Diş Çürüklerinde Yapay Zeka Kullanımının Bibliyometrik Analizi

Abstract

Amaç: Bu çalışmanın amacı, yapay zekanın diş çürüğü teşhisindeki kullanımına yönelik yapılan araştırmaların gelişim trendlerini ve dinamiklerini incelemek, mevcut literatürün güçlü ve zayıf yönlerini belirlemek ve gelecekteki araştırmalara rehberlik etmektir. Gereçler ve Yöntemler: Web of Science veritabanı kullanılarak 3 Haziran 2024'ten önce yayınlanan makaleleri kapsayan bir literatür taraması yapılmıştır. Pilot aramalar yapılarak 883 çalışmaya ulaşıldı. Belirlenen tarama ve filtreleme işlemlerinin ardından çalışma 270 yayın üzerinde gerçekleştirilmiştir. Bibliyometrik analizde görselleştirmeler için Biblioshiny R paketinin yanı sıra Web of Science'ın özellikleri ve VOSviewer yazılımı kullanıldı. Verilerin tablolanması için Microsoft Excel kullanıldı. Bulgular: Her yıl yayımlanan makale sayısında genel bir artış görülmektedir. Kariyolojide yapay zeka kullanımına ilişkin yayınlara toplam 3081 atıf yapılmıştır. Makale başına ortalama atıf sayısı 11,41, H indeksi ise 29 olarak bulunmuştur. En fazla alıntı yapılan ülke Almanya (581 alıntı), en etkili yazar ise Falk Schwendicke olmuştur. Kurumlar bazında ise en yüksek katkıyı Charite University Medicine Berlin (19 makale, 475 alıntı) yapmıştır. Sonuçlar: 2008 yılından itibaren ve özellikle 2018'den sonra yapay zekanın (AI), kariyoloji ve ağız ve diş hastalıklarının araştırılmasında kullanımı giderek büyük ilgi görmeye başlamıştır. Yapay Zekanın (AI) diş hekimliğinin çeşitli dallarında giderek daha fazla uygulanacak çığır açıcı bir keşif olduğu söylenebilir.

Keywords

• Yapay zeka
• Biblioymetri
• Diş çürüğü

A Bibliometric Analysis of the Field of Artificial Intelligence in Cariology

Abstract

Background: The aim of this study is to examine the development trends and dynamics of research on the use of artificial intelligence in dental caries diagnosis, to identify the strengths and limitations of the existing literature, and to guide future research. Methods: A literature search was conducted using the Web of Science database, covering articles published before 3 June 2024. Pilot searches were conducted and 883 studies were reached. After the specified scanning and filtering processes, the study was carried out on 270 publications. In the bibliometric analysis, the Biblioshiny R package as well as the features of Web of Science and VOSviewer software were used for visualizations. Microsoft Excel was used to tabulate the data. Results: There is a general increase in the number of articles published each year. A total of 3081 citations were made to publications on the use of artificial intelligence in cariology. The average number of citations per article was found to be 11.41, and the H index was 29. The most cited country was Germany (581 citations), and the most influential author was Falk Schwendicke. On the basis of institutions, the highest contribution was made by Charite University Medicine Berlin (19 articles, 475 citations). Conclusion: Since 2008, and particularly since 2018, the utilisation of artificial intelligence (AI) in the investigation of dental caries and oral and dental diseases has garnered increasing interest. Artificial Intelligence (AI) can be said to be a groundbreaking discovery that will be increasingly applied in various branches of dentistry.

Keywords

• Artificial intelligence
• Bibliometric
• Dental caries

References

1. 1. Khanagar SB, Alfouzan K, Awawdeh M, Alkadi L, Albalawi F, Alfadley A. Application and performance of artificial intelligence technology in detection, diagnosis and prediction of dental caries (DC)—a systematic review. Diagnostics. 2022;12(5):1083.
2. 2. James SL, Abate D, Abate KH, Abay SM, Abbafati C, Abbasi N, et al. 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. The Lancet. 2018;392(10159):1789-858.
3. 3. Zhang JS, Huang S, Chen Z, Chu C-H, Takahashi N, Yu OY. Application of omics technologies in Cariology research: a critical review with bibliometric analysis. Journal of Dentistry. 2023:104801.
4. 4. Grieco P, Jivraj A, Da Silva J, Kuwajima Y, Ishida Y, Ogawa K, et al. Importance of bitewing radiographs for the early detection of interproximal carious lesions and the impact on healthcare expenditure in Japan. Annals of Translational Medicine. 2022;10(1).
5. 5. Gomez J. Detection and diagnosis of the early caries lesion. BMC oral health. 2015;15(Suppl 1):S3.
6. 6. Wenzel A. Radiographic modalities for diagnosis of caries in a historical perspective: from film to machine-intelligence supported systems. Dentomaxillofacial Radiology. 2021;50(5):20210010.
7. 7. Manziuk E, Barmak O, Krak I, Mazurets O, Skrypnyk T, editors. Formal Model of Trustworthy Artificial Intelligence Based on Standardization. IntelITSIS; 2021.
8. 8. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nature medicine. 2019;25(1):44-56.

9. 9. Anil S, Porwal P, Porwal A. Transforming dental caries diagnosis through artificial intelligence-based techniques. Cureus. 2023;15(7).
10. 10. Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM. How to conduct a bibliometric analysis: An overview and guidelines. Journal of business research. 2021;133:285-96.
11. 11. Van Eck N, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. scientometrics. 2010;84(2):523-38.
12. 12. Aria M, Cuccurullo C. bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of informetrics. 2017;11(4):959-75.
13. 13. Taghdisi kashani A, Batooli Z, Mozafari M. Bibliometric analysis and visualization of top papers in dentistry from 2012 to 2022 based on essential science indicators. Clinical and Experimental Dental Research. 2024;10(1):e832.
14. 14. Lee J-H, Kim D-H, Jeong S-N, Choi S-H. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. Journal of dentistry. 2018;77:106-11.
15. 15. Schwendicke F, Elhennawy K, Paris S, Friebertshäuser P, Krois J. Deep learning for caries lesion detection in near-infrared light transillumination images: A pilot study. Journal of dentistry. 2020;92:103260.
16. 16. Casalegno F, Newton T, Daher R, Abdelaziz M, Lodi-Rizzini A, Schürmann F, et al. Caries detection with near-infrared transillumination using deep learning. Journal of dental research. 2019;98(11):1227-33.
17. 17. Cantu AG, Gehrung S, Krois J, Chaurasia A, Rossi JG, Gaudin R, et al. Detecting caries lesions of different radiographic extension on bitewings using deep learning. Journal of dentistry. 2020;100:103425.
18. 18. Devito KL, de Souza Barbosa F, Felippe Filho WN. An artificial multilayer perceptron neural network for diagnosis of proximal dental caries. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontology. 2008;106(6):879-84.
19. 19. Hung M, Voss MW, Rosales MN, Li W, Su W, Xu J, et al. Application of machine learning for diagnostic prediction of root caries. Gerodontology. 2019;36(4):395-404.
20. 20. Geetha V, Aprameya K, Hinduja DM. Dental caries diagnosis in digital radiographs using back-propagation neural network. Health Information Science and Systems. 2020;8:1-14.
21. 21. Mohammad-Rahimi H, Motamedian SR, Rohban MH, Krois J, Uribe SE, Mahmoudinia E, et al. Deep learning for caries detection: A systematic review. Journal of Dentistry. 2022;122:104115.
22. 22. Kühnisch J, Meyer O, Hesenius M, Hickel R, Gruhn V. Caries detection on intraoral images using artificial intelligence. Journal of dental research. 2022;101(2):158-65.
23. 23. Lian L, Zhu T, Zhu F, Zhu H. Deep learning for caries detection and classification. Diagnostics. 2021;11(9):1672.
24. 24. Small H. Co‐citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for information Science. 1973;24(4):265-9.
25. 25. Selwitz RH, Ismail AI, Pitts NB. Dental caries. The Lancet. 2007;369(9555):51-9.
26. 26. Choi J, Eun H, Kim C. Boosting proximal dental caries detection via combination of variational methods and convolutional neural network. Journal of Signal Processing Systems. 2018;90:87-97.
27. 27. Karimian N, Salehi HS, Mahdian M, Alnajjar H, Tadinada A, editors. Deep learning classifier with optical coherence tomography images for early dental caries detection. Lasers in Dentistry XXIV; 2018: SPIE.
28. 28. Gavinho LG, Araujo SA, Bussadori SK, Silva JV, Deana AM. Detection of white spot lesions by segmenting laser speckle images using computer vision methods. Lasers in Medical Science. 2018;33:1565-71.
29. 29. Bouchahma M, Hammouda SB, Kouki S, Alshemaili M, Samara K, editors. An automatic dental decay treatment prediction using a deep convolutional neural network on X-ray images. 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA); 2019: IEEE.
30. 30. Balbin JR, Banhaw RL, Martin CRO, Rivera JLR, Victorino JR, editors. Caries lesion detection tool using near infrared image processing and decision tree learning. Fourth International Workshop on Pattern Recognition; 2019: SPIE.
31. 31. Gonella G, Binaghi E, Vergani A, Biotti I, Levrini L, editors. A cloud fuzzy logic framework for oral disease risk assessment. Fuzzy Logic and Applications: 12th International Workshop, WILF 2018, Genoa, Italy, September 6–7, 2018, Revised Selected Papers; 2019: Springer.
32. 32. Javed S, Zakirulla M, Baig RU, Asif S, Meer AB. Development of artificial neural network model for prediction of post-streptococcus mutans in dental caries. Computer Methods and Programs in Biomedicine. 2020;186:105198.
33. 33. Ezhov M, Gusarev M, Golitsyna M, Yates JM, Kushnerev E, Tamimi D, et al. Clinically applicable artificial intelligence system for dental diagnosis with CBCT. Scientific reports. 2021;11(1):15006.
34. 34. Vinayahalingam S, Kempers S, Limon L, Deibel D, Maal T, Hanisch M, et al. Classification of caries in third molars on panoramic radiographs using deep learning. Scientific reports. 2021;11(1):12609.
35. 35. Lee E, Park S, Um S, Kim S, Lee J, Jang J, et al. Microbiome of saliva and plaque in children according to age and dental caries experience. Diagnostics. 2021;11(8):1324.
36. 36. Zheng L, Wang H, Mei L, Chen Q, Zhang Y, Zhang H. Artificial intelligence in digital cariology: a new tool for the diagnosis of deep caries and pulpitis using convolutional neural networks. Annals of Translational Medicine. 2021;9(9).
37. 37. Heimisdóttir LH, Lin B, Cho H, Orlenko A, Ribeiro A, Simon-Soro A, et al. Metabolomics insights in early childhood caries. Journal of dental research. 2021;100(6):615-22.
38. 38. 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. Frontiers in Genetics. 2021;12:636867.
39. 39. Wu TT, Xiao J, Sohn MB, Fiscella KA, Gilbert C, Grier A, et al. Machine learning approach identified multi-platform factors for caries prediction in child-mother dyads. Frontiers in cellular and infection microbiology. 2021;11:727630.
40. 40. Milošević D, Vodanović M, Galić I, Subašić M, editors. automated sex assessment of individual adult tooth X-Ray images. 2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA); 2021: IEEE.
41. 41. Zhu H, Cao Z, Lian L, Ye G, Gao H, Wu J. CariesNet: a deep learning approach for segmentation of multi-stage caries lesion from oral panoramic X-ray image. Neural Computing and Applications. 2022:1-9.
42. 42. Kumari AR, Rao SN, Reddy PR. Design of hybrid dental caries segmentation and caries detection with meta-heuristic-based ResneXt-RNN. Biomedical Signal Processing and Control. 2022;78:103961.
43. 43. Rashid U, Javid A, Khan AR, Liu L, Ahmed A, Khalid O, et al. A hybrid mask RCNN-based tool to localize dental cavities from real-time mixed photographic images. PeerJ Computer Science. 2022;8:e888.
44. 44. Bui TH, Hamamoto K, Paing MP. Automated caries screening using ensemble deep learning on panoramic radiographs. Entropy. 2022;24(10):1358.
45. 45. Jaiswal P, Katkar V, Bhirud S. Multi oral disease classification from panoramic radiograph using transfer learning and XGBoost. International Journal of Advanced Computer Science and Applications. 2022;13(12).
46. 46. Li W, Zhu X, Wang X, Wang F, Liu J, Chen M, et al. Segmentation and accurate identification of large carious lesions on high quality x-ray images based on Attentional U-Net model. A proof of concept study. Journal of Applied Physics. 2022;132(3).
47. 47. Qu X, Zhang C, Houser SH, Zhang J, Zou J, Zhang W, et al. Prediction model for early childhood caries risk based on behavioral determinants using a machine learning algorithm. Computer Methods and Programs in Biomedicine. 2022;227:107221.
48. 48. Oztekin F, Katar O, Sadak F, Yildirim M, Cakar H, Aydogan M, et al. An explainable deep learning model to prediction dental caries using panoramic radiograph images. Diagnostics. 2023;13(2):226.
49. 49. Dayı B, Üzen H, Çiçek İB, Duman ŞB. A novel deep learning-based approach for segmentation of different type caries lesions on panoramic radiographs. Diagnostics. 2023;13(2):202.
50. 50. Haghanifar A, Majdabadi MM, Haghanifar S, Choi Y, Ko S-B. PaXNet: Tooth segmentation and dental caries detection in panoramic X-ray using ensemble transfer learning and capsule classifier. Multimedia Tools and Applications. 2023;82(18):27659-79.
51. 51. Ben-Assuli O, Bar O, Geva G, Siri S, Tzur D, Almoznino G. Body mass index and caries: machine learning and statistical analytics of the Dental, Oral, Medical Epidemiological (DOME) nationwide big data study. Metabolites. 2022;13(1):37.
52. 52. Amasya H, Alkhader M, Serindere G, Futyma-Gąbka K, Aktuna Belgin C, Gusarev M, et al. Evaluation of a decision support system developed with deep learning approach for detecting dental caries with cone-beam computed tomography imaging. Diagnostics. 2023;13(22):3471.
53. 53. Park EY, Jeong S, Kang S, Cho J, Cho J-Y, Kim E-K. Tooth caries classification with quantitative light-induced fluorescence (QLF) images using convolutional neural network for permanent teeth in vivo. BMC Oral Health. 2023;23(1):981.
54. 54. Velusamy J, Rajajegan T, Alex SA, Ashok M, Mayuri A, Kiran S. Faster Region‐based Convolutional Neural Networks with You Only Look Once multi‐stage caries lesion from oral panoramic X‐ray images. Expert Systems. 2024;41(6):e13326.
55. 55. Jiang H, Zhang P, Che C, Jin B, Zhu Y. CariesFG: A fine-grained RGB image classification framework with attention mechanism for dental caries. Engineering Applications of Artificial Intelligence. 2023;123:106306.
56. 56. Karakuş R, Öziç MÜ, Tassoker M. AI-Assisted Detection of Interproximal, Occlusal, and Secondary Caries on Bite-Wing Radiographs: A Single-Shot Deep Learning Approach. Journal of Imaging Informatics in Medicine. 2024:1-14.
57. 57. Priya J, Raja SKS, Sudha S. An intellectual caries segmentation and classification using modified optimization-assisted transformer denseUnet++ and ViT-based multiscale residual denseNet with GRU. Signal, Image and Video Processing. 2024:1-15.
58. 58. Ahmed WM, Azhari AA, Alfaraj A, Alhamadani A, Zhang M, Lu C-T. The Quality of AI-Generated Dental Caries Multiple Choice Questions: A Comparative Analysis of ChatGPT and Google Bard Language Models. Heliyon. 2024;10(7).
59. 59. Ayhan B, Ayan E, Bayraktar Y. A novel deep learning-based perspective for tooth numbering and caries detection. Clinical Oral Investigations. 2024;28(3):1-17.
60. 60. Kawazu T, Takeshita Y, Fujikura M, Okada S, Hisatomi M, Asaumi J. Preliminary Study of Dental Caries Detection by Deep Neural Network Applying Domain-Specific Transfer Learning. Journal of Medical and Biological Engineering. 2024:1-6.