczek S, Geenen C . Detection of bone loss with different X-ray techniques in periodontal patients. J Periodontol. 2008; 79(7):1141-9. DOI: 10.1902/jop.2008.070578. View

16.

Parsegian K, Okano D, Chandrasekaran S, Kim Y, Carter T, Shimpi N . The PocketPerio application significantly increases the accuracy of diagnosing periodontal conditions in didactic and chairside settings. Sci Rep. 2024; 14(1):10189. PMC: 11068793. DOI: 10.1038/s41598-024-59394-9. View

17.

LeCun Y, Bengio Y, Hinton G . Deep learning. Nature. 2015; 521(7553):436-44. DOI: 10.1038/nature14539. View

18.

Venkatesh E, Elluru S . Cone beam computed tomography: basics and applications in dentistry. J Istanb Univ Fac Dent. 2018; 51(3 Suppl 1):S102-S121. PMC: 5750833. DOI: 10.17096/jiufd.00289. View

19.

Kim E, Kim S, Kim H, Jeong H, Lee J, Jang J . Prediction of Chronic Periodontitis Severity Using Machine Learning Models Based On Salivary Bacterial Copy Number. Front Cell Infect Microbiol. 2020; 10:571515. PMC: 7701273. DOI: 10.3389/fcimb.2020.571515. View

20.

Pepelassi E . Selection of the most accurate method of conventional radiography for the assessment of periodontal osseous destruction. J Clin Periodontol. 1997; 24(8):557-67. DOI: 10.1111/j.1600-051x.1997.tb00229.x. View