Management of Dental Caries in Digital Era, Artificial Intelligence perspective – Scoping Review
Dr.Surbhi Kapoor*, Dr. Vinayam , Dr. Chander Mohan, Dr. Roopika Handa, Dr. Himakshi Kumari, Dr. Prajwal Singh Tomar
Received Date: 27/04/2022; Published Date: 09/05/2022
*Corresponding author: Dr.Surbhi Kapoor, Public Health Dentist, MDS, BDS; email@example.com
Cite this article: Dr. Surbhi Kapoor*, Dr. Vinayam , Dr. Chander Mohan, Dr. Roopika Handa, Dr. Himakshi Kumari, Dr. Prajwal Singh Tomar.Management of Dental Caries in Digital Era, Artificial Intelligence perspective – Scoping Review
Dental Caries is defined as an irreversible microbial disease of the calcified tissue of teeth characterized by demineralization of the inorganic part and destruction of organic substance of the tooth which leads to cavitation . Dental caries is the most prevalent dental disease worldwide. According to Global Burden Diseases 2017, most common health condition in permanent teeth is untreated dental caries . The visual-tactile method is most commonly used technique in diagnosis of dental caries whereas in radiography bitewing radiography is most common . Accuracy of early diagnosis of dental caries is still a challenging problem for dentists. Neural networks and artificial intelligence (AI) are increasingly being used in the field of dentistry. AI technology has a possibility of improving patient care through better diagnostic aids and reduced errors in daily practice .
Artificial intelligence is defined as the theory and development of computer systems that are able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages . Convolutional Neural Network (CNN) is a tool of artificial intelligence for effective analysis of images (Figure 1) . Several limitations exist in healthcare like serious diagnostic errors, mistakes in treatment, an enormous waste of resources, inefficiencies in workflow, inequities, and inadequate time between patients and clinicians [7,8]. Leaders in healthcare and computer scientists have asserted that AI might have a role in addressing all these problems . It has been estimated that, per day, AI would process over 250 million images for the cost of about $1,000 . Artificial intelligence will be used in augmented reality for future remote eHealth [11,12].
Various databases like NCBI (Pubmed), Google Scholar, Web of Science and SCOPUS were comprehensively searched for all research related to artificial intelligence integration in dental caries management from January 2000 till April 2022. The research work included in this review is according to the following inclusion criteria: 1. Full Text Articles in English language 2. Research work focusing on developing artificial intelligence network in dental caries diagnostics and therapeutics. The keywords and MeSH terms (Medical Subject Heading) used were Dental Caries AND Artificial Intelligence”, “Dental Caries Diagnosis AND artificial Intelligence”, “Dental Caries AND Machine Learning”, “Dental Caries Management AND Artificial Intelligence”
The search yielded around 148 articles identified from electronic databases and 41 articles were identified through reference list. After removing the duplicates, the articles were screened based on eligibility criteria. Information was taken from 25 articles
Artificial Intelligence Integrated in Dental Caries Management
AI in dentistry is a growing topic, as its benefits clinicians with a high quality patient care and simplifies complicated protocols by providing a predictable outcome. Its application evolves rapidly day by day . The AI softwares that have been developed in field in dentistry have focused mainly on dental disease diagnostics. Many softwares have been developed and tested in diagnosis of dental caries which will act as an adjunct to Health Care Professionals. Majority of softwares (17 out of 25) were based on Convolutional Neural Network. CNN is useful in a lot of applications, especially in image related tasks. Applications of CNN include image classification, image semantic segmentation, object detection in images, etc. . The AI diagnostic softwares by Hung M et al  and Duong DL et al  were Support Vector Machine based on machine learning algorithms. The power of an SVM stems from its ability to learn data classification patterns with balanced accuracy and reproducibility . An Artificial Intelligence–Powered Smartphone App, AICaries by Xiao J  was the only mobile based application for self-screening for dental caries by people themselves.
Several softwares have been developed to enhance dental Caries diagnosis. The softwares are promising tools and enhance the diagnostic ability of caries detection via dental Xray, bitewing or panoramic. Only one tool was developed which was based on self-screening and dental awareness by the patient himself. All the tools have been developed and tested but none of them have been widely implemented in dental health care centres. The cost effectiveness of these tolls needs to be done before launching these tools in the market.
Recommendations and Conclusion
Dental diseases are highly prevalent in today’s era. In the digital era technology can play a prominent role in dental disease diagnostics, risk factor analysis, prognosis and management. Artificial Intelligence software are helping dentists in accurate diagnosis and efficiently treating their patients. The advancements in artificial intelligence may still be in its nascent stage but in no way can replace human intelligence and skill. Artificial Intelligence can be utilized in various applications in dentistry other than enlisted in Table 1 like modified dental chair with voice assistant, dental education, teleconsultation specially in covid era, modified dental radiography, oral cancer screening by mobile based AI application precision dental prosthesis, 3D scans and aligners, AI based laboratories to design precise dental restorations, apex location or implantology for making precise surgical guides and identifying type of bone to cortical thickness etc.
Table 1: Artificial Intelligence based Dental Caries Management Software.
Affordable dental caries AI based diagnostic tools needs to be developed and validated after which they can launched in the market. Tools should focus on engaging people by giving more importance to self-screening tools. Ai based tools should also be developed that focus on dental caries risk factor analysis and dental caries prognosis. The field of artificial intelligence has grown tremendously in the last decade. While the advances in AI like neural networking, natural language processing, image recognition, and speech recognition have transformed the field of medicine and dentistry in many ways, they have a number of drawbacks and challenges that are yet to be overcome. One of which is the high initial capital equipment costs involved . The culmination of artificial intelligence along with digitization has seen a new era in the field of dentistry and its future aspects appear extremely promising.
- Sivapathasundharam B, Raghu AR (2020) Dental caries. InShafer's Textbook of Oral Pathology: Ninth Edition pp. 369-403. Elsevier.
- (2018) Global regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. 392: 1789–8583.
- Schwendicke F, Tzschoppe M, Paris S (2015) Radiographic caries detection: A systematic review and meta-analysis. J Dent 43: 924–933.
- Putra RH, Doi C, Yoda N, Astuti ER, Sasaki K (2022) Current applications and development of artificial intelligence for digital dental radiography. Dentomaxillofacial Radiology. 51(1): 20210197.
- Fowler F, Fowler H, Thompson D (2000) The pocket Oxford dictionary of current English. Oxford [England]: Oxford University Press, UK.
- Krishna AB, Tanveer A, Bhagirath PV, Gannepalli A (2020) Role of artificial intelligence in diagnostic oral pathology-A modern approach. Journal of Oral and Maxillofacial Pathology: JOMFP 24(1): 152.
- Singh H (2014) The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations. BMJ Qual Saf 23: 727–731.
- Berwick DM, Hackbarth AD (2012) Eliminating waste in US health care. JAMA 307: 1513–1516.
- Topol EJ (2019) High-performance medicine: the convergence of human and artificial intelligence. Nature medicine 25(1): 44
- Beam AL, Kohane IS (2016) Translating artificial intelligence into clinical care. JAMA 316: 2368–2369.
- Wiederhold G, Riva M, Wiederhold (2015) “Virtual reality in healthcare: medical simulation and experiential interface,” Annual Review of Cyber Therapy and Telemedicine 13: 239.
- Bartsch, A. Mitra, S. Mitra, A. Almal, K. Steven, D. Skinner, D. Fry, P. Lenehan, W. Worzel, R. Cote, “Use of artificial intelligence and machine learning algorithms with gene expression profiling to predict recurrent nonmuscle invasive urothelial carcinoma of the bladder,” The Journal of Urology, vol.195, pp.493-498, 2016
- Xiao J, Luo J, Ly-Mapes O, Wu TT, Dye T, Al Jallad N, et al. (2021) Assessing a Smartphone App (AICaries) That Uses Artificial Intelligence to Detect Dental Caries in Children and Provides Interactive Oral Health Education: Protocol for a Design and Usability Testing Study. JMIR research protocols. 10(10): e32921.
- Devito KL, de Souza Barbosa F, Felippe Filho WN (2008) An artificial multilayer perceptron neural network for diagnosis of proximal dental caries. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontology. 106(6): 879-884.
- Hung M, Voss MW, Rosales MN, Li W, Su W, et al. (2019) Application of machine learning for diagnostic prediction of root caries. Gerodontology. 36(4): 395-404.
- Kühnisch J, Meyer O, Hesenius M, Hickel R, Gruhn V (2021) Caries Detection on Intraoral Images Using Artificial Intelligence. Journal of dental research.
- Mertens S, Krois J, Cantu AG, Arsiwala LT, Schwendicke F (2021) Artificial intelligence for caries detection: Randomized trial. Journal of Dentistry. 115: 103849.
- Cantu AG, Gehrung S, Krois J, Chaurasia A, Rossi JG, et al. (2020) Detecting caries lesions of different radiographic extension on bitewings using deep learning. Journal of dentistry 100: 103425.
- Lee JH, Kim DH, Jeong SN, Choi SH (2018) Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. Journal of dentistry. 77: 106-111.
- Kositbowornchai S, Siriteptawee S, Plermkamon S, Bureerat S, Chetchotsak D (2006) An artificial neural network for detection of simulated dental caries. International Journal of Computer Assisted Radiology and Surgery. 1(2): 91-96.
- Devlin H, Williams T, Graham J, Ashley M (2021) The ADEPT study: a comparative study of dentists' ability to detect enamel-only proximal caries in bitewing radiographs with and without the use of Assist Dent artificial intelligence software. British dental journal. 231(8): 481-485.
- Zhang X, Liang Y, Li W, Liu C, Gu D, et al. (2022) Development and evaluation of deep learning for screening dental caries from oral photographs. Oral diseases. 28(1):173-181.
- Li RZ, Zhu JX, Wang YY, Zhao SY, Peng CF, et al. (2021) Development of a deep learning based prototype artificial intelligence system for the detection of dental caries in children. Zhonghua kou qiang yi xue za zhi= Zhonghua kouqiang yixue zazhi= Chinese journal of stomatology. 56(12): 1253-1260.
- Bayraktar Y, Ayan E (2022) Diagnosis of interproximal caries lesions with deep convolutional neural network in digital bitewing radiographs. Clinical oral investigations. 26(1): 623-632.
- Karhade DS, Roach J, Shrestha P, Simancas-Pallares MA, Ginnis J, et al. (2021) An automated machine learning classifier for early childhood caries. Pediatric Dentistry. 43(3): 191-197.
- Salehi HS, Mahdian M, Murshid MM, Judex S, Tadinada A (2019) Deep learning-based quantitative analysis of dental caries using optical coherence tomography: an ex vivo study. InLasers in Dentistry 10857: 39-46.
- Moutselos K, Berdouses E, Oulis C, Maglogiannis I (2019) Recognizing occlusal caries in dental intraoral images using deep learning. In2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) pp. 1617-1620.
- Orhan K, Bayrakdar IS, Ezhov M, Kravtsov A, Özyürek TA (2020) Evaluation of artificial intelligence for detecting periapical pathosis on cone‐beam computed tomography scans. International endodontic journal. 53(5): 680-689.
- Srivastava MM, Kumar P, Pradhan L, Varadarajan S (2017) Detection of tooth caries in bitewing radiographs using deep learning. arXiv preprint Xiv: 1711.07312.
- Megalan Leo L, Kalpalatha Reddy T (2020) Dental caries classification system using deep learning based convolutional neural network. Journal of Computational and Theoretical Nanoscience. 17(9-10): 4660-4665.
- Bayrakdar IS, Orhan K, Akarsu S, Çelik Ö, Atasoy S, et al. (2021) Deep-learning approach for caries detection and segmentation on dental bitewing radiographs. Oral Radiology 22: 1-2.
- De Araujo Faria V, Azimbagirad M, Viani Arruda G, Fernandes Pavoni J, Cezar Felipe J, et al. (2021) Prediction of Radiation-Related Dental Caries Through PyRadiomics Features and Artificial Neural Network on Panoramic Radiography. Journal of Digital Imaging 34(5): 1237-1248.
- Li S, Liu J, Zhou Z, Zhou Z, Wu X, Li Y, Wang S, Liao W, Ying S, Zhao Z. Artificial intelligence for caries and periapical periodontitis detection. Journal of Dentistry. 2022 Mar 24:104107.
- Moran M, Faria M, Giraldi G, Bastos L, Oliveira L, Conci A. Classification of Approximal Caries in Bitewing Radiographs Using Convolutional Neural Networks. Sensors. 2021 Jan;21(15):5192.
- Park YH, Kim SH, Choi YY (2021) Prediction Models of Early Childhood Caries Based on Machine Learning Algorithms. International Journal of Environmental Research and Public Health. 18(16): 8613.
- Vinayahalingam S, Kempers S, Limon L, Deibel D, Maal T, et al. (2021) Classification of caries in third molars on panoramic radiographs using deep learning. Scientific Reports. 11(1): 1-7.
- Duong DL, Nguyen QD, Tong MS, Vu MT, Lim JD, et al. (2021) Proof-of-Concept Study on an Automatic Computational System in Detecting and Classifying Occlusal Caries Lesions from Smartphone Color Images of Unrestored Extracted Teeth. Diagnostics. 11(7): 1136.
- Chen YW, Stanley K, Att W (2020) Artificial intelligence in dentistry: current applications and future perspectives. Quintessence Int. 51(3): 248-257.
- Wu J (2017) Introduction to convolutional neural networks. National Key Lab for Novel Software Technology. Nanjing University. China. 5(23): 495.
- Pisner DA, Schnyer DM (2020) Support vector machine. InMachine learning pp. 101-121.
- Khanna SS, Dhaimade PA (2017) Artificial intelligence: transforming dentistry today. Indian J Basic Appl Med Res. 6(3): 161-167.