Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs.
Artificial intelligence
Classification
Machine learning
Panoramic radiography
Tooth
Journal
Clinical oral investigations
ISSN: 1436-3771
Titre abrégé: Clin Oral Investig
Pays: Germany
ID NLM: 9707115
Informations de publication
Date de publication:
Apr 2021
Apr 2021
Historique:
received:
22
06
2020
accepted:
20
08
2020
pubmed:
28
8
2020
medline:
19
3
2021
entrez:
27
8
2020
Statut:
ppublish
Résumé
To evaluate the performance of a new artificial intelligence (AI)-driven tool for tooth detection and segmentation on panoramic radiographs. In total, 153 radiographs were collected. A dentomaxillofacial radiologist labeled and segmented each tooth, serving as the ground truth. Class-agnostic crops with one tooth resulted in 3576 training teeth. The AI-driven tool combined two deep convolutional neural networks with expert refinement. Accuracy of the system to detect and segment teeth was the primary outcome, time analysis secondary. The Kruskal-Wallis test was used to evaluate differences of performance metrics among teeth groups and different devices and chi-square test to verify associations among the amount of corrections, presence of false positive and false negative, and crown and root parts of teeth with potential AI misinterpretations. The system achieved a sensitivity of 98.9% and a precision of 99.6% for tooth detection. For segmenting teeth, lower canines presented best results with the following values for intersection over union, precision, recall, F1-score, and Hausdorff distances: 95.3%, 96.9%, 98.3%, 97.5%, and 7.9, respectively. Although still above 90%, segmentation results for both upper and lower molars were somewhat lower. The method showed a clinically significant reduction of 67% of the time consumed for the manual. The AI tool yielded a highly accurate and fast performance for detecting and segmenting teeth, faster than the ground truth alone. An innovative clinical AI-driven tool showed a faster and more accurate performance to detect and segment teeth on panoramic radiographs compared with manual segmentation.
Identifiants
pubmed: 32844259
doi: 10.1007/s00784-020-03544-6
pii: 10.1007/s00784-020-03544-6
doi:
Types de publication
Journal Article
Langues
eng
Pagination
2257-2267Subventions
Organisme : Fundação de Apoio à Pesquisa do Distrito Federal
ID : 23106.013588/2019-05
Références
Sklavos A, Beteramia D, Delpachitra SN, Kumar R (2019) The panoramic dental radiograph for emergency physicians. Emerg Med J 36(9):565–571. https://doi.org/10.1136/emermed-2018-208332
doi: 10.1136/emermed-2018-208332
Yeung AWK, Mozos I (2020) The innovative and sustainable use of dental panoramic radiographs for the detection of osteoporosis. Int J Environ Res Public Health 17(7). https://doi.org/10.3390/ijerph17072449
Jacobs R, Quirynen M (2014) Dental cone beam computed tomography: justification for use in planning oral implant placement. Periodontol 2000 66(1):203–213. https://doi.org/10.1111/prd.12051
doi: 10.1111/prd.12051
pubmed: 25123769
Lin PL, Huang PY, Huang PW, Hsu HC, Chen CC (2014) Teeth segmentation of dental periapical radiographs based on local singularity analysis. Comput Methods Prog Biomed 113(2):433–445. https://doi.org/10.1016/j.cmpb.2013.10.015
doi: 10.1016/j.cmpb.2013.10.015
Vinayahalingam S, Xi T, Bergé S, Maal T, de Jong G (2019) Automated detection of third molars and mandibular nerve by deep learning. Sci Rep 9(1):9007. https://doi.org/10.1038/s41598-019-45487-3
doi: 10.1038/s41598-019-45487-3
pubmed: 31227772
pmcid: 6588560
Vranckx M, Ockerman A, Coucke W, Claerhout E, Grommen B, Miclotte A, van Vlierberghe M, Politis C, Jacobs R (2019) Radiographic prediction of mandibular third molar eruption and mandibular canal involvement based on angulation. Orthod Craniofacial Res 22(2):118–123. https://doi.org/10.1111/ocr.12297
doi: 10.1111/ocr.12297
Hiraiwa T, Ariji Y, Fukuda M, Kise Y, Nakata K, Katsumata A, Fujita H, Ariji E (2019) A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography. Dentomaxillofacial Radiol 48(3):20180218. https://doi.org/10.1259/dmfr.20180218
doi: 10.1259/dmfr.20180218
Ariji Y, Yanashita Y, Kutsuna S, Muramatsu C, Fukuda M, Kise Y, Nozawa M, Kuwada C, Fujita H, Katsumata A, Ariji E (2019) Automatic detection and classification of radiolucent lesions in the mandible on panoramic radiographs using a deep learning object detection technique. Oral Surg Oral Med Oral Pathol Oral Radiol 128(4):424–430. https://doi.org/10.1016/j.oooo.2019.05.014
doi: 10.1016/j.oooo.2019.05.014
pubmed: 31320299
Fukuda M, Inamoto K, Shibata N, Ariji Y, Yanashita Y, Kutsuna S, Nakata K, Katsumata A, Fujita H, Ariji E (2019) Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography. Oral Radiol 2019:1617–1620. https://doi.org/10.1007/s11282-019-00409-x
doi: 10.1007/s11282-019-00409-x
Kise Y, Ikeda H, Fujii T, Fukuda M, Ariji Y, Fujita H, Katsumata A, Ariji E (2019) Preliminary study on the application of deep learning system to diagnosis of Sjögren’s syndrome on CT images. Dentomaxillofacial Radiol 48(6):20190019. https://doi.org/10.1259/dmfr.20190019
doi: 10.1259/dmfr.20190019
Murata M, Ariji Y, Ohashi Y, Kawai T, Fukuda M, Funakoshi T, Kise Y, Nozawa M, Katsumata A, Fujita H, Ariji E (2019) Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography. Oral Radiol 35(3):301–307. https://doi.org/10.1007/s11282-018-0363-7
doi: 10.1007/s11282-018-0363-7
pubmed: 30539342
Kats L, Vered M, Zlotogorski-Hurvitz A, Harpaz I (2019) Atherosclerotic carotid plaque on panoramic radiographs: neural network detection. Int J Comput Dent 22(2):163–169
pubmed: 31134222
Moutselos K, Berdouses E, Oulis C, Maglogiannis I (2019) Recognizing occlusal caries in dental intraoral images using deep learning. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2019, pp 1617–1620. https://doi.org/10.1109/EMBC.2019.8856553
doi: 10.1109/EMBC.2019.8856553
Lee JH, Kim DH, Jeong SN, Choi SH (2018) Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm. J Periodontal Implant Sci 48(2):114. https://doi.org/10.5051/jpis.2018.48.2.114
doi: 10.5051/jpis.2018.48.2.114
pubmed: 29770240
pmcid: 5944222
Leite AF, de Faria Vasconcelos K, Willems H, Jacobs R (2020) Radiomics and machine learning in oral healthcare. Proteomics Clin Appl 14(3):e1900040. https://doi.org/10.1002/prca.201900040
doi: 10.1002/prca.201900040
pubmed: 31950592
Barboza EB, Marana AN, Oliveira DT (2012) Semiautomatic dental recognition using a graph-based segmentation algorithm and teeth shapes features. In: Proceedings - 2012 5th IAPR International Conference on Biometrics, ICB 2012. https://doi.org/10.1109/ICB.2012.6199831 .
Baksi BG, Alpöz E, Soǧur E, Mert A (2010) Perception of anatomical structures in digitally filtered and conventional panoramic radiographs: A clinical evaluation. Dentomaxillofacial Radiol 39(7):424–430. https://doi.org/10.1259/dmfr/30570374
doi: 10.1259/dmfr/30570374
Hasan MM, Ismail W, Hassan R, Yoshitaka A (2016) Automatic segmentation of jaw from panoramic dental X-ray images using GVF snakes. In: World Automation Congress Proceedings, vol 1, pp 1–6. https://doi.org/10.1109/WAC.2016.7583022
doi: 10.1109/WAC.2016.7583022
Banar N, Bertels J, Laurent F, Boedi RM, de Tobel J, Thevissen P, Vandermeulen D (2020) Towards fully automated third molar development staging in panoramic radiographs. Int J Legal Med 134(5):1831–1841. https://doi.org/10.1007/s00414-020-02283-3
doi: 10.1007/s00414-020-02283-3
pubmed: 32239317
Galibourg A, Dumoncel J, Telmon N, Calvet A, Michetti J, Maret D (2018) Assessment of automatic segmentation of teeth using a watershed-based method. Dentomaxillofacial Radiol 47(1):20170220. https://doi.org/10.1259/dmfr.20170220
doi: 10.1259/dmfr.20170220
Lee JH, Han SS, Kim YH, Lee C, Kim I (2020) Application of a fully deep convolutional neural network to the automation of tooth segmentation on panoramic radiographs. Oral Surg Oral Med Oral Pathol Oral Radiol 129(6):635–642. https://doi.org/10.1016/j.oooo.2019.11.007
doi: 10.1016/j.oooo.2019.11.007
pubmed: 31992524
Tuzoff DV, Tuzova LN, Bornstein MM, Krasnov AS, Kharchenko MA, Nikolenko SI, Sveshnikov MM, Bednenko GB (2019) Tooth detection and numbering in panoramic radiographs using convolutional neural networks. Dentomaxillofacial Radiol 48(4):20180051. https://doi.org/10.1259/dmfr.20180051
doi: 10.1259/dmfr.20180051
Silva G, Oliveira L, Pithon M (2018) Automatic segmenting teeth in X-ray images: trends, a novel data set, benchmarking and future perspectives. Expert Syst Appl 107:15–31. https://doi.org/10.1016/j.eswa.2018.04.001
doi: 10.1016/j.eswa.2018.04.001
Wirtz A, Mirashi SG, Wesarg S (2018) Automatic teeth segmentation in panoramic X-ray images using a coupled shape model in combination with a neural network. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 16-20, pp 712–179. https://doi.org/10.1007/978-3-030-00937-3_81
doi: 10.1007/978-3-030-00937-3_81
Jader G, Fontineli J, Ruiz M et al (2019) Deep instance segmentation of teeth in panoramic X-ray images. In: Proceedings - 31st Conference on Graphics, Patterns and Images, SIBGRAPI 2018. https://doi.org/10.1109/SIBGRAPI.2018.00058
doi: 10.1109/SIBGRAPI.2018.00058
Chen L-C, Zhu Y, Papandreou G, et al (2018) Rethinking atrous convolution for semantic image segmentation arXiv Prepr arXiv170605587. https://doi.org/10.1159/000018039
Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2015. https://doi.org/10.1109/CVPR.2015.7298965
doi: 10.1109/CVPR.2015.7298965
Wada K labelme: Image polygonal annotation with Python. https://github.com/wkentaro/labelme . Accessed 1 Nov 2019
Shaheen E, Khalil W, Ezeldeen M, van de Casteele E, Sun Y, Politis C, Jacobs R (2017) Accuracy of segmentation of tooth structures using 3 different CBCT machines. Oral Surg Oral Med Oral Pathol Oral Radiol 23(1):123–128. https://doi.org/10.1016/j.oooo.2016.09.005
doi: 10.1016/j.oooo.2016.09.005
Schwendicke F, Golla T, Dreher M, Krois J (2019) Convolutional neural networks for dental image diagnostics: a scoping review. J Dent 91:103226. https://doi.org/10.1016/j.jdent.2019.103226
doi: 10.1016/j.jdent.2019.103226
pubmed: 31704386
Yu HJ, Cho SR, Kim MJ, Kim WH, Kim JW, Choi J (2020) Automated skeletal classification with lateral cephalometry based on artificial intelligence. J Dent Res 99(3):249–256. https://doi.org/10.1177/0022034520901715
doi: 10.1177/0022034520901715
pubmed: 31977286
Ilhan B, Lin K, Guneri P, Wilder-Smith P (2020) Improving oral cancer outcomes with imaging and artificial intelligence. J Dent Res 99(3):241–248. https://doi.org/10.1177/0022034520902128
doi: 10.1177/0022034520902128
pubmed: 32077795
pmcid: 7036512