Journal of Dental Implant Research 2020; 39(4): 48-52  https://doi.org/10.54527/jdir.2020.39.4.48
Application of artificial intelligence in the identification of dental implant systems: a literature review
Ho-Kyung Lim , Yeh-Jin Kwon, Eui-Seok Lee
Department of Oral and Maxillofacial Surgery, Korea University Guro Hospital, Seoul, Korea
Correspondence to: Ho-Kyung Lim, Department of Oral and Maxillofacial Surgery, Korea University Guro Hospital, 148 Gurodong-ro, Guro-gu, Seoul 08308, Korea. Tel: +82-2-2626-1520, Fax: +82-2-837-6245, E-mail: ungassi@naver.com
Received: November 11, 2020; Revised: November 16, 2020; Accepted: November 17, 2020; Published online: December 30, 2020.
© The Korean Academy of Implant Dentistry. All rights reserved.

This is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Artificial intelligence (AI) can be used across multiple disciplines and has also found application in the dental field. Although there have not been many studies on AI-related to dental implants, identification of implant systems in radiographic images using AI is being actively researched. The purpose of this study was to review articles related to the application of AI for the identification of dental implant systems. A systematic review was conducted using the Pubmed and Scopus databases to identify English language articles about AI and dental implant systems. Factors such as implant systems, data modality, sample size, training time, validation method, AI method, accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were extracted from the 4 selected articles. All the surveyed research adopted pre-trained convolutional neural networks as the AI method. The accuracy of AI in finding the correct implant system ranged from 51 to 99.5 percent.
Keywords: Artificial intelligence, Machine learning, Deep learning, Dental implant
INTRODUCTION

The concept of a strong artificial intelligence (AI) which is capable of mimicking the intelligence of humans has led to, the application of weak AI capable of answering specific questions or solving some disputes1). The concept of weak AI explores different methods for constructing algorithms which learns from a set of data and make predictions. Machine learning aids in developing algorithms supervised by data2). Neural network (NN) was the earliest types of AI to be built consisting of nodes and weights2). The training data attributes influences the computing skills of the network, allowing it to update the connections weight2). A simple network structure involving a few layers is called a “shallow” learning neural networks, while a “deep” learning neural network structure involves a plentiful and large layers3). Convolutional neural network (CNN) which is a deep learning structure, can be used for extracting many features from abstract filter layers. CNN are commonly useful for processing large and complex figures4). The progression made in self-learning back-propagation algorithms has accelerated deep learning by refining the outcome from the data, thereby increasing the computing capability. The advancements in AI today have made it possible to be implemented for solving real-life problems and is applicable in various fields of study5).

Since dental implants were first introduced in the 1960s, they have now become the standard treatment method for replacing edentulous area. There are many implant manufacturers, and there are subtle differences in treatment protocols for each company. Jokstad et al. reported that approximately 220 implant brands from 80 companies exist worldwide6). If mechanical complications such as screw loosening or screw fracture occur in the implant, accessories provided by the manufacturer should be used to overcome the complications. However, it may be difficult for certain dentists to identify new implant systems simply by only viewing the images of the fixtures in radiographs. Therefore, radiographic identification of implants is especially important to provide appropriate diagnoses and treatments to patients.

For this reason, research to identify implant systems on radiographic images through AI is currently being actively conducted. In this article, papers about AI applied to find and detect dental implant system using dental plain radiography were reviewed.

MATERIALS AND METHODS

1. Search strategy

In PubMed and Scopus, a search was performed for ‘Machine learning OR deep learning OR neural network’ and ‘dental implants AND (identification OR detection OR classification)’ until September 2020, and 7, 7, and 9 search results were obtained, respectively. A total of 4 peer-reviewed papers were obtained by removing articles not written in English, those focusing on radiological artifact, papers not related to dental implant, as well as reviews, editorials, and letters.

2. Data extraction

The following items were investigated within each paper; Author and year, Implant system, Data modality, Sample Size, Training time, Validation method, AI method, Accuracy, Sensitivity, Specificity, and Area under the receiver operating characteristic curve (AUC).

Among the items listed, Accuracy, Sensitivity, Specificity, AUC are defined as follows.

Accuracy(%)=TP+TNTP+FP+FN+TN×100

Sensitivity(%)=TPTP+FN×100

Specificity(%)=TNTN+FP×100

TP: True positive, TN: True negative, FP: False positive, FN: False negative.

ROC curve: a curve that visualizes how the evaluation value changes according to the change of each threshold after placing the sensitivity on the y-axis and (1-specificity) on the x-axis.

AUC: The value by obtaining the area of the lower area of the ROC curve has a value between 0~1, and a higher value means better classification performance.

RESULTS

The summary of the data derived from the selected papers is given in Table 1.

Table 1 . Summary of artificial intelligence articles in identification of dental implants system

Author and yearImplant systemData modalitySample sizeTrainingValidation methodAI methodAccuracy (%)Sensitivity (%)Specificity (%)AUC
Lee et al.24) 20203 types (Osstem TSIII, Dentium Superline, Straumann Bone level)Panorama, periapical107701000 epoch10-fold cross-validationPretrained CNN (GoogLeNet Inception-v3)99.595.397.60.971
Takahashi et al.25) 20206 types (Nobel Biocare MK III/IIIG/MKIV/SG, Starumann Bone level, GC Genesio)Panorama12821000 epochPretrained CNN (YOLO)51~8550~820.72
Kim et al.26) 20204 types (Nobel Biocare MK TiUnite, Dentium Implantium, Straumann Bone level/Tissue level)Periapical801500 epochk-fold cross-validationPretrained CNN (SqueezeNet, GoogLeNet, ResNet-18, MobileNet-v2, and ResNet-50)93~9892~9894~98
Sukegawa et al.27) 202011 types (Zimmer Full Osseotite, Dentsply Astra EV/TX/Microthread, Nobel Biocare MKIII/SG/CC, Kyocera Finesia, Straumann Tissue level)Panorama8859700 epoch4-fold cross-validationPretrained CNN (VGG16 and VGG19)86.0~93.584.2~92.880.2~90.70.958~1.000


In all studies, CNN was adopted and used as a main network component. The size of the data set was different for each paper, and it ranged from 801 to 10770. In order to maximize the learning effect of the computer, the number of original data was replicated and increased through cross-validation. Then, deep learning was conducted on more replicated data. In the papers, the frequency of learning was set between 500 and 1000 times. Most of them used pretrained networks such as Alexnet, VGG, GoogLeNet, and Inception in existing servers. For radiographs, two-dimensional images such as panorama or periapical view were used alone or in combination. In the case of implant systems, global-market-leading products such as Straumann and Nobel Biocare were commonly investigated in all paper. Additionally, Osstem and Dentium's products were investigated in a Korean author's paper, and Zimmer, Astra, GC, and Kyocera's products were investigated in a Japanese author's paper. The accuracy of AI was 51∼99.5%, and the AUC ranged from 0.72∼1.00.

DISCUSSION

AI have been studied in various fields within dentistry. Most of them are machine or deep learning using radiographic images or clinical pictures. In the field of oral and maxillofacial surgery, studies have been reported to classify tongue muscles or to provide anatomical guides for segmenting the mandibular canal using AI7,8). In addition, studies to diagnose oral cancer using tissue slides or cytology images have been reported9). There was also a study analyzing the accuracy of automatic diagnosis using ultrasonography in patients with Sjogren syndrome10). Additionally, there have been studies on the correlation between TMD and TMJ changes on CBCT or MRI11). As for orthodontics, studies on automating measurement points in lateral cephalometry or cone-beam CT have been reported12-14). On top of that, studies have been reported to discriminate the dentition on which orthodontic treatment was performed, or to analyze the relationship between age and face15,16). In cariology, studies to detect dental caries from radiographic or infrared images have been conducted17,18). Also, studies to find normal root morphology, root fracture, and periapical pathogen have also been reported19-21). In prosthodontics, there have been studies to distinguish between restorations and normal teeth in clinical pictures, or to predict the color tone of porcelain powder or composite resin22,23).

Researches on dental implants using AI had yet to be conducted only on the determining the implant system. The reason that only studies on implant systems have been conducted is thought that AI research using radiographic images or clinical images is relatively easy to perform. However, clinicians' desperate need for grasping the implant system is probably the main reason. When a patient has undergone an implant procedure at another hospital, or when the clinical information is not available because of an implant treatment long ago, clinicians must infer the implant system only by looking at radiographs. As mentioned in the introduction, when prosthetic complications of implants occur, clinicians need to know the implant system to solve the complications. Considering these issues, an AI based approach seems to be a potentially suitable solution to solve that problem, and most studies were conducted to focus on developing an automated identification system of implants from radiographic images using object detection.

The accuracy of AI was 51∼99.5%, and a large difference in accuracy was observed between literatures. Takahashi et al.'s research showed relatively low accuracy of 51∼85%, while other papers had an accuracy of 90% or more, and Kim's research showed that the accuracy was 93∼98% even though the sample size was only 80124-27). Presumably, this is likely to be the effect of sample size replication by cross-validation. In the study of Takahashi et al., the content of cross-validation was not mentioned25). Even though the sample size was not small, as a result, accuracy may be low because only a small scale of learning was performed.

Currently, the areas that AI must overcome are largely classified into four categories. The first is data acquisition. Establishing an open-access standard data set will play a more important role since the sample size is ultimately directly related to accuracy28). The second is interpretability. Data-based AI cannot explain the decision-making process because it uses a purely computational method, so it needs to be supplemented to compensate mistake29). The third is Computing Power. As the AI model becomes more complex, the output speed depends on the computing power of the computer, so the advancement of hardware must be accompanied30). The last is Ethical consideration. Since AI does not imply standards on human ethics, development with ethical issues must be done for symbiosis with humans31).

Contemporary AI excels in utilizing formalized knowledge and extracting information from massive data sets. However, it is hard to involve human emotion in its algorithm. AI should be viewed as an augmentation tool and, at times, relieve dentists so that they can perform more valued tasks, such as integrating emotional sympathy of patient and improving dentist-patient interactions32). In dental implantology, where humans are currently in charge of a large part, it is expected that more development will be achieved through the help of AI in the near future.

CONFLICT OF INTEREST

No potential conflict of interest relevant to this article was reported.

References
  1. Park WJ, Park JB. History and application of artificial neural networks in dentistry. Eur J Dent 2018;12:594-601.
    Pubmed KoreaMed CrossRef
  2. Mupparapu M, Wu CW, Chen YC. Artificial intelligence, machine learning, neural networks, and deep learning: futuristic concepts for new dental diagnosis. Quintessence Int 2018;49:687-8.
  3. 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.
    Pubmed KoreaMed CrossRef
  4. Shan T, Tay FR, Gu L. Application of Artificial Intelligence in Dentistry. J Dent Res :22034520969115.
    Pubmed CrossRef
  5. Shahid N, Rappon T, Berta W. Applications of artificial neural networks in health care organizational decision-making: a scoping review. PLoS One 2019;14:e0212356.
    Pubmed KoreaMed CrossRef
  6. Jokstad A, Braegger U, Brunski JB, Carr AB, Naert I, Wennerberg A. Quality of dental implants. Int Dent J 2003;53(6 Suppl 2):409-43.
    Pubmed CrossRef
  7. Alabi RO, Elmusrati M, Sawazaki-Calone I, Kowalski LP, Haglund C, Coletta RD, et al. Comparison of supervised machine learning classification techniques in prediction of locoregional recurrences in early oral tongue cancer. Int J Med Inform 2020;136:104068.
    Pubmed CrossRef
  8. Gerlach NL, Meijer GJ, Kroon DJ, Bronkhorst EM, Berge SJ, Maal TJ. Evaluation of the potential of automatic segmentation of the mandibular canal using cone-beam computed tomography. Br J Oral Maxillofac Surg 2014;52:838-44.
    Pubmed CrossRef
  9. Sunny S, Baby A, James BL, Balaji D, N VA, Rana MH, et al. A smart tele-cytology point-of-care platform for oral cancer screening. PLoS One 2019;14:e0224885.
    Pubmed KoreaMed CrossRef
  10. Kise Y, Shimizu M, Ikeda H, Fujii T, Kuwada C, Nishiyama M, et al. Usefulness of a deep learning system for diagnosing Sjogren's syndrome using ultrasonography images. Dentomaxillofac Radiol 2020;49:20190348.
    Pubmed KoreaMed CrossRef
  11. Shoukri B, Prieto JC, Ruellas A, Yatabe M, Sugai J, Styner M, et al. Minimally invasive approach for diagnosing TMJ osteoarthritis. J Dent Res 2019;98:1103-11.
    Pubmed KoreaMed CrossRef
  12. Kok H, Acilar AM, Izgi MS. Usage and comparison of artificial intelligence algorithms for determination of growth and development by cervical vertebrae stages in orthodontics. Prog Orthod 2019;20:41.
    Pubmed KoreaMed CrossRef
  13. 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. Dentomaxillofac Radiol 2020;49:20190441.
    Pubmed KoreaMed CrossRef
  14. Gupta A, Kharbanda OP, Sardana V, Balachandran R, Sardana HK. A knowledge-based algorithm for automatic detection of cephalometric landmarks on CBCT images. Int J Comput Assist Radiol Surg 2015;10:1737-52.
    Pubmed CrossRef
  15. Patcas R, Bernini DAJ, Volokitin A, Agustsson E, Rothe R, Timofte R. Applying artificial intelligence to assess the impact of orthognathic treatment on facial attractiveness and estimated age. Int J Oral Maxillofac Surg 2019;48:77-83.
    Pubmed CrossRef
  16. Wang X, Cai B, Cao Y, Zhou C, Yang L, Liu R, et al. Objective method for evaluating orthodontic treatment from the lay perspective: An eye-tracking study. Am J Orthod Dentofacial Orthop 2016;150:601-10.
    Pubmed CrossRef
  17. Casalegno F, Newton T, Daher R, Abdelaziz M, Lodi-Rizzini A, Schurmann F, et al. Caries detection with near-infrared transillumination using deep learning. J Dent Res 2019;98:1227-33.
    Pubmed KoreaMed CrossRef
  18. Schwendicke F, Elhennawy K, Paris S, Friebertshauser P, Krois J. Deep learning for caries lesion detection in near-infrared light transillumination images: a pilot study. J Dent 2020;92:103260.
    Pubmed CrossRef
  19. Setzer 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.
    Pubmed CrossRef
  20. Orhan K, Bayrakdar IS, Ezhov M, Kravtsov A, Ozyurek T. Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans. Int Endod J 2020;53:680-9.
    Pubmed CrossRef
  21. 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.
    Pubmed KoreaMed CrossRef
  22. Yamaguchi S, Lee C, Karaer O, Ban S, Mine A, Imazato S. Predicting the debonding of CAD/CAM composite resin crowns with AI. J Dent Res 2019;98:1234-8.
    Pubmed CrossRef
  23. Yoneda M, Yamada K, Izumi T, Matsuzaki E, Maruta M, Hatakeyama J, et al. Development of an error-detection examination for conservative dentistry education. Clin Exp Dent Res 2020;6:69-74.
    Pubmed KoreaMed CrossRef
  24. 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.
    Pubmed KoreaMed CrossRef
  25. 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.
    Pubmed KoreaMed CrossRef
  26. Kim JE, Nam NE, Shim JS, Jung YH, Cho BH, Hwang JJ. Transfer learning via deep neural networks for implant fixture system classification using periapical radiographs. J Clin Med 2020;9.
    Pubmed KoreaMed CrossRef
  27. Sukegawa S, Yoshii K, Hara T, Yamashita K, Nakano K, Yamamoto N, et al. Deep neural networks for dental implant system classification. Biomolecules 2020;10.
    Pubmed KoreaMed CrossRef
  28. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts H. Artificial intelligence in radiology. Nat Rev Cancer 2018;18:500-10.
    Pubmed KoreaMed CrossRef
  29. Kulikowski CA. Beginnings of artificial intelligence in medicine (aim): computational artifice assisting scientific inquiry and clinical art - with reflections on present aim challenges. Yearb Med Inform 2019;28:249-56.
    Pubmed KoreaMed CrossRef
  30. Fabrizio A, Meyer B, Fabregat R, Corminboeuf C. Quantum chemistry meets machine learning. Chimia (Aarau) 2019;73:983-9.
    Pubmed CrossRef
  31. Currie G, Hawk KE, Rohren EM. Ethical principles for the application of artificial intelligence (AI) in nuclear medicine. Eur J Nucl Med Mol Imaging 2020;47:748-52.
    Pubmed CrossRef
  32. Recht M, Bryan RN. Artificial intelligence: threat or boon to radiologists? J Am Coll Radiol 2017;14:1476-80.
    Pubmed CrossRef


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