Çocuk Diş Sağlık Hizmetlerinde Yapay Zekanın Kullanımı

Yıl 2024, Cilt: 7 Sayı: 3, 53 - 61, 28.12.2024

Özlem Beren Satılmış

https://doi.org/10.51536/tusbad.1589543

Öz

Son yıllarda, yapay zeka (YZ) hızlı bir şekilde gelişmekte ve hayatın çoğu alanına entegre olmaktadır. Geleneksel olarak uzmanlarına bırakılmasının en doğru olduğu düşünülen tıp ve diş hekimliğinde de hızla yerini almaktadır. Birçok veriyi hızlı ve doğru bir şekilde analiz etmesi diş hekimlerine tanı ve tedaviye karar verme süreçlerinde avantaj sağlamaktadır. Çocuk diş hekimliğinde de ağız sağlığı ve hijyeninin değerlendirilmesinde kullanılan anketlerde, diş çürüklerinin tespitinde, panoramik fimlerde anatomik oluşumların tespit edilmesinde, kronolojik yaş tespiti gibi birçok alanda yer almaya başlamıştır. Bu makalede çocuk diş hekimliğinde tamamlayıcı bir yardımcı olarak yapay zekanın klinikte ne şekilde kullanıldığını değerlendirmek amaçlanmıştır.

Anahtar Kelimeler

Çocuk diş hekimliği, Derin öğrenme, Makine öğrenmesi, Yapay zeka

Using Artificial Intelligence in Pediatric Dental Healthcare Services

Öz

In recent years, artificial intelligence (AI) has been rapidly developing and integrating into most areas of life It is rapidly taking its place in medicine and dentistry, traditionally thought to be best left to specialists. Analyzing many data quickly and accurately gives dentists an advantage in diagnosis and treatment decision-making processes. In pediatric dental health services, it has started to take place in many areas such as questionnaires used in the evaluation of oral health and hygiene, detection of dental caries, detection of anatomical landmark in panoramic film and chronological age determination. This study aims to evaluate the use of artificial intelligence as a complementary aid in pediatric dentistry.

Anahtar Kelimeler

Artificial intelligence, Deep learning, Machine learning, Pediatric dentistry

Kaynakça

• Çetin B. Sağlık Hizmetleri ve Yapay Zeka. Uluslararası Ekonomi ve Siyaset Bilimleri Akademik Araştırmalar Dergisi. 2023;7(17):53-67.
• Kong S-C, Cheung WM-Y, Zhang G. Evaluating artificial intelligence literacy courses for fostering conceptual learning, literacy and empowerment in university students: Refocusing to conceptual building. Computers in Human Behavior Reports. 2022;7:100223.
• Hutson M. AI Glossary: Artificial intelligence, in so many words. Science. 2017;357(6346):19.
• Bonny T, Al Nassan W, Obaideen K, Al Mallahi MN, Mohammad Y, El-Damanhoury HM. Contemporary Role and Applications of Artificial Intelligence in Dentistry. F1000Res. 2023;12:1179.
• Schwendicke F, Samek W, Krois J. Artificial Intelligence in Dentistry: Chances and Challenges. J Dent Res. 2020;99(7):769-74.
• Alessa N. Application of Artificial Intelligence in Pediatric Dentistry: A Literature Review. J Pharm Bioallied Sci. 2024;16(Suppl 3):S1938-S40.
• Mine Y, Iwamoto Y, Okazaki S, Nakamura K, Takeda S, Peng TY, et al. Detecting the presence of supernumerary teeth during the early mixed dentition stage using deep learning algorithms: A pilot study. Int J Paediatr Dent. 2022;32(5):678-85.
• Bunyarit SS, Nambiar P, Naidu MK, Ying RPY, Asif MK. Dental age estimation of Malay children and adolescents: Chaillet and Demirjian's data improved using artificial multilayer perceptron neural network. Pediatric Dent. 2021;31(2):176-85.
• 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 Sci Dent. 2022;52(3):275-81.
• Karhade DS, Roach J, Shrestha P, Simancas-Pallares MA, Ginnis J, Burk ZJS, et al. An Automated Machine Learning Classifier for Early Childhood Caries. Pediatr Dent. 2021;43(3):191-7.
• 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.
• Park YH, Kim SH, Choi YY. Prediction Models of Early Childhood Caries Based on Machine Learning Algorithms. Int J Environ Res Public Health. 2021;18(16).
• Rokhshad R, Zhang P, Mohammad-Rahimi H, Pitchika V, Entezari N, Schwendicke F. Accuracy and consistency of chatbots versus clinicians for answering pediatric dentistry questions: A pilot study. Journal of Dentistry. 2024;144:104938.
• Mahajan K, Kunte SS, Patil KV, Shah PP, Shah RV, Jajoo SS. Artificial Intelligence in Pediatric Dentistry – A Systematic Review. Journal of Dental Research and Review. 2023;10(1).
• Wang Y, Hays RD, Marcus M, Maida CA, Shen J, Xiong D, et al. Developing Children's Oral Health Assessment Toolkits Using Machine Learning Algorithm. JDR Clin Trans Res. 2020;5(3):233-43.
• Joseph B, Prasanth CS, Jayanthi JL, Presanthila J, Subhash N. Detection and quantification of dental plaque based on laser-induced autofluorescence intensity ratio values. J Biomed Opt. 2015;20(4):048001.
• Yan YJ, Wang BW, Yang CM, Wu CY, Ou-Yang M. Autofluorescence Detection Method for Dental Plaque Bacteria Detection and Classification: Example of Porphyromonas gingivalis, Aggregatibacter actinomycetemcomitans, and Streptococcus mutans. Dent J (Basel). 2021;9(7).
• 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):141.
• Vishwanathaiah S, Fageeh HN, Khanagar SB, Maganur PC. Artificial Intelligence Its Uses and Application in Pediatric Dentistry: A Review. Biomedicines. 2023;11(3).
• Gonzalez C, Badr Z, Güngör HC, Han S, Hamdan MD. Identifying Primary Proximal Caries Lesions in Pediatric Patients From Bitewing Radiographs Using Artificial Intelligence. Pediatr Dent. 2024;46(5):332-6.
• Talpur S, Azim F, Rashid M, Syed SA, Talpur BA, Khan SJ. Uses of Different Machine Learning Algorithms for Diagnosis of Dental Caries. J Healthc Eng. 2022;2022:5032435.
• Pang L, Wang K, Tao Y, Zhi Q, Zhang J, Lin H. A New Model for Caries Risk Prediction in Teenagers Using a Machine Learning Algorithm Based on Environmental and Genetic Factors. Front Genet. 2021;12:636867.
• Ramos-Gomez F, Marcus M, Maida CA, Wang Y, Kinsler JJ, Xiong D, et al. Using a Machine Learning Algorithm to Predict the Likelihood of Presence of Dental Caries among Children Aged 2 to 7. Dent J (Basel). 2021;9(12).
• Schlickenrieder A, Meyer O, Schonewolf J, Engels P, Hickel R, Gruhn V, et al. Automatized Detection and Categorization of Fissure Sealants from Intraoral Digital Photographs Using Artificial Intelligence. Diagnostics (Basel). 2021;11(9).
• 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. Dentomaxillofac Radiol. 2021;50(6):20200172.
• Caliskan S, Tuloglu N, Celik O, Ozdemir C, Kizilaslan S, Bayrak S. A pilot study of a deep learning approach to submerged primary tooth classification and detection. Int J Comput Dent. 2021;24(1):1-9.
• Maganur PC, Vishwanathaiah S, Mashyakhy M, Abumelha AS, Robaian A, Almohareb T, et al. Development of Artificial Intelligence Models for Tooth Numbering and Detection: A Systematic Review. Int Dent J. 2024;74(5):917-29.
• 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. Int J Paediatr Dent. 2022;32(6):785-92.
• Liu J, Liu Y, Li S, Ying S, Zheng L, Zhao Z. Artificial intelligence-aided detection of ectopic eruption of maxillary first molars based on panoramic radiographs. J Dent. 2022;125:104239.
• Ahn Y, Hwang JJ, Jung YH, Jeong T, Shin J. Automated Mesiodens Classification System Using Deep Learning on Panoramic Radiographs of Children. Diagnostics (Basel). 2021;11(8).
• Kuwada C, Ariji Y, Fukuda M, Kise Y, Fujita H, Katsumata A, et al. Deep learning systems for detecting and classifying the presence of impacted supernumerary teeth in the maxillary incisor region on panoramic radiographs. Oral Surg Oral Med Oral Pathol Oral Radiol. 2020;130(4):464-9.
• Ha E-G, Jeon KJ, Kim YH, Kim J-Y, Han S-S. Automatic detection of mesiodens on panoramic radiographs using artificial intelligence. Scientific Reports. 2021;11(1):23061.
• Kim J, Hwang JJ, Jeong T, Cho BH, Shin J. Deep learning-based identification of mesiodens using automatic maxillary anterior region estimation in panoramic radiography of children. Dentomaxillofac Radiol. 2022;51(7):20210528.
• Mladenovic R, Kalevski K, Davidovic B, Jankovic S, Todorovic VS, Vasovic M. The Role of Artificial Intelligence in the Accurate Diagnosis and Treatment Planning of Non-Syndromic Supernumerary Teeth: A Case Report in a Six-Year-Old Boy. Children (Basel). 2023;10(5).
• Alevizakos V, Bekes K, Steffen R, von See C. Artificial intelligence system for training diagnosis and differentiation with molar incisor hypomineralization (MIH) and similar pathologies. Clin Oral Investig. 2022;26(12):6917-23.
• Zaborowicz K, Biedziak B, Olszewska A, Zaborowicz M. Tooth and Bone Parameters in the Assessment of the Chronological Age of Children and Adolescents Using Neural Modelling Methods. Sensors (Basel). 2021;21(18).
• Bunyarit SS, Jayaraman J, Naidu MK, Yuen Ying RP, Nambiar P, Asif MK. Dental age estimation of Malaysian Chinese children and adolescents: Chaillet and Demirjian’s method revisited using artificial multilayer perceptron neural network. Australian Journal of Forensic Sciences. 2020;52(6):681-98.
• Zaborowicz M, Zaborowicz K, Biedziak B, Garbowski T. Deep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone Parameters. Sensors (Basel). 2022;22(2).
• Lee Y-H, Won JH, Auh QS, Noh Y-K. Age group prediction with panoramic radiomorphometric parameters using machine learning algorithms. Scientific Reports. 2022;12(1):11703.
• Lee JS, Adhikari S, Liu L, Jeong HG, Kim H, Yoon SJ. Osteoporosis detection in panoramic radiographs using a deep convolutional neural network-based computer-assisted diagnosis system: a preliminary study. Dentomaxillofac Radiol. 2019;48(1):20170344.
• 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 Radiol. 2019;35(3):301-7.
• Corbella S, Srinivas S, Cabitza F. Applications of deep learning in dentistry. Oral Surg Oral Med Oral Pathol Oral Radiol. 2021;132(2):225-38.
• Bag I, Bilgir E, Bayrakdar IS, Baydar O, Atak FM, Celik O, et al. An artificial intelligence study: automatic description of anatomic landmarks on panoramic radiographs in the pediatric population. BMC Oral Health. 2023;23(1):764.
• Perschbacher S. Interpretation of panoramic radiographs. Australian Dental Journal. 2012;57(s1):40-5.
• 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. J Dent Sci. 2021;16(1):508-22.
• Cunningham A, McPolin O, Fallis R, Coyle C, Best P, McKenna G. A systematic review of the use of virtual reality or dental smartphone applications as interventions for management of paediatric dental anxiety. BMC Oral Health. 2021;21(1):244.
• Acharya S, Godhi BS, Saxena V, Assiry AA, Alessa NA, Dawasaz AA, et al. Role of artificial intelligence in behavior management of pediatric dental patients-a mini review. J Clin Pediatr Dent. 2024;48(3):24-30.