Endodontide Yapay Zekânın Yeri
Özet
Yapay zekâ terimi problem çözmede insan bilişsel yeteneklerini taklit edebilen herhangi bir makine veya teknoloji olarak tanımlanmaktadır. Yapay zeka pek çok alanda olduğu gibi diş hekimliği pratiğinde de yerini almaya başlamıştır. Tanı, tedavi stratejisini belirleme, tedavi sonuçlarını tahmin etme gibi çeşitli aşamalarda yapay zekâdan faydalanılmaktadır. Yapay zekâ bu konularda başarılı sonuçlar elde etmiştir. Endodontide de yapay zekâ bahsedilen bu aşamalarda kullanılmaktadır. Son yıllarda yapay zekanın endodontideki rolünü ele alan çalışmalar popülerlik kazanmıştır. Radyolojik tetkikler üzerinden periapikal lezyonların ve kök kırıklarının tespiti, kök kanal anatomisinin analizi ve kök kanal tedavisinin planlanması açısından yapay zekâ modelleri ele alınmıştır. Pek çok çalışmada yapay zekanın bahsedilen bu konularda başarılı olduğu bildirilmiştir. Bu sonuçların daha ileri çalışmalarla desteklenmesi gerekmektedir. Bu yazıda güncel literatür taranarak endodonti klinik pratiğinde yapay zekâ kullanımı hakkında bilgi verilmiştir.
The term artificial intelligence is defined as any machine or technology that can mimic human cognitive abilities in problem-solving. Artificial intelligence has started to find its place in dental practice, just as in many other fields. It is utilized in various stages such as diagnosis, determining treatment strategies, and predicting treatment outcomes. Artificial intelligence has achieved successful results in these areas. In endodontics, artificial intelligence is also used in these mentioned stages. In recent years, studies addressing the role of artificial intelligence in endodontics have gained popularity. Artificial intelligence models have been examined for detecting periapical lesions, root fractures and analyzing root canal anatomy through radiological evaluations, and planning root canal treatments. Many studies have reported that artificial intelligence has been successful in these areas. However, these results need to be supported by further research. This article provides information about the use of artificial intelligence in endodontic clinical practice by reviewing the current literature.
Referanslar
Ahmed ZH, Almuharib AM, Abdulkarim AA, et al. Artificial intelligence and its application in Endodontics: A review. The Journal of Contemporary Dental Practice. 2023;24(11): 912–917. doi: 10.5005/jp-journals-10024-3593
Setzer FC, Li J, Khan AA. The use of artificial intelligence in Endodontics. Journal of Dental Research. 2024:220345241255593 (Epub ahead of print). doi: 10.1177/00220345241255593
Mohammad-Rahimi H, Sohrabniya F, Ourang SA, et al. Artificial intelligence in endodontics: Data preparation, clinical applications, ethical considerations, limitations, and future directions. International Endodontic Journal. 2024 (Epub ahead of print). doi: 10.1111/iej.14128
Koc S, Felek T, Erkal D, et al. The developing technology of artificial intelligence in endodontics: a literature review. Akdeniz Dental Journal. 2023;2(2): 99-104.
Aminoshariae A, Kulild J, Nagendrababu V. Artificial intelligence in endodontics: current applications and future directions. Journal of Endodontics. 2021;47(9): 1352–1357. doi: 10.1016/j.joen.2021.06.003
Umer F, Habib S. Critical analysis of artificial intelligence in endodontics: A scoping review. Journal of Endodontics. 2022;48(2): 152–160. doi: 10.1016/j.joen.2021.11.007
McCarthy J, Minsky ML, Rochester N, et al. A proposal for the Dartmouth summer research project on artificial intelligence, August 31, 1955. AI Magazine. 2006;27(4): 12. doi: 10.1609/aimag.v27i4.1904
O'Leary DE. Artificial intelligence and big data. IEEE Intelligent Systems. 2013;28(2): 96–99. doi: 10.1109/MIS.2013.39
Lai G, Dunlap C, Gluskin A, et al. Artificial intelligence in Endodontics. Journal of the California Dental Association. 2023;51(1): 2199933, doi: 10.1080/19424396.2023.2199933
Sudeep P, Gehlot PM, Murali B, et al. Artificial intelligence in endodontics: A narrative review. Journal of International Oral Health. 2023;15: 134-141. doi: 0.4103/jioh.jioh_257_22
Choi RY, Coyner AS, Kalpathy-Cramer J, et al. Introduction to machine learning, neural networks, and deep learning. Translational Vision Science & Technology. 2020;9(2): 14. doi:10.1167/tvst.9.2.14
Khanagar SB, Ehaideb AA, Maganur PC, et al. Developments, application, and performance of artificial intelligence in dentistry—A systematic review. Journal of Dental Sciences. 2021;19: 508-522. doi: 10.1016/j.jds.2020.06.019
Ramesh AN, Kambhampati C, Monson JR, et al. Artificial intelligence in medicine. Annals of the Royal College of Surgeons of England. 2004;86: 334–338. doi: 10.1308/147870804290
Lecun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521: 436-444. doi: 10.1038/nature14539
Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism: Clinical and Experimental. 2017;69: 36-40. doi: 10.1016/j.metabol.2017.01.011
Brickley MR, Shepherd JP, Armstrong RA. Neural networks: A new technique for development of decision support systems in dentistry. Journal of Dentistry. 1998;26: 305-309. doi: 10.1016/s0300-5712(97)00027-4
Boreak N. Effectiveness of artificial intelligence applications designed for endodontic diagnosis, decision-making, and prediction of prognosis: A systematic review. The Journal of Contemporary Dental Practice. 2020;21(8): 926–934.
Albitar L, Zhao T, Huang C, et al. Artificial intelligence (AI) for detection and localization of unobturated second mesial buccal (MB2) canals in cone-beam computed tomography (CBCT). Diagnostics. 2022;12(12): 3214. doi:10.3390/diagnostics12123214
Yang S, Lee H, Jang B, et al. Development and validation of a visually explainable deep learning model for classification of C-shaped canals of the mandibular second molars in periapical and panoramic dental radiographs. Journal of Endodontics. 2022;48(7): 914–921. doi: 10.1016/j. joen.2022.04.007.
Sherwood AA, Sherwood AI, Setzer FC, et al. A deep learning approach to segment and classify C-shaped canal morphologies in mandibular second molars using cone-beam computed tomography. Journal of Endodontics. 2021;47(12): 1907–1916. doi: 10.1016/j.joen.2021.09.009
Goncharuk-Khomyn M, Noenko I, Cavalcanti AL, et al. Artificial intelligence in endodontics:relevant trends and practical perspectives. Ukrainian Dental Journal. 2023;2(1): 96-101. doi: 10.56569/UDJ.2.1.2023.96-101
Hiraiwa T, Ariji Y, Fukuda M, 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: 20180218. doi: 10.1259/dmfr.20180218
Jeon SJ, Yun JP, Yeom HG, et al. Deep-learning for predicting C-shaped canals in mandibular second molars on panoramic radiographs. Dentomaxillofacial Radiology. 2021;50: 20200513. doi: 10.1259/dmfr.20200513
Mallishery S, Chhatpar P, Banga KS, et al. The precision of case difficulty and referral decisions: an innovative automated approach. Clinical Oral Investigations. 2020;24(6): 1909-1915. doi: 10.1007/s00784-019-03050-4
Becconsall‐Ryan K, Tong D, Love RM. Radiolucent inflammatory jaw lesions: a twenty‐year analysis. International Endodontic Journal. 2010;43(10): 859–865. doi: 10.1111/j.1365-2591.2010.01751.x
Chapman MN, Nadgir RN, Akman AS, et al. Periapical lucency around the tooth: radiologic evaluation and differential diagnosis. RadioGraphics 2013;33(1): 15–32. doi: 10.1148/rg.331125172
Velvart P, Hecker H, Tillinger G. Detection of the apical lesion and the mandibular canal in conventional radiography and computed tomography. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontics. 2001;92(6): 682–688. doi: 10.1067/moe.2001.118904
Bender I. Factors influencing radiographic appearance of bony lesions. Journal of Endodontics. 1997;23(1): 5–14. doi:10.1016/S0099-2399(97)80199-9.
Endres MG, Hillen F, Salloumis M, et al. Development of a deep learning algorithm for periapical disease detection in dental radiographs. Diagnostics. 2020;10(6): 430. doi: 10.3390/diagnostics10060430
Orhan K, Bayrakdar IS, Ezhov M, et al. Evaluation of artificial intelligence for detecting periapical pathosis on cone‐beam computed tomography scans. International Endodontic Journal. 2020;53(5): 680–689. doi: 10.1111/iej.13265
Issa J, Jaber M, Rifai I, et al. Diagnostic test accuracy of artificial intelligence in detecting periapical periodontitis on two-dimensional radiographs: A retrospective study and literature review. Medicina (Kaunas, Lithuania). 2023;59(4): 768. doi: 10.3390/medicina59040768
Ekert T, Krois J, Meinhold L, et al. Deep learning for the radiographic detection of apical lesions. Journal of Endodontics. 2019;45: 917–922.e5. doi: 10.1016/j.joen.2019.03.016
Mora MA, Mol A, Tyndall DA, et al. In vitro assessment of local computed tomography for the detection of longitudinal tooth fractures. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontics. 2007;103(6): 825–829. doi: 10.1016/j.tripleo.2006.09.009
Fukuda M, Inamoto K, Shibata N, et al. Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography. Oral Radiology. 2020;36(4): 337–343. doi: 10.1007/s11282-019-00409-x
Hu Z, Cao D, Hu Y, et al. Diagnosis of in vivo vertical root fracture using deep learning on cone-beam CT images. BMC Oral Health. 2022;22(1): 382. doi: 10.1186/s12903-022-02422-9
Saghiri MA, Asgar K, Boukani KK, et al. A new approach for locating the minor apical foramen using an artificial neural network. International Endodontic Journal. 2012;45(3): 257–265. doi: 10.1111/j.1365-2591.2011.01970.x
Saghiri MA, Garcia-Godoy F, Gutmann JL, et al. The reliability of artificial neural network in locating minor apical foramen: A cadaver study. Journal of Endodontics. 2012;38(8): 1130–1134. doi: 10.1016/j.joen.2012.05.004
Campo L, Aliaga IJ, De Paz JF, et al. Retreatment Predictions in Odontology by means of CBR Systems. Computational Intelligence and Neuroscience. 2016;2016: 7485250. doi: 10.1155/2016/7485250
Buyuk C, Arican Alpay B, Er F. Detection of the separated root canal instrument on panoramic radiograph: a comparison of LSTM and CNN deep learning methods. Dentomaxillofacial Radiology, 2023;52(3): 20220209. doi: 10.1259/dmfr.20220209
Özbay Y, Kazangirler BY, Özcan C, et al. Detection of the separated endodontic instrument on periapical radiographs using a deep learning-based convolutional neural network algorithm. Australian Endodontic Journal. 2023;50: 131–139. doi: 10.1111/aej.12822
Alexander B, John S. Artificial intelligence in dentistry: current concepts and a peep into the future. International Journal of Advanced Research. 2018;6(12): 1105–1108. doi: 10.21474/IJAR01/8242.
Asiri AF, Altuwalah AS. The role of neural artificial intelligence for diagnosis and treatment planning in endodontics: A qualitative review. Saudi Dental Journal. 2022;34(4): 270-281. doi: 10.1016/j.sdentj.2022.04.004.
Das S. Artificial intelligence in Endodontics: A peek into the future. RGUHS Journal of Dental Sciences. 2022;14(3): 35-37. doi: 10.26715/rjds.14_3_7
Chen YW, Stanley K, Att W. Artificial intelligence in dentistry: current applications and future perspectives. Quintessence International (Berlin, Germany : 1985). 2020;51(3): 248-257. doi: 10.3290/j.qi.a43952.
Referanslar
Ahmed ZH, Almuharib AM, Abdulkarim AA, et al. Artificial intelligence and its application in Endodontics: A review. The Journal of Contemporary Dental Practice. 2023;24(11): 912–917. doi: 10.5005/jp-journals-10024-3593
Setzer FC, Li J, Khan AA. The use of artificial intelligence in Endodontics. Journal of Dental Research. 2024:220345241255593 (Epub ahead of print). doi: 10.1177/00220345241255593
Mohammad-Rahimi H, Sohrabniya F, Ourang SA, et al. Artificial intelligence in endodontics: Data preparation, clinical applications, ethical considerations, limitations, and future directions. International Endodontic Journal. 2024 (Epub ahead of print). doi: 10.1111/iej.14128
Koc S, Felek T, Erkal D, et al. The developing technology of artificial intelligence in endodontics: a literature review. Akdeniz Dental Journal. 2023;2(2): 99-104.
Aminoshariae A, Kulild J, Nagendrababu V. Artificial intelligence in endodontics: current applications and future directions. Journal of Endodontics. 2021;47(9): 1352–1357. doi: 10.1016/j.joen.2021.06.003
Umer F, Habib S. Critical analysis of artificial intelligence in endodontics: A scoping review. Journal of Endodontics. 2022;48(2): 152–160. doi: 10.1016/j.joen.2021.11.007
McCarthy J, Minsky ML, Rochester N, et al. A proposal for the Dartmouth summer research project on artificial intelligence, August 31, 1955. AI Magazine. 2006;27(4): 12. doi: 10.1609/aimag.v27i4.1904
O'Leary DE. Artificial intelligence and big data. IEEE Intelligent Systems. 2013;28(2): 96–99. doi: 10.1109/MIS.2013.39
Lai G, Dunlap C, Gluskin A, et al. Artificial intelligence in Endodontics. Journal of the California Dental Association. 2023;51(1): 2199933, doi: 10.1080/19424396.2023.2199933
Sudeep P, Gehlot PM, Murali B, et al. Artificial intelligence in endodontics: A narrative review. Journal of International Oral Health. 2023;15: 134-141. doi: 0.4103/jioh.jioh_257_22
Choi RY, Coyner AS, Kalpathy-Cramer J, et al. Introduction to machine learning, neural networks, and deep learning. Translational Vision Science & Technology. 2020;9(2): 14. doi:10.1167/tvst.9.2.14
Khanagar SB, Ehaideb AA, Maganur PC, et al. Developments, application, and performance of artificial intelligence in dentistry—A systematic review. Journal of Dental Sciences. 2021;19: 508-522. doi: 10.1016/j.jds.2020.06.019
Ramesh AN, Kambhampati C, Monson JR, et al. Artificial intelligence in medicine. Annals of the Royal College of Surgeons of England. 2004;86: 334–338. doi: 10.1308/147870804290
Lecun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521: 436-444. doi: 10.1038/nature14539
Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism: Clinical and Experimental. 2017;69: 36-40. doi: 10.1016/j.metabol.2017.01.011
Brickley MR, Shepherd JP, Armstrong RA. Neural networks: A new technique for development of decision support systems in dentistry. Journal of Dentistry. 1998;26: 305-309. doi: 10.1016/s0300-5712(97)00027-4
Boreak N. Effectiveness of artificial intelligence applications designed for endodontic diagnosis, decision-making, and prediction of prognosis: A systematic review. The Journal of Contemporary Dental Practice. 2020;21(8): 926–934.
Albitar L, Zhao T, Huang C, et al. Artificial intelligence (AI) for detection and localization of unobturated second mesial buccal (MB2) canals in cone-beam computed tomography (CBCT). Diagnostics. 2022;12(12): 3214. doi:10.3390/diagnostics12123214
Yang S, Lee H, Jang B, et al. Development and validation of a visually explainable deep learning model for classification of C-shaped canals of the mandibular second molars in periapical and panoramic dental radiographs. Journal of Endodontics. 2022;48(7): 914–921. doi: 10.1016/j. joen.2022.04.007.
Sherwood AA, Sherwood AI, Setzer FC, et al. A deep learning approach to segment and classify C-shaped canal morphologies in mandibular second molars using cone-beam computed tomography. Journal of Endodontics. 2021;47(12): 1907–1916. doi: 10.1016/j.joen.2021.09.009
Goncharuk-Khomyn M, Noenko I, Cavalcanti AL, et al. Artificial intelligence in endodontics:relevant trends and practical perspectives. Ukrainian Dental Journal. 2023;2(1): 96-101. doi: 10.56569/UDJ.2.1.2023.96-101
Hiraiwa T, Ariji Y, Fukuda M, 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: 20180218. doi: 10.1259/dmfr.20180218
Jeon SJ, Yun JP, Yeom HG, et al. Deep-learning for predicting C-shaped canals in mandibular second molars on panoramic radiographs. Dentomaxillofacial Radiology. 2021;50: 20200513. doi: 10.1259/dmfr.20200513
Mallishery S, Chhatpar P, Banga KS, et al. The precision of case difficulty and referral decisions: an innovative automated approach. Clinical Oral Investigations. 2020;24(6): 1909-1915. doi: 10.1007/s00784-019-03050-4
Becconsall‐Ryan K, Tong D, Love RM. Radiolucent inflammatory jaw lesions: a twenty‐year analysis. International Endodontic Journal. 2010;43(10): 859–865. doi: 10.1111/j.1365-2591.2010.01751.x
Chapman MN, Nadgir RN, Akman AS, et al. Periapical lucency around the tooth: radiologic evaluation and differential diagnosis. RadioGraphics 2013;33(1): 15–32. doi: 10.1148/rg.331125172
Velvart P, Hecker H, Tillinger G. Detection of the apical lesion and the mandibular canal in conventional radiography and computed tomography. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontics. 2001;92(6): 682–688. doi: 10.1067/moe.2001.118904
Bender I. Factors influencing radiographic appearance of bony lesions. Journal of Endodontics. 1997;23(1): 5–14. doi:10.1016/S0099-2399(97)80199-9.
Endres MG, Hillen F, Salloumis M, et al. Development of a deep learning algorithm for periapical disease detection in dental radiographs. Diagnostics. 2020;10(6): 430. doi: 10.3390/diagnostics10060430
Orhan K, Bayrakdar IS, Ezhov M, et al. Evaluation of artificial intelligence for detecting periapical pathosis on cone‐beam computed tomography scans. International Endodontic Journal. 2020;53(5): 680–689. doi: 10.1111/iej.13265
Issa J, Jaber M, Rifai I, et al. Diagnostic test accuracy of artificial intelligence in detecting periapical periodontitis on two-dimensional radiographs: A retrospective study and literature review. Medicina (Kaunas, Lithuania). 2023;59(4): 768. doi: 10.3390/medicina59040768
Ekert T, Krois J, Meinhold L, et al. Deep learning for the radiographic detection of apical lesions. Journal of Endodontics. 2019;45: 917–922.e5. doi: 10.1016/j.joen.2019.03.016
Mora MA, Mol A, Tyndall DA, et al. In vitro assessment of local computed tomography for the detection of longitudinal tooth fractures. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontics. 2007;103(6): 825–829. doi: 10.1016/j.tripleo.2006.09.009
Fukuda M, Inamoto K, Shibata N, et al. Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography. Oral Radiology. 2020;36(4): 337–343. doi: 10.1007/s11282-019-00409-x
Hu Z, Cao D, Hu Y, et al. Diagnosis of in vivo vertical root fracture using deep learning on cone-beam CT images. BMC Oral Health. 2022;22(1): 382. doi: 10.1186/s12903-022-02422-9
Saghiri MA, Asgar K, Boukani KK, et al. A new approach for locating the minor apical foramen using an artificial neural network. International Endodontic Journal. 2012;45(3): 257–265. doi: 10.1111/j.1365-2591.2011.01970.x
Saghiri MA, Garcia-Godoy F, Gutmann JL, et al. The reliability of artificial neural network in locating minor apical foramen: A cadaver study. Journal of Endodontics. 2012;38(8): 1130–1134. doi: 10.1016/j.joen.2012.05.004
Campo L, Aliaga IJ, De Paz JF, et al. Retreatment Predictions in Odontology by means of CBR Systems. Computational Intelligence and Neuroscience. 2016;2016: 7485250. doi: 10.1155/2016/7485250
Buyuk C, Arican Alpay B, Er F. Detection of the separated root canal instrument on panoramic radiograph: a comparison of LSTM and CNN deep learning methods. Dentomaxillofacial Radiology, 2023;52(3): 20220209. doi: 10.1259/dmfr.20220209
Özbay Y, Kazangirler BY, Özcan C, et al. Detection of the separated endodontic instrument on periapical radiographs using a deep learning-based convolutional neural network algorithm. Australian Endodontic Journal. 2023;50: 131–139. doi: 10.1111/aej.12822
Alexander B, John S. Artificial intelligence in dentistry: current concepts and a peep into the future. International Journal of Advanced Research. 2018;6(12): 1105–1108. doi: 10.21474/IJAR01/8242.
Asiri AF, Altuwalah AS. The role of neural artificial intelligence for diagnosis and treatment planning in endodontics: A qualitative review. Saudi Dental Journal. 2022;34(4): 270-281. doi: 10.1016/j.sdentj.2022.04.004.
Das S. Artificial intelligence in Endodontics: A peek into the future. RGUHS Journal of Dental Sciences. 2022;14(3): 35-37. doi: 10.26715/rjds.14_3_7
Chen YW, Stanley K, Att W. Artificial intelligence in dentistry: current applications and future perspectives. Quintessence International (Berlin, Germany : 1985). 2020;51(3): 248-257. doi: 10.3290/j.qi.a43952.
