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Oral Radiology

Published in:

15-03-2023 | Artificial Intelligence | Original Article

ResMIBCU-Net: an encoder–decoder network with residual blocks, modified inverted residual block, and bi-directional ConvLSTM for impacted tooth segmentation in panoramic X-ray images

Authors: Andaç Imak, Adalet Çelebi, Onur Polat, Muammer Türkoğlu, Abdulkadir Şengür

Published in: Oral Radiology | Issue 4/2023

Abstract

Objective

Impacted tooth is a common problem that can occur at any age, causing tooth decay, root resorption, and pain in the later stages. In recent years, major advances have been made in medical imaging segmentation using deep convolutional neural network-based networks. In this study, we report on the development of an artificial intelligence system for the automatic identification of impacted tooth from panoramic dental X-ray images.

Methods

Among existing networks, in medical imaging segmentation, U-Net architectures are widely implemented. In this article, for dental X-ray image segmentation, blocks and convolutional block structures using inverted residual blocks are upgraded by taking advantage of U-Net’s network capacity-intensive connections. At the same time, we propose a method for jumping connections in which bi-directional convolution long short-term memory is used instead of a simple connection. Assessment of the proposed artificial intelligence model performance was evaluated with accuracy, F1-score, intersection over union, and recall.

Results

In the proposed method, experimental results are obtained with 99.82% accuracy, 91.59% F1-score, 84.48% intersection over union, and 90.71% recall.

Conclusion

Our findings show that our artificial intelligence system could help with future diagnostic support in clinical practice.

Orhan K, Bilgir E, Bayrakdar IS, Ezhov M, Gusarev M, Shumilov E. Evaluation of artificial intelligence for detecting impacted third molars on cone-beam computed tomography scans. J Stomatol Oral Maxillofac Surg. 2021;122(4):333–7. https://doi.org/10.1016/j.jormas.2020.12.006.CrossRefPubMed

Tajima S, Okamoto Y, Kobayashi T, Kiwaki M, Sonoda C, Tomie K, et al. Development of an automatic detection model using artificial intelligence for the detection of cyst-like radiolucent lesions of the jaws on panoramic radiographs with small training datasets. J Oral Maxillofac Surg Med Pathol. 2022;34(5):553–60. https://doi.org/10.1016/j.ajoms.2022.02.004.CrossRef

Faure J, Engelbrecht A. 2021. Impacted tooth detection in panoramic radiographs. In: International work-conference on artificial neural networks, vol 12861. Springer, Cham, pp 525–536.

Padilla R, Netto SL, da Silva EAB. A survey on performance metrics for object-detection algorithms. In: 2020 ınternational conference on systems, signals and ımage processing (IWSSIP). 2020;237–242. https://doi.org/10.1109/IWSSIP48289.2020.9145130

Geetha V, Aprameya KS, Hinduja DM. Dental caries diagnosis in digital radiographs using back-propagation neural network. Health Inform Sci Syst. 2020;8(1):1–14. https://doi.org/10.1007/s13755-019-0096-y.CrossRef

Cantu AG, Gehrung S, Krois J, Chaurasia A, Rossi JG, Gaudin R, et al. Detecting caries lesions of different radiographic extension on bitewings using deep learning. J Dentistry. 2020;100:103425. https://doi.org/10.1016/j.jdent.2020.103425.CrossRef

Obuchowicz R, Nurzynska K, Obuchowicz B, Urbanik A, Piórkowski A. Caries detection enhancement using texture feature maps of intraoral radiographs. Oral Radiol. 2020;36(3):275–87. https://doi.org/10.1007/s11282-018-0354-8.CrossRefPubMed

Imak A, Celebi A, Siddique K, Turkoglu M, Sengur A, Salam I. Dental caries detection using score-based multi-input deep convolutional neural network. IEEE Access. 2022;10:18320–9. https://doi.org/10.1109/ACCESS.2022.3150358.CrossRef

Lakshmi MM, Chitra P. 2020. Tooth decay prediction and classification from X-ray images using deep CNN. In: Proceedings of the 2020 ınternational conference on communication and signal processing (ICCSP), pp 1349–1355.

10.

Fukuda M, Inamoto K, Shibata N, Ariji Y, Yanashita Y, Kutsuna S, et al. Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography. Oral Radiol. 2020;36(4):337–43. https://doi.org/10.1007/s11282-019-00409-x.CrossRefPubMed

11.

Murata M, Ariji Y, Ohashi Y, Kawai T, Fukuda M, Funakoshi T, et al. Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography. Oral Radiol. 2019;35(3):301–7. https://doi.org/10.1007/s11282-018-0363-7.CrossRefPubMed

12.

Vinayahalingam S, Xi T, Bergé S, Maal T, De Jong G. Automated detection of third molars and mandibular nerve by deep learning. Sci Rep. 2019;9(1):1–7. https://doi.org/10.1038/s41598-019-45487-3.CrossRef

13.

Vranckx M, Ockerman A, Coucke W, Claerhout E, Grommen B, Miclotte A, et al. Radiographic prediction of mandibular third molar eruption and mandibular canal involvement based on angulation. Orthod Craniofac Res. 2019;22(2):118–23. https://doi.org/10.1111/ocr.12297.CrossRefPubMed

14.

Tuzoff DV, Tuzova LN, Bornstein MM, Krasnov AS, Kharchenko MA, Nikolenko SI, et al. Tooth detection and numbering in panoramic radiographs using convolutional neural networks. Dentomaxillofac Radiol. 2019;48(4):20180051. https://doi.org/10.1259/dmfr.20180051.CrossRefPubMedPubMedCentral

15.

Imak A, Çelebi A, Türkoğlu M, Şengür A. Dental material detection based on faster regional convolutional neural networks and shape features. Neural Process Lett. 2022. https://doi.org/10.1007/s11063-021-10721-5.CrossRef

16.

Chen H, Zhang K, Lyu P, Li H, Zhang L, Wu J, Lee CH. A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films. Sci Rep. 2019;9(1):1–11. https://doi.org/10.1038/s41598-019-40414-y.CrossRef

17.

Banar N, Bertels J, Laurent F, Boedi RM, De Tobel J, Thevissen P, Vandermeulen D. Towards fully automated third molar development staging in panoramic radiographs. Int J Legal Med. 2020;134(5):1831–41. https://doi.org/10.1007/s00414-020-02283-3.CrossRefPubMed

18.

Ekert T, Krois J, Meinhold L, Elhennawy K, Emara R, Golla T, Schwendicke F. Deep learning for the radiographic detection of apical lesions. J Endodont. 2019;45(7):917–22. https://doi.org/10.1016/j.joen.2019.03.016.CrossRef

19.

Kuwada C, Ariji Y, Fukuda M, Kise Y, Fujita H, Katsumata A, Ariji E. Deep learning systems for detecting and classifying the presence of impacted supernumerary teeth in the maxillary incisor region on panoramic radiographs. Oral Surg Oral Med Oral Pathol Oral Radiol. 2020;130(4):464–9. https://doi.org/10.1016/j.oooo.2020.04.813.CrossRefPubMed

20.

Zhang W, Li J, Li ZB, Li Z. Predicting postoperative facial swelling following impacted mandibular third molars extraction by using artificial neural networks evaluation. Sci Rep. 2018;8(1):1–9. https://doi.org/10.1038/s41598-018-29934-1.CrossRef

21.

Başaran M, Çelik Ö, Bayrakdar IS, Bilgir E, Orhan K, Odabaş A, et al. Diagnostic charting of panoramic radiography using deep-learning artificial intelligence system. Oral Radiol. 2022;38(3):363–9. https://doi.org/10.1007/s11282-021-00572-0.CrossRefPubMed

22.

Celik ME. Deep learning based detection tool for impacted mandibular third molar teeth. Diagnostics. 2022;12(4):942. https://doi.org/10.3390/diagnostics12040942.CrossRefPubMedPubMedCentral

23.

Mubashar M, Ali H, Grönlund C, Azmat S. R2U++: a multiscale recurrent residual U-Net with dense skip connections for medical image segmentation. Neural Comput Appl. 2022. https://doi.org/10.1007/s00521-022-07419-7.CrossRefPubMedPubMedCentral

24.

Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. Proc Int Conf Med Image Comput Computer-Assisted Intervent. 2015. https://doi.org/10.48550/arXiv.1505.04597.CrossRef

25.

Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. Proc Int Conf Mach Learn. 2015;37:448–56. https://doi.org/10.48550/arXiv.1502.03167.CrossRef

26.

Badshah N, Ahmad A. ResBCU-Net: deep learning approach for segmentation of skin images. Biomed Signal Process Control. 2022;71:103137. https://doi.org/10.1016/j.bspc.2021.103137.CrossRef

27.

Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC. Mobilenetv 2: inverted residuals and linear bottlenecks. Proc IEEE Conf Comput Vision Pattern Recognit. 2018. https://doi.org/10.48550/arXiv.1801.04381.CrossRef

28.

Le DN, Parvathy VS, Gupta D, Khanna A, Rodrigues JJ, Shankar K. IoT enabled depthwise separable convolution neural network with deep support vector machine for COVID-19 diagnosis and classification. Int J Mach Learn Cybernet. 2021. https://doi.org/10.1007/s13042-020-01248-7.CrossRef

29.

Li Y, Zhang D, Lee DJ. IIRNet: a lightweight deep neural network using intensely inverted residuals for image recognition. Image Vision Comput. 2019;92:103819. https://doi.org/10.1016/j.imavis.2019.10.005.CrossRef

30.

Boulila W, Ghandorh H, Khan MA, Ahmed F, Ahmad J. A novel CNN-LSTM-based approach to predict urban expansion. Ecol Inform. 2021. https://doi.org/10.1016/j.ecoinf.2021.101325.CrossRef

31.

Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735–80. https://doi.org/10.1162/neco.1997.9.8.1735.CrossRefPubMed

32.

Huang G, Zhang Y, Ou J. Transfer remaining useful life estimation of bearing using depth-wise separable convolution recurrent network. Measurement. 2021;176:109090. https://doi.org/10.1016/j.measurement.2021.109090.CrossRef

33.

Albumentations. https://albumentations.ai/. 2022

34.

Azad R, Asadi-Aghbolaghi M, Fathy M, Escalera S. Bi-directional ConvLSTM U-Net with densley connected convolutions. Proc IEEE/CVF Int Conf Comput Vision Workshops. 2019. https://doi.org/10.48550/arXiv.1909.00166.CrossRef

35.

Jha D, Smedsrud PH, Riegler MA, Johansen D, De Lange T, Halvorsen P, Johansen HD. Resunet++: an advanced architecture for medical image segmentation. 2019 IEEE Int Symp Multimed (ISM). 2019. https://doi.org/10.1109/ISM46123.2019.00049.CrossRef

Title: ResMIBCU-Net: an encoder–decoder network with residual blocks, modified inverted residual block, and bi-directional ConvLSTM for impacted tooth segmentation in panoramic X-ray images
Authors: Andaç Imak
Adalet Çelebi
Onur Polat
Muammer Türkoğlu
Abdulkadir Şengür
Publication date: 15-03-2023
Publisher: Springer Nature Singapore
Keyword: Artificial Intelligence
Published in: Oral Radiology / Issue 4/2023
Print ISSN: 0911-6028
Electronic ISSN: 1613-9674
DOI: https://doi.org/10.1007/s11282-023-00677-8

Keynote webinar | Spotlight on medication adherence

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ResMIBCU-Net: an encoder–decoder network with residual blocks, modified inverted residual block, and bi-directional ConvLSTM for impacted tooth segmentation in panoramic X-ray images

Abstract

Objective

Methods

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objective

Methods

Results

Conclusion

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