Abesi F, Hozuri M, Zamani M. Performance of artificial intelligence using cone-beam computed tomography for segmentation of oral and maxillofacial structures: A systematic review and meta-analysis. J Clin Exp Dent. 2023;15(11):e954-62.

 

doi:10.4317/jced.60287

https://doi.org/10.4317/jced.60287

_______

 

References

1. Kaasalainen T, Ekholm M, Siiskonen T, Kortesniemi M. Dental cone beam CT: An updated review. Phys Med. 2021;88:193-217.
https://doi.org/10.1016/j.ejmp.2021.07.007
PMid:34284332

 

2. Abesi F, Alimohamadi M. Cone beam computed tomography (CBCT) findings of fungal sinusitis in post COVID-19 patient: A case report. Caspian J Intern Med. 2022;13:307-10.

PMid:35872677 PMCid:PMC9272968

 

3. Maspero C, Abate A, Bellincioni F, Cavagnetto D, Lanteri V, Costa A, et al. Comparison of a tridimensional cephalometric analysis performed on 3T-MRI compared with CBCT: a pilot study in adults. Prog Orthod. 2019;20:40.
https://doi.org/10.1186/s40510-019-0293-x
PMid:31631241 PMCid:PMC6801285

 

4. Abesi F, Motaharinia S, Moudi E, Haghanifar S, Khafri S. Prevalence and anatomical variations of maxillary sinus septa: A cone-beam computed tomography analysis. J Clin Exp Dent. 2022;14:e689-e93.
https://doi.org/10.4317/jced.59599
PMid:36158776 PMCid:PMC9498644

 

5. Patel S, Brown J, Pimentel T, Kelly RD, Abella F, Durack C. Cone beam computed tomography in Endodontics - a review of the literature. Int Endod J. 2019;52:1138-52.
https://doi.org/10.1111/iej.13115
PMid:30868610

 

6. Kapila SD, Nervina JM. CBCT in orthodontics: assessment of treatment outcomes and indications for its use. Dentomaxillofac Radiol. 2015;44:20140282.
https://doi.org/10.1259/dmfr.20140282
PMid:25358833 PMCid:PMC4277443

 

7. Barnett CW, Glickman GN, Umorin M, Jalali P. Interobserver and intraobserver reliability of cone-beam computed tomography in identification of apical periodontitis. J Endod. 2018;44:938-40.
https://doi.org/10.1016/j.joen.2017.12.022
PMid:29550001

 

8. Tsolakis IA, Kolokitha OE, Papadopoulou E, Tsolakis AI, Kilipiris EG, Palomo JM. Artificial intelligence as an aid in CBCT airway analysis: A systematic review. Life (Basel). 2022;12:1894.
https://doi.org/10.3390/life12111894
PMid:36431029 PMCid:PMC9696726

 

9. Lerner H, Mouhyi J, Admakin O, Mangano F. Artificial intelligence in fixed implant prosthodontics: a retrospective study of 106 implant-supported monolithic zirconia crowns inserted in the posterior jaws of 90 patients. BMC Oral Health. 2020;20:80.
https://doi.org/10.1186/s12903-020-1062-4
PMid:32188431 PMCid:PMC7081700

 

10. Schwendicke F, Samek W, Krois J. Artificial intelligence in dentistry: chances and challenges. J Dent Res. 2020;99:769-74.
https://doi.org/10.1177/0022034520915714
PMid:32315260 PMCid:PMC7309354

 

11. Shan T, Tay FR, Gu L. Application of artificial intelligence in dentistry. J Dent Res. 2021;100:232-44.
https://doi.org/10.1177/0022034520969115
PMid:33118431

 

12. Mureșanu S, Almășan O, Hedeșiu M, Dioșan 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;39:18-40.
https://doi.org/10.1007/s11282-022-00660-9
PMid:36269515

 

13. 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. Dentomaxillofac Radiol. 2020;49:20190107.
https://doi.org/10.1259/dmfr.20190107
PMid:31386555 PMCid:PMC6957072

 

14. Shujaat S, Jazil O, Willems H, Van Gerven A, Shaheen E, Politis C, et al. Automatic segmentation of the pharyngeal airway space with convolutional neural network. J Dent. 2021;111:103705.
https://doi.org/10.1016/j.jdent.2021.103705
PMid:34077802

 

15. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6:e1000097.
https://doi.org/10.1371/journal.pmed.1000097
PMid:19621072 PMCid:PMC2707599

 

16. Moons KGM, Wolff RF, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST: A tool to assess risk of bias and applicability of prediction model studies: Explanation and elaboration. Ann Intern Med. 2019;170:W1-w33.
https://doi.org/10.7326/M18-1377
PMid:30596876

 

17. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327:557-60.
https://doi.org/10.1136/bmj.327.7414.557
PMid:12958120 PMCid:PMC192859

 

18. Fontenele RC, Gerhardt MDN, Pinto JC, Van Gerven A, Willems H, Jacobs R, et al. Influence of dental fillings and tooth type on the performance of a novel artificial intelligence-driven tool for automatic tooth segmentation on CBCT images - A validation study. J Dent. 2022;119:104069.
https://doi.org/10.1016/j.jdent.2022.104069
PMid:35183696

 

19. Gillot M, Baquero B, Le C, Deleat-Besson R, Bianchi J, Ruellas A, et al. Automatic multi-anatomical skull structure segmentation of cone-beam computed tomography scans using 3D UNETR. PLoS One. 2022;17:e0275033.
https://doi.org/10.1371/journal.pone.0275033
PMid:36223330 PMCid:PMC9555672

 

20. Hung KF, Ai QYH, King AD, Bornstein MM, Wong LM, Leung YY. Automatic detection and segmentation of morphological changes of the maxillary sinus mucosa on cone-beam computed tomography images using a three-dimensional convolutional neural network. Clin Oral Investig. 2022;26:3987-98.
https://doi.org/10.1007/s00784-021-04365-x
PMid:35032193

 

21. 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.
https://doi.org/10.1016/j.jdent.2021.103891
PMid:34780873

 

22. Leonardi R, Lo Giudice A, Farronato M, Ronsivalle V, Allegrini S, Musumeci G, et al. Fully automatic segmentation of sinonasal cavity and pharyngeal airway based on convolutional neural networks. Am J Orthod Dentofacial Orthop. 2021;159:824-35.e1.
https://doi.org/10.1016/j.ajodo.2020.05.017
PMid:34059213

 

23. Li Q, Chen K, Han L, Zhuang Y, Li J, Lin J. Automatic tooth roots segmentation of cone beam computed tomography image sequences using U-net and RNN. J Xray Sci Technol. 2020;28:905-22.
https://doi.org/10.3233/XST-200678
PMid:32986647

 

24. Lim HK, Jung SK, Kim SH, Cho Y, Song IS. Deep semi-supervised learning for automatic segmentation of inferior alveolar nerve using a convolutional neural network. BMC Oral Health. 2021;21:630.
https://doi.org/10.1186/s12903-021-01983-5
PMid:34876105 PMCid:PMC8650351

 

25. Lin X, Fu Y, Ren G, Yang X, Duan W, Chen Y, et al. Micro-computed tomography-guided artificial intelligence for pulp cavity and tooth segmentation on cone-beam computed tomography. J Endod. 2021;47:1933-41.
https://doi.org/10.1016/j.joen.2021.09.001
PMid:34520812

 

26. Lo Giudice A, Ronsivalle V, Spampinato C, Leonardi R. Fully automatic segmentation of the mandible based on convolutional neural networks (CNNs). Orthod Craniofac Res. 2021;24:100-7.
https://doi.org/10.1111/ocr.12536
PMid:34553817

 

27. Minnema J, van Eijnatten M, Hendriksen AA, Liberton N, Pelt DM, Batenburg KJ, et al. Segmentation of dental cone-beam CT scans affected by metal artifacts using a mixed-scale dense convolutional neural network. Med Phys. 2019;46:5027-35.
https://doi.org/10.1002/mp.13793
PMid:31463937 PMCid:PMC6900023

 

28. Nogueira-Reis F, Morgan N, Nomidis S, Van Gerven A, Oliveira-Santos N, Jacobs R, et al. Three-dimensional maxillary virtual patient creation by convolutional neural network-based segmentation on cone-beam computed tomography images. Clin Oral Investig. 2022;27:1133-41.
https://doi.org/10.1007/s00784-022-04708-2
PMid:36114907 PMCid:PMC9985582

 

29. Pei Y, Ai X, Zha H, Xu T, Ma G. 3D exemplar-based random walks for tooth segmentation from cone-beam computed tomography images. Med Phys. 2016;43:5040.
https://doi.org/10.1118/1.4960364
PMid:27587034

 

30. Qiu B, van der Wel H, Kraeima J, Glas HH, Guo J, Borra RJH, et al. Mandible segmentation of dental CBCT scans affected by metal artifacts using coarse-to-fine learning model. J Pers Med. 2021;11:560.
https://doi.org/10.3390/jpm11060560
PMid:34208429 PMCid:PMC8232763

 

31. Shaheen E, Leite A, Alqahtani KA, Smolders A, Van Gerven A, Willems H, et al. A novel deep learning system for multi-class tooth segmentation and classification on cone beam computed tomography. A validation study. J Dent. 2021;115:103865.
https://doi.org/10.1016/j.jdent.2021.103865
PMid:34710545

 

32. Sin Ç, Akkaya N, Aksoy S, Orhan K, Öz U. A deep learning algorithm proposal to automatic pharyngeal airway detection and segmentation on CBCT images. Orthod Craniofac Res. 2021;24 Suppl 2:117-23.
https://doi.org/10.1111/ocr.12480
PMid:33619828

 

33. Torosdagli N, Liberton DK, Verma P, Sincan M, Lee JS, Bagci U. Deep Geodesic Learning for Segmentation and Anatomical Landmarking. IEEE Trans Med Imaging. 2019;38:919-31.
https://doi.org/10.1109/TMI.2018.2875814
PMid:30334750 PMCid:PMC6475529

 

34. Verhelst PJ, 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.
https://doi.org/10.1016/j.jdent.2021.103786
PMid:34425172

 

35. Wang H, Minnema J, Batenburg KJ, Forouzanfar T, Hu FJ, Wu G. Multiclass CBCT image segmentation for orthodontics with deep learning. J Dent Res. 2021;100:943-9.
https://doi.org/10.1177/00220345211005338
PMid:33783247 PMCid:PMC8293763

 

36. Wang L, Chen KC, Shi F, Liao S, Li G, Gao Y, et al. Automated segmentation of CBCT image using spiral CT atlases and convex optimization. Med Image Comput Comput Assist Interv. 2013;16:251-8.
https://doi.org/10.1007/978-3-642-40760-4_32
PMid:24505768 PMCid:PMC3918683

 

37. Wang L, Gao Y, Shi F, Li G, Chen KC, Tang Z, et al. Automated segmentation of dental CBCT image with prior-guided sequential random forests. Med Phys. 2016;43:336.
https://doi.org/10.1118/1.4938267
PMid:26745927 PMCid:PMC4698124

 

38. Wang X, Pastewait M, Wu TH, Lian C, Tejera B, Lee YT, et al. 3D morphometric quantification of maxillae and defects for patients with unilateral cleft palate via deep learning-based CBCT image auto-segmentation. Orthod Craniofac Res. 2021;24:108-16.
https://doi.org/10.1111/ocr.12482
PMid:33711187 PMCid:PMC8435046

 

39. Zhang J, Liu M, Wang L, Chen S, Yuan P, Li J, et al. Context-guided fully convolutional networks for joint craniomaxillofacial bone segmentation and landmark digitization. Med Image Anal. 2020;60:101621.
https://doi.org/10.1016/j.media.2019.101621
PMid:31816592 PMCid:PMC7360136

 

40. Zheng Q, Ge Z, Du H, Li G. Age estimation based on 3D pulp chamber segmentation of first molars from cone-beam-computed tomography by integrated deep learning and level set. Int J Legal Med. 2021;135:365-73.
https://doi.org/10.1007/s00414-020-02459-x
PMid:33185706

 

41. Lee S, Woo S, Yu J, Seo J, Lee J, Lee C. Automated CNN-Based tooth segmentation in cone-beam ct for dental implant planning. IEEE Access. 2020;8:50507-18.
https://doi.org/10.1109/ACCESS.2020.2975826