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Bibliometric Analysis of the Use of Artificial Intelligence in Dental Implantology

Year 2024, Volume: 10 Issue: 2, 155 - 167, 16.08.2024

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

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.
There are 22 citations in total.

Details

Primary Language Turkish
Subjects Oral and Maxillofacial Surgery, Oral and Maxillofacial Radiology, Oral Implantology, Prosthodontics
Journal Section Research Article
Authors

Ruşen Erdem 0000-0002-5298-7949

Yavuz Selim Genç 0000-0003-0556-2830

Gülbeddin Yalınız 0000-0003-4406-1393

İbrahim Tevfik Gülşen 0000-0002-1014-4417

Publication Date August 16, 2024
Submission Date July 4, 2024
Acceptance Date July 14, 2024
Published in Issue Year 2024 Volume: 10 Issue: 2

Cite

Vancouver Erdem R, Genç YS, Yalınız G, Gülşen İT. Dental İmplantolojide Yapay Zeka Kullanımının Bibliyometrik Analizi. Aydin Dental Journal. 2024;10(2):155-67.

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