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

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.

Article  PubMed  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  Google Scholar 

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.

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.

Article  PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  PubMed  PubMed Central  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  Google Scholar 

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.

Article  PubMed  Google Scholar 

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

Article  PubMed  PubMed Central  Google Scholar 

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.

Article  PubMed  PubMed Central  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  Google Scholar 

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

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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