Ortodontide Yapay Zekâ

Shujaat S, Bornstein MM, Price JB, Jacobs R. Integration of imaging modalities in digital dental workflows-possibilities, limitations, and potential future developments. Dentomaxillofacial Radiology. 2021;50(7):20210268.

Tekeli A. YAPAY ZEKÂNIN ORTODONTİK TEDAVİDEKİ ROLÜ. Kırıkkale Üniversitesi Tıp Fakültesi Dergisi. 2023;25(2):340-6.

Kurumu TD. Türk Dil Kurumu. D. Türk Tarih Kurumu Basımevi; 1954.

Muggleton S. Alan Turing and the development of Artificial Intelligence. AI communications. 2014;27(1):3-10.

Lin P, Hazelbaker T. Meeting the challenge of artificial intelligence: what CPAs need to know. The CPA Journal. 2019;89(6):48-52.

Domingos P. The master algorithm: How the quest for the ultimate learning machine will remake our world: Basic Books; 2015.

Mitchell TM, Mitchell TM. Machine learning: McGraw-hill New York; 1997.

McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics. 1943;5:115-33.

Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review. 1958;65(6):386.

Ding H, Wu J, Zhao W, Matinlinna JP, Burrow MF, Tsoi JK. Artificial intelligence in dentistry—A review. Frontiers in Dental Medicine. 2023;4:1085251.

Maltarollo VG, Honório KM, da Silva ABF. Applications of artificial neural networks in chemical problems. Artificial neural networks-architectures and applications. 2013:203-23.

Manovich L. Can we think without categories? Digital Culture & Society. 2018;4(1):17-28.

Hinton GE, Osindero S, Teh Y-W. A fast learning algorithm for deep belief nets. Neural computation. 2006;18(7):1527-54.

LeCun Y, Bengio Y, Hinton G. Deep learning. nature. 2015;521(7553):436-44.

do Nascimento Gerhardt M, Shujaat S, Jacobs R. AIM in Dentistry. Artificial Intelligence in Medicine: Springer; 2022. p. 905-18.

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. Journal of dental sciences. 2021;16(1):508-22.

Qiu B, van der Wel H, Kraeima J, Glas HH, Guo J, Borra RJ, et al. Automatic segmentation of mandible from conventional methods to deep learning—a review. Journal of personalized medicine. 2021;11(7):629.

Egger J, Pfarrkirchner B, Gsaxner C, Lindner L, Schmalstieg D, Wallner J, editors. Fully convolutional mandible segmentation on a valid ground-truth dataset. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2018: IEEE.

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. Journal of Dentistry. 2021;115:103865.

Eun H, Kim C, editors. Oriented tooth localization for periapical dental X-ray images via convolutional neural network. 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA); 2016: IEEE.

Yasa Y, Çelik Ö, Bayrakdar IS, Pekince A, Orhan K, Akarsu S, et al. An artificial intelligence proposal to automatic teeth detection and numbering in dental bite-wing radiographs. Acta Odontologica Scandinavica. 2021;79(4):275-81.

Ariji Y, Yanashita Y, Kutsuna S, Muramatsu C, Fukuda M, Kise Y, et al. Automatic detection and classification of radiolucent lesions in the mandible on panoramic radiographs using a deep learning object detection technique. Oral surgery, oral medicine, oral pathology and oral radiology. 2019;128(4):424-30.

Lee JH, Kim DH, Jeong SN. Diagnosis of cystic lesions using panoramic and cone beam computed tomographic images based on deep learning neural network. Oral diseases. 2020;26(1):152-8.

Orhan K, Bayrakdar I, Ezhov M, Kravtsov A, Özyürek T. Evaluation of artificial intelligence for detecting periapical pathosis on cone‐beam computed tomography scans. International endodontic journal. 2020;53(5):680-9.

Bas B, Ozgonenel O, Ozden B, Bekcioglu B, Bulut E, Kurt M. Use of artificial neural network in differentiation of subgroups of temporomandibular internal derangements: a preliminary study. Journal of Oral and Maxillofacial Surgery. 2012;70(1):51-9.

Nam Y, Kim HG, Kho HS. Differential diagnosis of jaw pain using informatics technology. Journal of Oral Rehabilitation. 2018;45(8):581-8.

Kuwana R, Ariji Y, Fukuda M, Kise Y, Nozawa M, Kuwada C, et al. Performance of deep learning object detection technology in the detection and diagnosis of maxillary sinus lesions on panoramic radiographs. Dentomaxillofacial Radiology. 2021;50(1):20200171.

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 radiology. 2019;35:301-7.

Gaudin R, Vinayahalingam S, van Nistelrooij N, Ghanad I, Otto W, Kewenig S, et al. AI-Powered Identification of Osteoporosis in Dental Panoramic Radiographs: Addressing Methodological Flaws in Current Research. Diagnostics. 2024;14(20):2298.

Gaudin R, Otto W, Ghanad I, Kewenig S, Rendenbach C, Alevizakos V, et al. Enhanced Osteoporosis Detection Using Artificial Intelligence: A Deep Learning Approach to Panoramic Radiographs with an Emphasis on the Mental Foramen. Medical Sciences. 2024;12(3):49.

Lister P, Sudharson N, Joseph M, Kaur P. Cloud intelligence in diagnosis? British Dental Journal. 2023;235(11):843-.

Selwitz RH, Ismail AI, Pitts NB. Dental caries. The Lancet. 2007;369(9555):51-9.

Moutselos K, Berdouses E, Oulis C, Maglogiannis I, editors. Recognizing occlusal caries in dental intraoral images using deep learning. 2019 41st annual international conference of the IEEE engineering in medicine and biology society (EMBC); 2019: IEEE.

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. Journal of dentistry. 2020;100:103425.

Zheng L, Wang H, Mei L, Chen Q, Zhang Y, Zhang H. Artificial intelligence in digital cariology: a new tool for the diagnosis of deep caries and pulpitis using convolutional neural networks. Annals of Translational Medicine. 2021;9(9).

Abdalla-Aslan R, Yeshua T, Kabla D, Leichter I, Nadler C. An artificial intelligence system using machine-learning for automatic detection and classification of dental restorations in panoramic radiography. Oral surgery, oral medicine, oral pathology and oral radiology. 2020;130(5):593-602.

Aminoshariae A, Kulild J, Nagendrababu V. Artificial intelligence in endodontics: current applications and future directions. Journal of endodontics. 2021;47(9):1352-7.

Keskin C, Keleş A. Digital applications in endodontics. Journal of Experimental and Clinical Medicine. 2021;38(3s):168-74.

Saghiri MA, Asgar K, Boukani K, Lotfi M, Aghili H, Delvarani A, et al. A new approach for locating the minor apical foramen using an artificial neural network. International endodontic journal. 2012;45(3):257-65.

Johari M, Esmaeili F, Andalib A, Garjani S, Saberkari H. Detection of vertical root fractures in intact and endodontically treated premolar teeth by designing a probabilistic neural network: an ex vivo study. Dentomaxillofacial Radiology. 2017;46(2):20160107.

Hiraiwa T, Ariji Y, Fukuda M, Kise Y, Nakata K, Katsumata A, et al. A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography. Dentomaxillofacial Radiology. 2019;48(3):20180218.

Kim BS, Yeom HG, Lee JH, Shin WS, Yun JP, Jeong SH, et al. Deep learning-based prediction of paresthesia after third molar extraction: A preliminary study. Diagnostics. 2021;11(9):1572.

Zhang W, Li J, Li Z-B, Li Z. Predicting postoperative facial swelling following impacted mandibular third molars extraction by using artificial neural networks evaluation. Scientific reports. 2018;8(1):12281.

Danjo A, Kuwada C, Aijima R, Kamohara A, Fukuda M, Ariji Y, et al. Limitations of panoramic radiographs in predicting mandibular wisdom tooth extraction and the potential of deep learning models to overcome them. Scientific Reports. 2024;14(1):30806.

Sukegawa S, Yoshii K, Hara T, Matsuyama T, Yamashita K, Nakano K, et al. Multi-task deep learning model for classification of dental implant brand and treatment stage using dental panoramic radiograph images. Biomolecules. 2021;11(6):815.

Kurt Bayrakdar S, Orhan K, Bayrakdar IS, Bilgir E, Ezhov M, Gusarev M, et al. A deep learning approach for dental implant planning in cone-beam computed tomography images. BMC medical imaging. 2021;21(1):86.

Liu C-H, Lin C-J, Hu Y-H, You Z-H. Predicting the failure of dental implants using supervised learning techniques. Applied Sciences. 2018;8(5):698.

Vadzyuk S, Boliuk Y, Luchynskyi M, Papinko I, Vadzyuk N. Prediction of the development of periodontal disease. Proceeding of the Shevchenko Scientific Society Medical Sciences. 2021;65(2).

Atalay C, Balcı N, Toygar H. Periodontal hastalık değerlendirmesi ve prognozunda yapay zekâ katkıları. 2023.

Kılıc MC, Bayrakdar IS, Çelik Ö, Bilgir E, Orhan K, Aydın OB, et al. Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs. Dentomaxillofacial Radiology. 2021;50(6):20200172.

Kaya E, Gunec HG, Aydin KC, Urkmez ES, Duranay R, Ates HF. A deep learning approach to permanent tooth germ detection on pediatric panoramic radiographs. Imaging science in dentistry. 2022;52(3):275.

Duman S, Yılmaz EF, Eşer G, Çelik Ö, Bayrakdar IS, Bilgir E, et al. Detecting the presence of taurodont teeth on panoramic radiographs using a deep learning-based convolutional neural network algorithm. Oral Radiology. 2023;39(1):207-14.

Ahn Y, Hwang JJ, Jung Y-H, Jeong T, Shin J. Automated mesiodens classification system using deep learning on panoramic radiographs of children. Diagnostics. 2021;11(8):1477.

Zhu H, Yu H, Zhang F, Cao Z, Wu F, Zhu F. Automatic segmentation and detection of ectopic eruption of first permanent molars on panoramic radiographs based on nnU‐Net. International Journal of Paediatric Dentistry. 2022;32(6):785-92.

Zhao T, Zhou J, Yan J, Cao L, Cao Y, Hua F, et al. Automated adenoid hypertrophy assessment with lateral cephalometry in children based on artificial intelligence. Diagnostics. 2021;11(8):1386.

You W, Hao A, Li S, Wang Y, Xia B. Deep learning-based dental plaque detection on primary teeth: a comparison with clinical assessments. BMC Oral Health. 2020;20:1-7.

Koopaie M, Salamati M, Montazeri R, Davoudi M, Kolahdooz S. Salivary cystatin S levels in children with early childhood caries in comparison with caries-free children; statistical analysis and machine learning. BMC Oral Health. 2021;21(1):650.

Karhade DS, Roach J, Shrestha P, Simancas-Pallares MA, Ginnis J, Burk ZJ, et al. An automated machine learning classifier for early childhood caries. Pediatric dentistry. 2021;43(3):191-7.

Lee J-H, Jeong S-N. Efficacy of deep convolutional neural network algorithm for the identification and classification of dental implant systems, using panoramic and periapical radiographs: A pilot study. Medicine. 2020;99(26):e20787.

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;20:1-16.

Raith S, Vogel EP, Anees N, Keul C, Güth J-F, Edelhoff D, et al. Artificial Neural Networks as a powerful numerical tool to classify specific features of a tooth based on 3D scan data. Computers in biology and medicine. 2017;80:65-76.

Yamaguchi S, Lee C, Karaer O, Ban S, Mine A, Imazato S. Predicting the debonding of CAD/CAM composite resin crowns with AI. Journal of Dental Research. 2019;98(11):1234-8.

Cohen A, Ip H-S, Linney A. A preliminary study of computer recognition and identification of skeletal landmarks as a new method of cephalometric analysis. British Journal of Orthodontics. 1984;11(3):143-54.

Levy-Mandel A, Venetsanopoulos A, Tsotsos J. Knowledge-based landmarking of cephalograms. Computers and Biomedical Research. 1986;19(3):282-309.

Leonardi R, Giordano D, Maiorana F. An evaluation of cellular neural networks for the automatic identification of cephalometric landmarks on digital images. BioMed Research International. 2009;2009(1):717102.

Arık SÖ, Ibragimov B, Xing L. Fully automated quantitative cephalometry using convolutional neural networks. Journal of Medical Imaging. 2017;4(1):014501-.

Park J-H, Hwang H-W, Moon J-H, Yu Y, Kim H, Her S-B, et al. Automated identification of cephalometric landmarks: Part 1—Comparisons between the latest deep-learning methods YOLOV3 and SSD. The Angle Orthodontist. 2019;89(6):903-9.

Hwang H-W, Park J-H, Moon J-H, Yu Y, Kim H, Her S-B, et al. Automated identification of cephalometric landmarks: Part 2-Might it be better than human? The Angle Orthodontist. 2020;90(1):69-76.

Kunz F, Stellzig-Eisenhauer A, Zeman F, Boldt J. Artificial intelligence in orthodontics: Evaluation of a fully automated cephalometric analysis using a customized convolutional neural network. Journal of Orofacial Orthopedics/Fortschritte der Kieferorthopadie. 2020;81(1).

Uğurlu M. Performance of a convolutional neural network-based artificial intelligence algorithm for automatic cephalometric landmark detection. Turkish Journal of Orthodontics. 2022;35(2):94.

Yu H, Cho S, Kim M, Kim W, Kim J, Choi J. Automated skeletal classification with lateral cephalometry based on artificial intelligence. Journal of dental research. 2020;99(3):249-56.

Talaat S, Kaboudan A, Talaat W, Kusnoto B, Sanchez F, Elnagar MH, et al., editors. The validity of an artificial intelligence application for assessment of orthodontic treatment need from clinical images. Seminars in Orthodontics; 2021: Elsevier.

Li S, Guo Z, Lin J, Ying S. Artificial intelligence for classifying and archiving orthodontic images. BioMed Research International. 2022;2022(1):1473977.

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. Medical physics. 2016;43(1):336-46.

Chen S, Wang L, Li G, Wu T-H, Diachina S, Tejera B, et al. Machine learning in orthodontics: Introducing a 3D auto-segmentation and auto-landmark finder of CBCT images to assess maxillary constriction in unilateral impacted canine patients. The Angle Orthodontist. 2020;90(1):77-84.

Xie X, Wang L, Wang A. Artificial neural network modeling for deciding if extractions are necessary prior to orthodontic treatment. The Angle Orthodontist. 2010;80(2):262-6.

Jung S-K, Kim T-W. New approach for the diagnosis of extractions with neural network machine learning. American Journal of Orthodontics and Dentofacial Orthopedics. 2016;149(1):127-33.

Li P, Kong D, Tang T, Su D, Yang P, Wang H, et al. Orthodontic treatment planning based on artificial neural networks. Scientific reports. 2019;9(1):2037.

El-Dawlatly MM, Abdelmaksoud AR, Amer OM, El-Dakroury AE, Mostafa YA. Evaluation of the efficiency of computerized algorithms to formulate a decision support system for deepbite treatment planning. American Journal of Orthodontics and Dentofacial Orthopedics. 2021;159(4):512-21.

Choi H-I, Jung S-K, Baek S-H, Lim WH, Ahn S-J, Yang I-H, et al. Artificial intelligent model with neural network machine learning for the diagnosis of orthognathic surgery. Journal of Craniofacial Surgery. 2019;30(7):1986-9.

Shin W, Yeom H-G, Lee GH, Yun JP, Jeong SH, Lee JH, et al. Deep learning based prediction of necessity for orthognathic surgery of skeletal malocclusion using cephalogram in Korean individuals. BMC Oral Health. 2021;21:1-7.

Knoops PG, Papaioannou A, Borghi A, Breakey RW, Wilson AT, Jeelani O, et al. A machine learning framework for automated diagnosis and computer-assisted planning in plastic and reconstructive surgery. Scientific reports. 2019;9(1):13597.

Hägg U, Taranger J. Skeletal stages of the hand and wrist as indicators of the pubertal growth spurt. Acta Odontologica Scandinavica. 1980;38(3):187-200.

Flores-Mir C, Burgess CA, Champney M, Jensen RJ, Pitcher MR, Major PW. Correlation of skeletal maturation stages determined by cervical vertebrae and hand-wrist evaluations. The Angle Orthodontist. 2006;76(1):1-5.

Lamparski DG, Nanda SK. Skeletal age assessment utilizing cervical vertebrae. Craniofacial growth series. 2002;39:171-84.

Larson DB, Chen MC, Lungren MP, Halabi SS, Stence NV, Langlotz CP. Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs. Radiology. 2018;287(1):313-22.

Lee H, Tajmir S, Lee J, Zissen M, Yeshiwas BA, Alkasab TK, et al. Fully automated deep learning system for bone age assessment. Journal of digital imaging. 2017;30:427-41.

Kök H, Acilar AM, İzgi MS. Usage and comparison of artificial intelligence algorithms for determination of growth and development by cervical vertebrae stages in orthodontics. Progress in Orthodontics. 2019;20:1-10.

Kök H, İzgi MS, Acılar AM. Evaluation of the artificial neural network and Naive Bayes models trained with vertebra ratios for growth and development determination. Turkish Journal of Orthodontics. 2020;34(1):2.

Kök H, Izgi MS, Acilar AM. Determination of growth and development periods in orthodontics with artificial neural network. Orthodontics & craniofacial research. 2021;24:76-83.

Amasya H, Yildirim D, Aydogan T, Kemaloglu N, Orhan K. Cervical vertebral maturation assessment on lateral cephalometric radiographs using artificial intelligence: comparison of machine learning classifier models. Dentomaxillofacial Radiology. 2020;49(5):20190441.

Makaremi M, Lacaule C, Mohammad-Djafari A. Deep learning and artificial intelligence for the determination of the cervical vertebra maturation degree from lateral radiography. Entropy. 2019;21(12):1222.

Canan S, Aksoy A. Ortodonti ve üst solunum yolları ilişkisi. Smyrna Tıp Dergisi. 2013;1:47-52.

Page DC, Mahony D. The airway, breathing and orthodontics. Todays FDA. 2010;22(2):43-7.

Shen Y, Li X, Liang X, Xu H, Li C, Yu Y, et al. A deep‐learning‐based approach for adenoid hypertrophy diagnosis. Medical Physics. 2020;47(5):2171-81.

Dong W, Chen Y, Li A, Mei X, Yang Y. Automatic detection of adenoid hypertrophy on cone-beam computed tomography based on deep learning. American Journal of Orthodontics and Dentofacial Orthopedics. 2023;163(4):553-60. e3.

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. Orthodontics & Craniofacial Research. 2021;24:117-23.

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. Journal of Dentistry. 2021;111:103705.

Park JH, Kim Y-J, Kim J, Kim J, Kim I-H, Kim N, et al., editors. Use of artificial intelligence to predict outcomes of nonextraction treatment of Class II malocclusions. Seminars in Orthodontics; 2021: Elsevier.

Tanikawa C, Yamashiro T. Development of novel artificial intelligence systems to predict facial morphology after orthognathic surgery and orthodontic treatment in Japanese patients. Scientific Reports. 2021;11(1):15853.

Park Y, Choi J, Kim Y, Choi S, Lee J, Kim K, et al. Deep learning–based prediction of the 3D postorthodontic facial changes. Journal of Dental Research. 2022;101(11):1372-9.

Xu L, Mei L, Lu R, Li Y, Li H, Li Y. Predicting patient experience of Invisalign treatment: An analysis using artificial neural network. Korean Journal of Orthodontics. 2022;52(4):268-77.

Dhillon H, Chaudhari PK, Dhingra K, Kuo R-F, Sokhi RK, Alam MK, et al. Current applications of artificial intelligence in cleft care: a scoping review. Frontiers in medicine. 2021;8:676490.

Agarwal S, Hallac RR, Mishra R, Li C, Daescu O, Kane A, editors. Image based detection of craniofacial abnormalities using feature extraction by classical convolutional neural network. 2018 IEEE 8th international conference on computational advances in bio and medical sciences (ICCABS); 2018: IEEE.

Jurek J, Wójtowicz W, Wójtowicz A. Syntactic pattern recognition-based diagnostics of fetal palates. Pattern Recognition Letters. 2020;133:144-50.

Zhang Y, Pei Y, Chen S, Guo Y, Ma G, Xu T, et al., editors. Volumetric registration-based cleft volume estimation of alveolar cleft grafting procedures. 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI); 2020: IEEE.

Schiebl J, Bauer FX, Grill F, Loeffelbein DJ. RapidNAM: algorithm for the semi-automated generation of nasoalveolar molding device designs for the presurgical treatment of bilateral cleft lip and palate. IEEE Transactions on Biomedical Engineering. 2019;67(5):1263-71.

Wutiwiwatchai C, Chootrakool P, Kasuriya S, Makarabhirom K, Ooppanasak N, Prathanee B, editors. Naso-articulometry speech database for cleft-palate speech assessment. 2018 Oriental COCOSDA-International Conference on Speech Database and Assessments; 2018: IEEE.

Li Y, Cheng J, Mei H, Ma H, Chen Z, Li Y, editors. Clpnet: cleft lip and palate surgery support with deep learning. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2019: IEEE.

Lin G, Kim P-J, Baek S-H, Kim H-G, Kim S-W, Chung J-H. Early prediction of the need for orthognathic surgery in patients with repaired unilateral cleft lip and palate using machine learning and longitudinal lateral cephalometric analysis data. Journal of Craniofacial Surgery. 2021;32(2):616-20.