Segmentation of the nasopalatine canal and detection of canal furcation status with artificial intelligence on cone-beam computed tomography images

Jacobs R, Lambrichts I, Liang X, Martens W, Mraiwa N, Adriaensens P, Gelan J. Neurovascularization of the anterior jaw bones revisited using high-resolution magnetic resonance imaging. Oral Surg Oral Med Oral Pathol Oral Radiol Endodontol. 2007;103(5):683–93.

Article  Google Scholar 

Radlanski RJ, Emmerich S, Renz H. Prenatal morphogenesis of the human incisive canal. Anat Embryol (Berl). 2004;208(4):265–71.

Article  CAS  PubMed  Google Scholar 

Song WC, Jo DI, Lee JY, Kim JN, Hur MS, Hu KS, et al. Microanatomy of the incisive canal using three-dimensional reconstruction of microCT images: an ex vivo study. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2009;108(4):583–90.

Article  PubMed  Google Scholar 

Fernández-Alonso A, Suárez-Quintanilla JA, Rapado-González O, Suárez-Cunqueiro MM. Morphometric differences of nasopalatine canal based on 3D classifications: descriptive analysis on CBCT. Surg Radiol Anat. 2015;37(7):825–33.

Article  PubMed  Google Scholar 

Lake S, Iwanaga J, Kikuta S, Oskouian RJ, Loukas M, Tubbs RS. The incisive canal: a comprehensive review. Cureus. 2018;10(7): e3069.

PubMed  PubMed Central  Google Scholar 

von Arx T, Lozanoff S. Clinical oral anatomy. Springer; 2017. p. 103–31.

Book  Google Scholar 

Panjnoush M, Norouzi H, Kheirandish Y, Shamshiri AR, Mofidi N. Evaluation of morphology and anatomical measurement of nasopalatine canal using cone beam computed tomography. J Dent (Tehran). 2016;13(4):287–94.

PubMed  Google Scholar 

Friedrich RE, Laumann F, Zrnc T, Assaf AT. The nasopalatine canal in adults on cone beam computed tomograms-a clinical study and review of the literature. In Vivo. 2015;29(4):467–86.

PubMed  Google Scholar 

Liang X, Jacobs R, Martens W, Hu Y, Adriaensens P, Quirynen M, Lambrichts I. Macro- and micro-anatomical, histological and computed tomography scan characterization of the nasopalatine canal. J Clin Periodontol. 2009;36(7):598–603.

Article  PubMed  Google Scholar 

Mallya SM, Lam EWN. White and Pharoah’s oral radiology: principles and interpretation. 8th ed. St Louis: Elsevier; 2019. p. 190–4.

Google Scholar 

Scarfe WC, Farman AG. What is cone-beam CT and how does it work? Dent Clin North Am. 2008;52(4):707–30.

Article  PubMed  Google Scholar 

Tyndall DA, Brooks SL. Selection criteria for dental implant site imaging: a position paper of the American Academy of Oral and Maxillofacial radiology. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2000;89(5):630–7.

Article  CAS  PubMed  Google Scholar 

Lopes IA, Chicrala GM, Soares MQS, Capelozza ALA. Evaluation of the nasopalatine canal of patients with and without cleft lip and palate in CBCT exams. The Cleft Palate Craniofacial Journal. 2024;61(4):610–9.

Article  PubMed  Google Scholar 

Gibas-Stanek M, Kościółek-Rudy D, Szumilas K, Wojas-Hille K, Pihut M. Morphological evaluation of the nasopalatine canal using cone beam computed tomography and its clinical implications for orthodontic miniscrew insertion. Dental and Medical Problems. 2024;61(3):363–71.

Article  PubMed  Google Scholar 

Mureşanu S, Almasan O, Hedesiu M, Diosan L, Dinu C, Jacobs R. Artificial intelligence models for clinical usage in dentistry with a focus on dentomaxillofacial CBCT: a systematic review. Oral Radiol. 2023;39(1):18–40.

Article  PubMed  Google Scholar 

Khanagar SB, Al-Ehaideb A, Maganur PC, Vishwanathaiah S, Patil S, Baeshen HA, et al. Developments, application, and performance of artificial intelligence in dentistry - a systematic review. J Dent Sci. 2021;16(1):508–22.

Article  PubMed  Google Scholar 

European Society of Radiology (ESR). What the radiologist should know about artificial intelligence - an ESR white paper. Insights Imaging. 2019;10:44.

Article  Google Scholar 

Heo M-S, Kim J-E, Hwang J-J, Han S-S, Kim J-S, Yi W-J, Park I-W. Artificial intelligence in oral and maxillofacial radiology: what is currently possible? Dentomaxillofacial Radiology. 2021;50(3):20200375.

Article  PubMed  Google Scholar 

Redmon J, Divvala S, Girshick R, Farhadi A, editors. You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2016 27–30 June 2016.

Jiang P, Ergu D, Liu F, Cai Y, Ma B. A review of Yolo algorithm developments. Procedia Computer Science. 2022;199:1066–73.

Article  Google Scholar 

Jocher G, Stoken A, Borovec J, Christopher S, Laughing LC. ultralytics/yolov5: v4. 0-nn. SiLU () activations, Weights & Biases logging, PyTorch Hub integration. Zenodo. 2021.

Jocher G, Chaurasia A, Stoken A, Borovec J, NanoCode012., Kwon Y, et al. ultralytics/yolov5: v7.0 - YOLOv5 SOTA Realtime Instance Segmentation. Zenodo. 2022;v7.0.

Beser B, Reis T, Berber MN, Topaloglu E, Gungor E, Kılıc MC, et al. YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition. BMC Med Imaging. 2024;24(1):172.

Article  PubMed  PubMed Central  Google Scholar 

Duman ŞB, Çelik Özen D, Bayrakdar IŞ, Baydar O, Alhaija ESA, Helvacioğlu Yiğit D, et al. Second mesiobuccal canal segmentation with YOLOv5 architecture using cone beam computed tomography images. Odontology. 2024;112(2):552–61.

Article  PubMed  Google Scholar 

Kaheel H, Hussein A, Chehab A. AI-based image processing for COVID-19 detection in chest CT scan images. Front Commun Netw. 2021;2(2021).

Torres MGG, de Faro VL, Vidal MTA, Crusoé-Rebello IM. Trifid nasopalatine canal: case report of a rare anatomical variation and its surgical implications. Rev Cubana Estomatol. 2016;53(2):67–70.

Google Scholar 

Lopez AR, Giro-i-Nieto X, Burdick J, Marques O, editors. Skin lesion classification from dermoscopic images using deep learning techniques. In: 2017 13th IASTED international conference on biomedical engineering (BioMed); 2017: IEEE.

Deng X, Liu Q, Deng Y, Mahadevan S. An improved method to construct basic probability assignment based on the confusion matrix for classification problem. Inf Sci. 2016;340:250–61.

Article  Google Scholar 

Orhan K, Bayrakdar IS, Ezhov M, Kravtsov A, Ozyurek T. Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans. Int Endod J. 2020;53(5):680–9.

Article  CAS  PubMed  Google Scholar 

Japkowicz N. Performance evaluation for learning algorithms. Cambridge: Cambridge University Press; 2011.

Book  Google Scholar 

Bornstein MM, Balsiger R, Sendi P, von Arx T. Morphology of the nasopalatine canal and dental implant surgery: a radiographic analysis of 100 consecutive patients using limited cone-beam computed tomography. Clin Oral Implants Res. 2011;22(3):295–301.

Article  PubMed  Google Scholar 

Hung K, Montalvao C, Tanaka R, Kawai T, Bornstein MM. The use and performance of artificial intelligence applications in dental and maxillofacial radiology: a systematic review. Dentomaxillofacial Radiology. 2020;49(1):20190107.

Article  PubMed  Google Scholar 

Parker JM, Mol A, Rivera EM, Tawil PZ. Cone-beam computed tomography uses in clinical endodontics: observer variability in detecting periapical lesions. Journal of endodontics. 2017;43(2):184–7.

Article  PubMed  Google Scholar 

Katne T, Kanaparthi A, Gotoor SG, Muppirala S, Devaraju RR, Gantala R. Artificial intelligence: demystifying dentistry—the future and beyond. Int J Contemp Med Surg Radiol. 2019;4(4):D6–9.

Article  Google Scholar 

Martín Noguerol T, Paulano-Godino F, Martín-Valdivia MT, Menias CO, Luna A. Strengths, weaknesses, opportunities, and threats analysis of artificial intelligence and machine learning applications in radiology. J Am Coll Radiol. 2019;16(9 Pt B):1239–47.

Article  PubMed  Google Scholar 

Steyerberg EW. Validation of prediction models. In: Steyerberg EW, editor. Clinical prediction models: a practical approach to development, validation, and updating. New York: Springer; 2010. p. 299–310.

Google Scholar 

Minnema J, Wolff J, Koivisto J, Lucka F, Batenburg KJ, Forouzanfar T, van Eijnatten M. Comparison of convolutional neural network training strategies for cone-beam CT image segmentation. Comput Methods Programs Biomed. 2021;207: 106192.

Article  PubMed  Google Scholar 

Nogueira-Reis F, Morgan N, Suryani IR, Tabchoury CPM, Jacobs R. Full virtual patient generated by Artificial Intelligence-driven integrated segmentation of craniomaxillofacial structures from CBCT images. J Dent. 2024;141: 104829.

Article  CAS  PubMed  Google Scholar 

Verhelst P-J, Smolders A, Beznik T, Meewis J, Vandemeulebroucke A, Shaheen E, et al. Layered deep learning for automatic mandibular segmentation in cone-beam computed tomography. J Dent. 2021;114: 103786.

Article  CAS  PubMed  Google Scholar 

Kanti Dhar M, Yu Z. Automatic tracing of mandibular canal pathways using deep learning. arXiv e-prints. 2021. arXiv: 2111.15111.

Lahoud P, Diels S, Niclaes L, Van Aelst S, Willems H, Van Gerven A, et al. Development and validation of a novel artificial intelligence driven tool for accurate mandibular canal segmentation on CBCT. J Dent. 2022;116: 103891.

Article  PubMed  Google Scholar 

Kwak GH, Kwak E-J, Song JM, Park HR, Jung Y-H, Cho B-H, et al. Automatic mandibular canal detection using a deep convolutional neural network. Sci Rep. 2020;10(1):5711.

Article  CAS  PubMed 

Comments (0)

No login
gif