The role of artificial intelligence in colonoscopy imaging and colonic diseases: A scientometrics analysis and visualization study

Tuba Erürker Öztürk 1 *
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1 Department of Gastroenterology, Denizli Denipol Hospital, Denizli, Turkey
* Corresponding Author
J CLIN MED KAZ, Volume 20, Issue 4, pp. 54-59. https://doi.org/10.23950/jcmk/13539
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ABSTRACT

Introduction: Artificial intelligence (AI) has made a big difference and is used in many different sectors also in medicine. We sought to identify the areas of interest and potential future directions for AI in the field of colonoscopy imaging and colonic diseases by utilizing bibliometrics to analyze the previous 50 years' worth of changes on this topic.
Material and methods: Using the Web of Knowledge (WOS) database, we searched for articles published from 1970 to 2021 using the keywords related to colonoscopy imaging/colonic diseases and AI.  The retrieved articles were analysed with bibliometric methods.
Results: A total of 278 documents were analyzed in this study. The earliest article was published in 1997 and the vast majority of the documents were published in 2021 (n=81). There was a growth in publications number in the last 5 years. The documents were cited 3054 times in total and had 10.99 citations per document. The main Hirsch (H) index of the documents was 27. A total of 41 countries contributed to the literature. The United States of America (USA), the People’s Republic of China, and England were the leading countries on this topic. Also, England had the highest number of citations (total of 974 citations, 31.42 per document) and the USA publications had the highest H index.
Discussion: Artificial intelligence facilitates diagnosis and treatment possibilities, especially in the field of health. Especially the use of artificial intelligence in colonoscopic imaging reduces the risk of missing a possible polyp or a mucosal pathology. The integration of artificial intelligence into imaging methods has been the most in the last 5 years. Most studies on this subject have been done in the USA.
Conclusion: Our research may offer a historical perspective on the development of AI in colorectal diseases. The documents were limited to some developing countries.

CITATION

Erürker Öztürk T. The role of artificial intelligence in colonoscopy imaging and colonic diseases: A scientometrics analysis and visualization study. J CLIN MED KAZ. 2023;20(4):54-9. https://doi.org/10.23950/jcmk/13539

REFERENCES

  • Min JK, Kwak MS, Cha JM. Overview of Deep Learning in Gastrointestinal Endoscopy. Gut Liver. 2019;13(4):388-393. https://doi.org/10.5009/gnl18384
  • Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69S:S36-S40. https://doi.org/10.1016/j.metabol.2017.01.011
  • Theofilatos K, Pavlopoulou N, Papasavvas C, Likothanassis S, Dimitrakopoulos C, Georgopoulos E, et al. Predicting protein complexes from weighted protein-protein interaction graphs with a novel unsupervised methodology: Evolutionary enhanced Markov clustering. Artif Intell Med. 2015;63(3):181-9. https://doi.org/10.1016/j.artmed.2014.12.012
  • Le Berre C, Sandborn WJ, Aridhi S, Devignes MD, Fournier L, Smaïl-Tabbone M, et al. Application of Artificial Intelligence to Gastroenterology and Hepatology. Gastroenterology. 2020;158(1):76-94.e2. https://doi.org/10.1053/j.gastro.2019.08.058
  • Ribeiro E, Uhl A, Wimmer G, Häfner M. Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification. Comput Math Methods Med. 2016;2016:6584725. https://doi.org/10.1155/2016/6584725
  • Sánchez-Montes C, Bernal J, García-Rodríguez A, Córdova H, Fernández-Esparrach G. Review of computational methods for the detection and classification of polyps in colonoscopy imaging. Gastroenterol Hepatol. 2020;43(4):222-232. English, Spanish. https://doi.org/10.1016/j.gastrohep.2019.11.004
  • Ruffle JK, Farmer AD, Aziz Q. Artificial Intelligence-Assisted Gastroenterology- Promises and Pitfalls. Am J Gastroenterol. 2019;114(3):422-428. https://doi.org/10.1038/s41395-018-0268-4
  • Corley DA, Jensen CD, Marks AR, Zhao WK, Lee JK, Doubeni CA, et al. Adenoma detection rate and risk of colorectal cancer and death. N Engl J Med. 2014;370(14):1298-306. https://doi.org/10.1056/NEJMoa1309086
  • van Rijn JC, Reitsma JB, Stoker J, Bossuyt PM, van Deventer SJ, Dekker E. Polyp miss rate determined by tandem colonoscopy: a systematic review. Am J Gastroenterol. 2006;101(2):343-50. https://doi.org/10.1111/j.1572-0241.2006.00390.x
  • Byrne MF, Shahidi N, Rex DK. Will Computer-Aided Detection and Diagnosis Revolutionize Colonoscopy? Gastroenterology. 2017;153(6):1460-1464.e1. https://doi.org/10.1053/j.gastro.2017.10.026
  • Misawa M, Kudo SE, Mori Y, Cho T, Kataoka S, Yamauchi A, et al. Artificial Intelligence-Assisted Polyp Detection for Colonoscopy: Initial Experience. Gastroenterology. 2018;154(8):2027-2029.e3. https://doi.org/10.1053/j.gastro.2018.04.003
  • https://tr.wikipedia.org/wiki/Web_of_Science. [Access date: 25June 2022].
  • https://www.webofscience.com/wos/woscc/basic-search [Access date: 25June 2022].
  • Sirinukunwattana K, Ahmed Raza SE, Yee-Wah Tsang, Snead DR, Cree IA, Rajpoot NM. Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images. IEEE Trans Med Imaging. 2016;35(5):1196-1206. https://doi.org/10.1109/TMI.2016.2525803
  • Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394-424. https://doi.org/10.3322/caac.21492
  • Guinney J, Dienstmann R, Wang X, De Reyniès A, Schlicker A, Soneson C, et al. The consensus molecular subtypes of colorectal cancer. Nat Med. 2015;21:1350–6. https://doi.org/10.1038/nm.3967
  • Zhang C, Luo M, Zhu H, Zhou J, Miao L. The 100 Most-Cited Articles in the Field of Colorectal Diseases from 1955 to 2020: A Bibliometric Analysis. Turk J Gastroenterol. 2022;33(3):221-232. https://doi.org/10.5152/tjg.2021.20901
  • Özlü A. Bibliometric Analysis of Publications on Pulmonary Rehabilitation. Black Sea Journal of Health Science. 2022; 5(2): 219-225. https://doi.org/10.19127/bshealthscience.1032380
  • Özlü C. Scopus Veri Tabanına Dayalı Bibliyometrik Değerlendirme: Miyelodisplastik Sendrom Konulu Yayınların Global Analizi ve Türkiye Kaynaklı Yayınların Değerlendirilmesi. Biotech& Strategic Health Res. 2021; 5(2):125-131. https://doi.org/10.34084/bshr.948974
  • Alkan-Çeviker S, Öntürk H, Alıravcı ID, Sıddıkoğlu D. Trends of COVID 19 vaccines: International collaboration and visualized analysis. Infect Dis Clin Microbiol. 2021; 3: 129-136. https://doi.org/10.36519/idcm.2021.70
  • Powell AG, Hughes DL, Wheat JR, Lewis WG. The 100 most influential manuscripts in gastric cancer: A bibliometric analysis. Int J Surg. 2016;28:83-90. https://doi.org/10.1016/j.ijsu.2016.02.028
  • Uyar C, Alkan S, Tahmaz A. Research trends and hotspots of osteoarticular involvement in brucellosis. Journal of Zoonotic Diseases. 2022; 6(2):69-77. https://doi.org/10.22034/jzd.2022.14656
  • Durgun C. Qualitative analysis of theses on laparoscopic cholecystectomy. D J Med Sci. 2021;7(3):248-254.
  • Zhao Y, Yin Z, Du H, Huang K, Zhang F, Chen H. The latest research trends in primary biliary cholangitis: a bibliometric analysis. Clin Exp Med. 2022. https://doi.org/10.1007/s10238-022-00825-0
  • Köylüoğlu AN, Aydın B, Özlü C. Bibliometric evaluation based on scopus database: Global analysis of publications on diabetic retinopathy and comparison with publications from Turkey. D J Med Sci. 2021;7(3):268-275.
  • Şahin S. Vasküler Cerrahiye Genel Bakış. Black Sea Journal of Health Science. 2022;3-4.
  • Cinpolat HY. A bibliometric analysis of global research trends on biomarker studies in Alzheimer’s disease. D J Med Sci. 2022;8(1):5-10.
  • Küçük U, Alkan S, Uyar C. Bibliometric analysis of infective endocarditis. Iberoam J Med. 2021;3(4):350-5. https://doi.org/10.53986/ibjm.2021.0055
  • Gürler M, Alkan S, Özlü C, Aydın B. Collaborative Network Analysis and Bibliometric Analysis of Publications on Diabetic Foot Infection. Biotech&Strategic Health Res. 2021; 5(3):194-199. https://doi.org/10.34084/bshr.993099
  • Özlü A. Miyofisal Ağrı Sendromu Konulu Yayınların Analizi. International Anatolia Academic Online Journal Health Sciences. 2021; 7(3);65-78.
  • Bhandari M, Busse J, Devereaux PJ, Montori VM, Swiontkowski M, Tornetta Iii P, et al. Factors associated with citation rates in the orthopedic literature. Can J Surg. 2007;50(2):119-23.
  • Bornmann, L, Daniel, H-D. What do we know about theh index? J Am Soc Inf Sci Technol. 2007;58(9):1381-1385. https://doi.org/10.1002/asi.20609
  • Hirsch JE. An index to quantify an individual's scientific research output. Proc Natl Acad Sci Unit States Am. 2005;102(46):16569-16572. https://doi.org/10.1073/pnas.0507655102
  • Misawa M, Kudo SE, Mori Y, Cho T, Kataoka S, Yamauchi A, et al. Artificial Intelligence-Assisted Polyp Detection for Colonoscopy: Initial Experience. Gastroenterology 2018; 154: 2027-2029.e3. https://doi.org/10.1053/j.gastro.2018.04.003
  • Urban G, Tripathi P, Alkayali T, Mittal M, Jalali F, Karnes W, et al. Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy. Gastroenterology. 2018; 155:1069-1078.e8 https://doi.org/10.1053/j.gastro.2018.06.037