DENTOMAKSİLLOFASİYAL RADYOLOJİDE YAPAY ZEKA UYGULAMALARININ ROLÜ: BÖLÜM 1

Yıl 2022, , 713 - 720, 24.08.2022

Elif Şener , Güniz Baksi Şen

https://doi.org/10.15311/selcukdentj.853884

Öz

Günümüzde bilgisayar destekli görüntüleme teknikleri ve görüntü analiz yöntemlerinin kullanımının hızlanması; konvansiyonel hasta tedavi yaklaşımını da değiştirmiştir. Gerek medikal gerekse dental tanı ve tedavi planlamasında yararlanılan yapay zeka uygulamaları; hayatımızın her alanında olduğu gibi dental alanda da devrim yaratmıştır. İnsan zekasını taklit eden ve topladıkları bilgilere göre yinelemeli olarak kendilerini geliştirebilen sistemler anlamına gelen yapay zeka; hız artışı, yüksek başarı, düşük maliyet, ulaşılabilirlik ve işlerde optimizasyon gibi birçok avantajı ile günümüzde sağlık alanında giderek daha sıklıkla kullanılır hale gelmektedir. Bundan en çok etkilenecek disiplinlerin başında temel tanıya destek olan ve diğer tüm disiplinlere tanısal açıdan destek veren radyolojidir. Bu derlemenin amacı; dişhekimliğinin farklı disiplinlerinde radyolojik verilerden yararlanılarak gerçekleştirilen yapay zeka uygulamalarının tanı ve tedavi aşamalarındaki avantaj, dezavantaj ve sınırlıklarını tartışmaktır.

Anahtar Kelimeler

dijital görüntüleme, dentomaksillofasiyal radyoloji, yapay zeka, dental

ROLE OF ARTIFICIAL INTELLIGENCE APPLICATIONS IN DENTOMAXILLOFACIAL RADIOLOGY: PART 1

Öz

Nowadays, the acceleration of the use of computer-aided imaging techniques and image analysis methods has also changed the conventional patient treatment approach. Artificial intelligence applications utilized in both medical and dental diagnosis and treatment planning has revolutionized the dental field, as in all areas of our lives. Artificial intelligence, system imitating human intelligence and improving itself recursively according to the collected information, is more often used in healthcare with many advantages such as speed increase, high success, low cost, accessibility and optimization in work. One of the disciplines that will be most affected by this development is radiology, which supports basic diagnosis and provides diagnostic support to all other medical disciplines. The purpose of this review is to discuss the advantages, disadvantages and limitations of artificial intelligence applications realized using radiological data for diagnosis and treatment stages in different disciplines of dentistry.

Anahtar Kelimeler

digital imaging, dentomaxillofacial radiology, artificial intelligence, dental,

Kaynakça

• 1. Hung K, Yeung AWK, Tanaka R, Bornstein MM. Current applications, opportunities, and limitations of aı for 3D imaging in dental research and practice. Int J Environ Res Public Health 2020;17:4424.
• 2. Gandomi A, Haider M. Beyond the hype: Big data concepts, methods, and analytics. Int J Inf Manag 2015;35:137–44.
• 3. Jiang, F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol 2017;2:230–43.
• 4. King BF Jr. Artificial intelligence and radiology: what will the future hold? J Am Coll Radiol 2018;15:501-3.
• 5. Ferizi U, Besser H, Hysi P, Jacobs J, Rajapakse CS, Chen C, et al. Artificial intelligence applied to osteoporosis: a performance comparison of machine learning algorithms in predicting fragility fractures from MRI data. J Magn Reson Imaging 2019;49:1029-38.
• 6. Schuhbaeck A, Otaki Y, Achenbach S, Schneider C, Slomka P, Berman DS, et al. Coronary calcium scoring from contrast coronary CT angiography using a semiautomated standardized method. J Cardiovasc Comput Tomogr. 2015;9:446-53.
• 7. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;542:115-18.
• 8. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer 2018;18:500-10.
• 9. Abdolali F, Zoroofi RA, Otake Y, Sato Y. Automatic segmentation of maxillofacial cysts in cone beam CT images. Comput Biol Med 2016;72:108-19.
• 10. Lee JH, Kim DH, Jeong SN, Choi SH. Diagnosis and prediction of periodontally compromised teeth using a deep learning based convolutional neural network algorithm. J Periodontal Implant Sci 2018;48:114–23.
• 11. Lee JH, Kim DH, Jeong SN, Choi SH. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent 2018;77:106–11.
• 12. Seltzer FC, Shi KJ, Zhang Z, Yan H, Yoon H, Mupparapu M, et al. Artificial intelligence for the computer-aided detection of periapical lesions in cone-beam computed tomographic images. J Endod. 2020;46:987-93.
• 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.
• 14. Leite AF, Vasconcelos KF, Willems H, Jacobs R. Radiomics and machine learning in oral healthcare. Proteomics Clin Appl 2020;14:e1900040.
• 15. Hwang JJ, Jung YH, Cho BH, Heo MS. An overview of deep learning in the field of dentistry. Imaging Sci Dent 2019; 49:1-7.
• 16. Kaya U, Yılmaz A, Dikmen Y. Deep learning methods used in the field of health. European Journal of Science and Technology 2019;16:792-808.
• 17. Yilmaz E, Kayikcioglu T, Kayipmaz S. Computer-aided diagnosis of periapical cyst and keratocystic odontogenic tumor on cone beam computed tomography. Comput Methods Programs Biomed 2017;146:91-100.
• 18. Abdolali F, Zoroofi RA, Otake Y, Sato Y. Automated classification of maxillofacial cysts in cone beam CT images using contourlet transformation and spherical harmonics. Comput Methods Programs Biomed 2017;139:197-207.
• 19. Mikulka J, Gescheidtova E, Kabrda M, Perina V. Classification of jaw bone cysts and necrosis via the processing of orthopantomograms. Radioengineering 2013;22:114–22.
• 20. Nurtanio I, Astuti ER, Ketut Eddy Pumama I, Hariadi M, Purnomo MH. Classifying cyst and tumor lesion using support vector machine based on dental panoramic images texture features. IAENG Int J Comput Sci 2013;40:29–37.
• 21. Rana M, Modrow D, Keuchel J, Chui C, Rana M, Wagner M, et al. Development and evaluation of an automatic tumor segmentation tool: a comparison between automatic, semi-automatic and manual segmentation of mandibular odontogenic cysts and tumors. J Craniomaxillofac Surg 2015;43:355-59.
• 22. 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 Dis. 2020;26:152-58.
• 23. Poedjiastoeti W, Suebnukarn S. Application of convolutional neural network in the diagnosis of jaw tumors. Healthc Inform Res 2018;24:236-241.
• 24. Nicolielo LFP, Van Dessel J, van Lenthe GH, Lambrichts I, Jacobs R. Computer-based automatic classification of trabecular bone pattern can assist radiographic bone quality assessment at dental implant site. Br J Radiol 2018;91:20180437.
• 25. Lee JH, Kim YT, Lee JB, Jeong SN. A performance comparison between automated deep learning and dental professionals in classification of dental implant systems from dental imaging: a multi-center study. Diagnostics (Basel) 2020;10:910.
• 26. Lee JH, Jeong SN. 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 (Baltimore) 2020;99:e20787.
• 27. Takahashi T, Nozaki K, Gonda T, Mameno T, Wada M, Ikebe K. Identification of dental implants using deep learning-pilot study. Int J Implant Dent 2020;6:53.
• 28. Jaskari J, Sahlsten J, Järnstedt J, Mehtonen H, Karhu K, Sundqvist O, et al. Deep learning method for mandibular canal segmentation in dental cone beam computed tomography volumes. Sci Rep 2020;10:5842.
• 29. Vinayahalingam S, Xi T, Bergé S, Maal T, de Jong G. Automated detection of third molars and mandibular nerve by deep learning. Sci Rep 2019;9:9007.
• 30. Fukuda M, Ariji Y, Kise Y, Nozawa M, Kuwada C, Funakoshi T, et al. Comparison of 3 deep learning neural networks for classifying the relationship between the mandibular third molar and the mandibular canal on panoramic radiographs. Oral Surg Oral Med Oral Pathol Oral Radiol 2020;130:336-43.
• 31. Kwak GH, Kwak EJ, Song JM, Park HR, Jung YH, Cho BH, et al. Automatic mandibular canal detection using a deep convolutional neural network. Sci Rep 2020;10:5711.
• 32. Wenzel A. Gold standard for the comparison of the diagnostic accuracy of panoramic images for approximal caries detection. Dentomaxillofac Radiol 2009;38:245.
• 33. Kim DW, Kim H, Nam W, Kim HJ, Cha IH. Machine learning to predict the occurrence of bisphosphonate-related osteonecrosis of the jaw associated with dental extraction: A preliminary report. Bone 2018;116:207-14.
• 34. O'Sullivan S, Leonard S, Holzinger A, Allen C, Battaglia F, Nevejans N, et al. Operational framework and training standard requirements for AI-empowered robotic surgery Int J Med Robot 2020;16:1-13.
• 35. Tarassoli SP. Artificial intelligence, regenerative surgery, robotics? What is realistic for the future of surgery? Ann Med Surg (Lond) 2019;41:53-55.
• 36. Mol A, van der Stelt PF. Application of computer-aided image interpretation to the diagnosis of periapical bone lesions. Dentomaxillofac Radiol 1992;21:190-4.
• 37. Carmody DP, McGrath SP, Dunn SM, van der Stelt PF, Schouten E. Machine classification of dental images with visual search. Acad Radiol 2001;8:1239-46.
• 38. Orhan K, Bayrakdar IS, Ezhov M, Kravtsov A, Özyürek T. Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans. Int Endod J 2020;53:680-89.
• 39. Yang J, Xie Y, Liu L, Xia B, Cao Z, Guo C. Automated dental image analysis by deep learning on small dataset. IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC) 2018; 492-97.
• 40. Okada K, Rysavy S, Flores A, Linguraru MG. Noninvasive differential diagnosis of dental periapical lesions in cone-beam CT scans. Med Phys. 2015;42:1653-65.
• 41. Poswar Fde O, Farias LC, Fraga CA, Bambirra W Jr, Brito-Júnior M, Sousa-Neto MD, et al. Bioinformatics, interaction network analysis, and neural networks to characterize gene expression of radicular cyst and periapical granuloma. J Endod. 2015;41:877-83.
• 42. Saghiri MA, Garcia-Godoy F, Gutmann JL, Lotfi M, Asgar K. The reliability of artificial neural network in locating minor apical foramen: a cadaver study. J Endod 2012;38:1130-4.
• 43. 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. Dentomaxillofac Radiol 2019;48:20180218.
• 44. Benyó B. Identification of dental root canals and their medial line from micro-CT and cone-beam CT records. Biomed Eng Online 2012;11:81.
• 45. 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. Dentomaxillofac Radiol 2017;46:20160107.