Evaluation of an artificial intelligence-based algorithm for automated localization of craniofacial landmarks.
Algorithm
Artificial intelligence
Cone-beam computed tomography
Craniofacial landmarks
Journal
Clinical oral investigations
ISSN: 1436-3771
Titre abrégé: Clin Oral Investig
Pays: Germany
ID NLM: 9707115
Informations de publication
Date de publication:
May 2023
May 2023
Historique:
received:
05
01
2023
accepted:
21
03
2023
medline:
8
5
2023
pubmed:
5
4
2023
entrez:
4
4
2023
Statut:
ppublish
Résumé
Due to advancing digitalisation, it is of interest to develop standardised and reproducible fully automated analysis methods of cranial structures in order to reduce the workload in diagnosis and treatment planning and to generate objectifiable data. The aim of this study was to train and evaluate an algorithm based on deep learning methods for fully automated detection of craniofacial landmarks in cone-beam computed tomography (CBCT) in terms of accuracy, speed, and reproducibility. A total of 931 CBCTs were used to train the algorithm. To test the algorithm, 35 landmarks were located manually by three experts and automatically by the algorithm in 114 CBCTs. The time and distance between the measured values and the ground truth previously determined by an orthodontist were analyzed. Intraindividual variations in manual localization of landmarks were determined using 50 CBCTs analyzed twice. The results showed no statistically significant difference between the two measurement methods. Overall, with a mean error of 2.73 mm, the AI was 2.12% better and 95% faster than the experts. In the area of bilateral cranial structures, the AI was able to achieve better results than the experts on average. The achieved accuracy of automatic landmark detection was in a clinically acceptable range, is comparable in precision to manual landmark determination, and requires less time. Further enlargement of the database and continued development and optimization of the algorithm may lead to ubiquitous fully automated localization and analysis of CBCT datasets in future routine clinical practice.
Identifiants
pubmed: 37014502
doi: 10.1007/s00784-023-04978-4
pii: 10.1007/s00784-023-04978-4
pmc: PMC10159965
doi:
Types de publication
Journal Article
Langues
eng
Pagination
2255-2265Informations de copyright
© 2023. The Author(s).
Références
Hung K, Montalvao C, Tanaka R, Kawai T, Bornstein MM (2020) The use and performance of artificial intelligence applications in dental and maxillofacial radiology: a systematic review. Dentomaxillofac Radiol 49(1):20190107. https://doi.org/10.1259/dmfr.20190107
doi: 10.1259/dmfr.20190107
pubmed: 31386555
Hogarty DT, Mackey DA, Hewitt AW (2019) Current state and future prospects of artificial intelligence in ophthalmology: a review. Clin Exp Ophthalmol 47(1):128–139. https://doi.org/10.1111/ceo.13381
doi: 10.1111/ceo.13381
pubmed: 30155978
Rawson TM, Ahmad R, Toumazou C, Georgiou P, Holmes AH (2019) Artificial intelligence can improve decision-making in infection management. Nat Hum Behav 3(6):543–545. https://doi.org/10.1038/s41562-019-0583-9
doi: 10.1038/s41562-019-0583-9
pubmed: 31190023
Kothari S, Gionfrida L, Bharath AA, Abraham S (2019) Artificial intelligence (AI) and rheumatology: a potential partnership. Rheumatology (Oxford) 58(11):1894–1895. https://doi.org/10.1093/rheumatology/kez194
doi: 10.1093/rheumatology/kez194
pubmed: 31168589
Zamora N, Llamas JM, Cibrián R, Gandia JL, Paredes V (2011) Cephalometric measurements from 3D reconstructed images compared with conventional 2D images. Angle Orthod 81(5):856–864. https://doi.org/10.2319/121210-717.1
doi: 10.2319/121210-717.1
pubmed: 21469969
pmcid: 8916191
Baumrind S, Miller DM (1980) Computer-aided head film analysis: the University of California San Francisco method. Am J Orthod 78(1):41–65. https://doi.org/10.1016/0002-9416(80)90039-1
doi: 10.1016/0002-9416(80)90039-1
pubmed: 6930171
Forsyth DB, Shaw WC, Richmond S, Roberts CT (1996) Digital imaging of cephalometric radiographs, Part 2: Image quality. Angle Orthod 66(1):43–50. https://doi.org/10.1043/0003-3219(1996)066<0043:DIOCRP>2.3.CO;2
doi: 10.1043/0003-3219(1996)066<0043:DIOCRP>2.3.CO;2
pubmed: 8678345
Gribel BF, Gribel MN, Manzi FR, Brooks SL, McNamara JA Jr (2011) From 2D to 3D: an algorithm to derive normal values for 3-dimensional computerized assessment. Angle Orthod 81(1):3–10. https://doi.org/10.2319/032910-173.1
doi: 10.2319/032910-173.1
pubmed: 20936948
pmcid: 8926363
Tsai P, Torabinejad M, Rice D, Azevedo B (2012) Accuracy of cone-beam computed tomography and periapical radiography in detecting small periapical lesions. J Endod 38(7):965–970. https://doi.org/10.1016/j.joen.2012.03.001
doi: 10.1016/j.joen.2012.03.001
pubmed: 22703662
de Oliveira AE, Cevidanes LH, Phillips C, Motta A, Burke B, Tyndall D (2009) Observer reliability of three-dimensional cephalometric landmark identification on cone-beam computerized tomography. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 107(2):256–265. https://doi.org/10.1016/j.tripleo.2008.05.039
doi: 10.1016/j.tripleo.2008.05.039
pubmed: 18718796
Savage AW, Showfety KJ, Yancey J (1987) Repeated measures analysis of geometrically constructed and directly determined cephalometric points. Am J Orthod Dentofac Orthop 91(4):295–299. https://doi.org/10.1016/0889-5406(87)90169-7
doi: 10.1016/0889-5406(87)90169-7
Torosdagli N, Liberton DK, Verma P, Sincan M, Lee JS, Bagci U (2019) Deep geodesic learning for segmentation and anatomical landmarking. IEEE Trans Med Imaging 38(4):919–931. https://doi.org/10.1109/TMI.2018.2875814
doi: 10.1109/TMI.2018.2875814
pubmed: 30334750
Makram M, Kamel H (2014) Reeb graph for automatic 3D cephalometry. IJIP 8(2):17–29
Zhang J, Gao Y, Wang L, Tang Z, Xia JJ, Shen D (2016) Automatic craniomaxillofacial landmark digitization via segmentation-guided partially-joint regression forest model and multiscale statistical features. IEEE Trans Biomed Eng 63:1820–1829. https://doi.org/10.1109/TBME.2015.2503421
doi: 10.1109/TBME.2015.2503421
pubmed: 26625402
Shahidi S, Bahrampour E, Soltanimehr E, Zamani A, Oshagh M, Moattari M, Mehdizadeh A (2014) The accuracy of a designed software for automated localization of craniofacial landmarks on CBCT images. BMC Med Imaging 14:32. https://doi.org/10.1186/1471-2342-14-32
doi: 10.1186/1471-2342-14-32
pubmed: 25223399
pmcid: 4171715
Gupta A, Kharbanda OP, Sardana V, Balachandran R, Sardana HK (2015) A knowledge-based algorithm for automatic detection of cephalometric landmarks on CBCT images. Int J Comput Assist Radiol Surg 10(11):1737–1752. https://doi.org/10.1007/s11548-015-1173-6
doi: 10.1007/s11548-015-1173-6
pubmed: 25847662
Codari M, Caffini M, Tartaglia GM, Sforza C, Baselli G (2017) Computer-aided cephalometric landmark annotation for CBCT data. Int J Comput Assist Radiol Surg 12(1):113–121. https://doi.org/10.1007/s11548-016-1453-9
doi: 10.1007/s11548-016-1453-9
pubmed: 27358080
Montúfar J, Romero M, Scougall-Vilchis RJ (2018) Hybrid approach for automatic cephalometric landmark annotation on cone-beam computed tomography volumes. American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics 154(1):140–150. https://doi.org/10.1016/j.ajodo.2017.08.028
doi: 10.1016/j.ajodo.2017.08.028
pubmed: 29957312
Neelapu BC, Kharbanda OP, Sardana V, Gupta A, Vasamsetti S, Balachandran R, Sardana HK (2018) Automatic localization of three-dimensional cephalometric landmarks on CBCT images by extracting symmetry features of the skull. Dentomaxillofac Radiol 47(2):20170054. https://doi.org/10.1259/dmfr.20170054
doi: 10.1259/dmfr.20170054
pubmed: 28845693
pmcid: 5965913
Montúfar J, Romero M, Scougall-Vilchis RJ (2018) Automatic 3-dimensional cephalometric landmarking based on active shape models in related projections. American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics 153(3):449–458. https://doi.org/10.1016/j.ajodo.2017.06.028
doi: 10.1016/j.ajodo.2017.06.028
pubmed: 29501121
Ghowsi A, Hatcher D, Suh H, Wile D, Castro W, Krueger J, Park J, Oh H (2022) Automated landmark identification on cone-beam computed tomography: accuracy and reliability. Angle Orthod 92(5):642–654. Advance online publication. https://doi.org/10.2319/122121-928.1
doi: 10.2319/122121-928.1
pubmed: 35653226
pmcid: 9374352
Lee JG, Jun S, Cho YW, Lee H, Kim GB, Seo JB, Kim N (2017) Deep learning in medical imaging: general overview. Korean J Radiol 18(4):570–584. https://doi.org/10.3348/kjr.2017.18.4.570
doi: 10.3348/kjr.2017.18.4.570
pubmed: 28670152
pmcid: 5447633
Hwang JJ, Jung YH, Cho BH, Heo MS (2019) An overview of deep learning in the field of dentistry. Imaging Sci Dent 49(1):1–7. https://doi.org/10.5624/isd.2019.49.1.1
doi: 10.5624/isd.2019.49.1.1
pubmed: 30941282
pmcid: 6444007
Tokuyasu T, Iwashita Y, Matsunobu Y, Kamiyama T, Ishikake M, Sakaguchi S, Ebe K, Tada K, Endo Y, Etoh T, Nakashima M, Inomata M (2021) Development of an artificial intelligence system using deep learning to indicate anatomical landmarks during laparoscopic cholecystectomy. Surg Endosc 35(4):1651–1658. https://doi.org/10.1007/s00464-020-07548-x
doi: 10.1007/s00464-020-07548-x
pubmed: 32306111
Antony PG, Sebastian A, Varghese KG, Sobhana CR, Mohan S, Soumithran CS, Domnic S, Jayakumar N (2017) Neurosensory evaluation of inferior alveolar nerve after bilateral sagittal split ramus osteotomy of mandible. J Oral Biol Craniofac Res 7(2):81–88. https://doi.org/10.1016/j.jobcr.2017.03.004
doi: 10.1016/j.jobcr.2017.03.004
pubmed: 28706780
pmcid: 5497326
Yue W, Yin D, Li C, Wang G, Xu T (2006) Automated 2-D cephalometric analysis on X-ray images by a model-based approach. IEEE Trans Biomed Eng 53(8):1615–1623. https://doi.org/10.1109/TBME.2006.876638
doi: 10.1109/TBME.2006.876638
pubmed: 16916096
Hassan B, Nijkamp P, Verheij H, Tairie J, Vink C, van der Stelt P, van Beek H (2013) Precision of identifying cephalometric landmarks with cone beam computed tomography in vivo. Eur J Orthod 35(1):38–44. https://doi.org/10.1093/ejo/cjr050
doi: 10.1093/ejo/cjr050
pubmed: 21447781
Katkar RA, Kummet C, Dawson D, Moreno Uribe L, Allareddy V, Finkelstein M, Ruprecht A (2013) Comparison of observer reliability of three-dimensional cephalometric landmark identification on subject images from Galileos and i-CAT cone beam CT. Dentomaxillofac Radiol 42(9):20130059. https://doi.org/10.1259/dmfr.20130059
doi: 10.1259/dmfr.20130059
pubmed: 23833319
pmcid: 3828023
Park SH, Yu HS, Kim KD, Lee KJ, Baik HS (2006) A proposal for a new analysis of craniofacial morphology by 3-dimensional computed tomography. Am J Orthod Dentofacial Orthop 129(5):600.e23–600.e34. https://doi.org/10.1016/j.ajodo.2005.11.032
doi: 10.1016/j.ajodo.2005.11.032
pubmed: 16679198
Puişoru M, Forna N, Fătu AM, Fătu R, Fătu C (2006) Analysis of mandibular variability in humans of different geographic areas. Ann Anat – Anatomischer Anzeiger 188(6):547–554. https://doi.org/10.1016/j.aanat.2006.05.015
doi: 10.1016/j.aanat.2006.05.015
pubmed: 17140148
Böckmann R, Meyns J, Dik E, Kessler P (2015) The modifications of the sagittal ramus split osteotomy: a literature review. Plast Reconstr Surg Glob Open 2(12):e271. https://doi.org/10.1097/GOX.0000000000000127
doi: 10.1097/GOX.0000000000000127
pubmed: 25587505
pmcid: 4292253
Moon JH, Hwang HW, Yu Y, Kim MG, Donatelli RE, Lee SJ (2020) How much deep learning is enough for automatic identification to be reliable? Angle Orthod 90(6):823–830. https://doi.org/10.2319/021920-116.1
doi: 10.2319/021920-116.1
pubmed: 33378507
pmcid: 8028421
Ponce-Garcia C, Ruellas A, Cevidanes L, Flores-Mir C, Carey JP, Lagravere-Vich M (2020) Measurement error and reliability of three available 3D superimposition methods in growing patients. Head Face Med 16(1):1. https://doi.org/10.1186/s13005-020-0215-7
doi: 10.1186/s13005-020-0215-7
pubmed: 31987041
pmcid: 6983972