Bibliometric Analysis of the Use of Artificial Intelligence in Dental Implantology
Year 2024,
Volume: 10 Issue: 2, 155 - 167, 16.08.2024
Ruşen Erdem
,
Yavuz Selim Genç
,
Gülbeddin Yalınız
,
İbrahim Tevfik Gülşen
Abstract
Objectives: The objective of this study was to examine the evolution of research on the utilisation of artificial intelligence in the field of dental implantology, to identify the strengths and limitations of the existing literature, and to inform future research.
Materials and Methods: A literature search was conducted using the Web of Science database, covering articles published before 4 June 2024. Pilot searches were conducted, resulting in the identification of 488 studies. After the determined screening and filtering processes, the study was carried out on 175 publications. VOSviewer software was used for visualisations in bibliometric analysis. Microsoft Excel was used for tabulation of the data.
Results: The number of articles published each year is on the rise. China is the most influential country in terms of the number of publications on the application of artificial intelligence in the field of implantology, with 36 articles. South Korea is the most influential country in terms of citations, with 392. The most influential author was Jae-Hong Lee. In terms of institutional contributions, the highest number of publications was made by Wonkwang University and Yonsei University in South Korea.
Conclusion: Since 2018, the use of artificial intelligence (AI) in the field of dental implantology has attracted increasing attention. It can be argued that AI represents a groundbreaking discovery that will be increasingly applied in various branches of implantology.
References
- Sikri A, Sikri J, Gupta R. Artificial intelligence in
prosthodontics and oral implantology–A narrative review.
Glob Acad J Dent Oral Health. 2023;5(2):13–9.
- Altalhi AM, Alharbi FS, Alhodaithy MA, Almarshedy
BS, Al-Saaib MY, Aljohani AS, et al. The Impact of
Artificial Intelligence on Dental Implantology: A Narrative
Review. Cureus [Internet]. 2023 [cited 2024 Jul 2];15(10).
Available from: https://www.ncbi.nlm.nih.gov/pmc/
articles/PMC10685062/
- Thurzo A, Urbanová W, Novák B, Czako L, Siebert T,
Stano P, et al. Where is the artificial intelligence applied
in dentistry? Systematic review and literature analysis.
In: Healthcare [Internet]. MDPI; 2022 [cited 2024 Jul 2].
p. 1269. Available from: https://www.mdpi.com/2227-
9032/10/7/1269
- Alghamdi HS, Jansen JA. The development and future
of dental implants. Dent Mater J. 2020;39(2):167–72.
- Dibart S, Kernitsky-Barnatan J, Di Battista M,
Montesani L. Robot assisted implant surgery: Hype
or hope? J Stomatol Oral Maxillofac Surg. 2023
Dec;124(6S):101612.
- Saeed A, Alkhurays M, AlMutlaqah M, AlAzbah
M, Alajlan SA. Future of using robotic and artificial
intelligence in implant dentistry. Cureus [Internet]. 2023
[cited 2024 Jul 2];15(8). Available from: https://www.
ncbi.nlm.nih.gov/pmc/articles/PMC10494478/
- Revilla-León M, Gómez-Polo M, Vyas S, Barmak BA,
Galluci GO, Att W, et al. Artificial intelligence applications
in implant dentistry: A systematic review. J Prosthet Dent.
2023;129(2):293–300.
- Godin B. On the origins of bibliometrics. Scientometrics.
2006;68(1):109–33.
- Chen C. CiteSpace II: Detecting and visualizing
emerging trends and transient patterns in scientific
literature. J Am Soc Inf Sci Technol. 2006 Feb;57(3):359–
77.
- Van Eck N, Waltman L. Software survey: VOSviewer,
a computer program for bibliometric mapping.
scientometrics. 2010;84(2):523–38.
- Banerjee TN, Paul P, Debnath A, Banerjee S. Unveiling
the prospects and challenges of artificial intelligence in
implant dentistry. A systematic review. J Osseointegration.
2024;16(1):53–60.
- Moufti MA, Trabulsi N, Ghousheh M, Fattal T, Ashira
A, Danishvar S. Developing an Artificial Intelligence
Solution to Autosegment the Edentulous Mandibular Bone
for Implant Planning. Eur J Dent. 2023 Oct;17(04):1330–
7.
- Kurt Bayrakdar S, Orhan K, Bayrakdar IS, Bilgir E,
Ezhov M, Gusarev M, et al. A deep learning approach
for dental implant planning in cone-beam computed
tomography images. BMC Med Imaging. 2021 May
19;21(1):86.
- Minnema J, van Eijnatten M, Kouw W, Diblen F,
Mendrik A, Wolff J. CT image segmentation of bone for
medical additive manufacturing using a convolutional
neural network. Comput Biol Med. 2018;103:130–9.
- 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(26):e20787.
- 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(7):984.
- 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(4):1117.
- 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.
2020;10(11):910.
- Ghensi P, Manghi P, Zolfo M, Armanini F, Pasolli E,
Bolzan M, et al. Strong oral plaque microbiome signatures
for dental implant diseases identified by strain-resolution
metagenomics. Npj Biofilms Microbiomes. 2020;6(1):47.
- Small H. Co‐citation in the scientific literature: A new
measure of the relationship between two documents. J Am
Soc Inf Sci. 1973 Jul;24(4):265–9.
- Krois J, Ekert T, Meinhold L, Golla T, Kharbot B,
Wittemeier A, et al. Deep learning for the radiographic
detection of periodontal bone loss. Sci Rep. 2019;9(1):8495.
- Hadj Saïd M, Le Roux MK, Catherine JH, Lan R.
Development of an Artificial Intelligence Model to Identify
a Dental Implant from a Radiograph. Int J Oral Maxillofac
Implants [Internet]. 2020 [cited 2024 Jul 3];35(6).
Available from: https://search.ebscohost.com/login.
Dental İmplantolojide Yapay Zeka Kullanımının Bibliyometrik Analizi
Year 2024,
Volume: 10 Issue: 2, 155 - 167, 16.08.2024
Ruşen Erdem
,
Yavuz Selim Genç
,
Gülbeddin Yalınız
,
İbrahim Tevfik Gülşen
Abstract
Amaç: Bu çalışmanın amacı, yapay zekanın dental implantolojideki kullanımına yönelik yapılan araştırmaların gelişim trendlerini ve dinamiklerini incelemek, mevcut literatürün güçlü ve zayıf yönlerini belirlemek ve gelecekteki araştırmalara rehberlik etmektir.
Gereç ve Yöntemler: Web of Science veritabanı kullanılarak 4 Haziran 2024’den önce yayınlanan makaleleri kapsayan bir literatür taraması yapılmıştır. Pilot aramalar yapılarak 488 çalışmaya ulaşıldı. Belirlenen tarama ve filtreleme işlemlerinin ardından çalışma 175 yayın üzerinde gerçekleştirilmiştir. Bibliyometrik analizde görselleştirmeler için VOSviewer programı kullanıldı. Verilerin tablolanması için Microsoft Excel kullanıldı.
Bulgular: Her yıl yayınlanan makale sayısında genel bir artış söz konusudur. Yapay zekanın implantoloji alanında uygulanmasında 36 yayın ile Çin en etkili ülke iken, alıntılanma sayılarına bakıldığında ise Güney Kore 392 atıf ile en etkili ülke konumundadır. En etkili yazar Jae-Hong LEE olmuştur. Kurumlar bazında ise en yüksek katkıyı Güney Kore'deki Wonkwang Üniversitesi ve Yonsei Üniversitesi yapmıştır.
Sonuç: 2018 yılından itibaren yapay zekanın (YZ), dental implantoloji alanında kullanımı giderek büyük ilgi görmeye başlamıştır. Yapay zekanın implantolojinin çeşitli dallarında giderek daha fazla uygulanacak çığır açıcı bir keşif olduğu söylenebilir.
References
- Sikri A, Sikri J, Gupta R. Artificial intelligence in
prosthodontics and oral implantology–A narrative review.
Glob Acad J Dent Oral Health. 2023;5(2):13–9.
- Altalhi AM, Alharbi FS, Alhodaithy MA, Almarshedy
BS, Al-Saaib MY, Aljohani AS, et al. The Impact of
Artificial Intelligence on Dental Implantology: A Narrative
Review. Cureus [Internet]. 2023 [cited 2024 Jul 2];15(10).
Available from: https://www.ncbi.nlm.nih.gov/pmc/
articles/PMC10685062/
- Thurzo A, Urbanová W, Novák B, Czako L, Siebert T,
Stano P, et al. Where is the artificial intelligence applied
in dentistry? Systematic review and literature analysis.
In: Healthcare [Internet]. MDPI; 2022 [cited 2024 Jul 2].
p. 1269. Available from: https://www.mdpi.com/2227-
9032/10/7/1269
- Alghamdi HS, Jansen JA. The development and future
of dental implants. Dent Mater J. 2020;39(2):167–72.
- Dibart S, Kernitsky-Barnatan J, Di Battista M,
Montesani L. Robot assisted implant surgery: Hype
or hope? J Stomatol Oral Maxillofac Surg. 2023
Dec;124(6S):101612.
- Saeed A, Alkhurays M, AlMutlaqah M, AlAzbah
M, Alajlan SA. Future of using robotic and artificial
intelligence in implant dentistry. Cureus [Internet]. 2023
[cited 2024 Jul 2];15(8). Available from: https://www.
ncbi.nlm.nih.gov/pmc/articles/PMC10494478/
- Revilla-León M, Gómez-Polo M, Vyas S, Barmak BA,
Galluci GO, Att W, et al. Artificial intelligence applications
in implant dentistry: A systematic review. J Prosthet Dent.
2023;129(2):293–300.
- Godin B. On the origins of bibliometrics. Scientometrics.
2006;68(1):109–33.
- Chen C. CiteSpace II: Detecting and visualizing
emerging trends and transient patterns in scientific
literature. J Am Soc Inf Sci Technol. 2006 Feb;57(3):359–
77.
- Van Eck N, Waltman L. Software survey: VOSviewer,
a computer program for bibliometric mapping.
scientometrics. 2010;84(2):523–38.
- Banerjee TN, Paul P, Debnath A, Banerjee S. Unveiling
the prospects and challenges of artificial intelligence in
implant dentistry. A systematic review. J Osseointegration.
2024;16(1):53–60.
- Moufti MA, Trabulsi N, Ghousheh M, Fattal T, Ashira
A, Danishvar S. Developing an Artificial Intelligence
Solution to Autosegment the Edentulous Mandibular Bone
for Implant Planning. Eur J Dent. 2023 Oct;17(04):1330–
7.
- Kurt Bayrakdar S, Orhan K, Bayrakdar IS, Bilgir E,
Ezhov M, Gusarev M, et al. A deep learning approach
for dental implant planning in cone-beam computed
tomography images. BMC Med Imaging. 2021 May
19;21(1):86.
- Minnema J, van Eijnatten M, Kouw W, Diblen F,
Mendrik A, Wolff J. CT image segmentation of bone for
medical additive manufacturing using a convolutional
neural network. Comput Biol Med. 2018;103:130–9.
- 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(26):e20787.
- 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(7):984.
- 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(4):1117.
- 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.
2020;10(11):910.
- Ghensi P, Manghi P, Zolfo M, Armanini F, Pasolli E,
Bolzan M, et al. Strong oral plaque microbiome signatures
for dental implant diseases identified by strain-resolution
metagenomics. Npj Biofilms Microbiomes. 2020;6(1):47.
- Small H. Co‐citation in the scientific literature: A new
measure of the relationship between two documents. J Am
Soc Inf Sci. 1973 Jul;24(4):265–9.
- Krois J, Ekert T, Meinhold L, Golla T, Kharbot B,
Wittemeier A, et al. Deep learning for the radiographic
detection of periodontal bone loss. Sci Rep. 2019;9(1):8495.
- Hadj Saïd M, Le Roux MK, Catherine JH, Lan R.
Development of an Artificial Intelligence Model to Identify
a Dental Implant from a Radiograph. Int J Oral Maxillofac
Implants [Internet]. 2020 [cited 2024 Jul 3];35(6).
Available from: https://search.ebscohost.com/login.