A Comperative Study of Use Of Artificial Intelligence in Oral Radiology Education

Year 2023, Volume: 50 Issue: 1, 41 - 46, 30.04.2023

Sinem Coşkun , Müjgan Güngör

https://doi.org/10.52037/eads.2023.0009

Cited By: 1

Abstract

Purpose: The aim of this study is to compare the efficacy of artificial intelligence use in oral radiology learning in the undergraduate dental students.
Materials&Methods: Fifty third-year students in the University of Lokman Hekim were detected images with the artificial intelligence method (AI) and standard lecture method (SL) for anatomical landmarks in panoramic radiographs. SL consisted of a frontal lecture through a standardized presentation. CranioCatch model (Eskisehir, Turkey) was used as deep learning-based artificial intelligence model. One panoramic image was loaded to the application and anatomic landmarks were detected by teacher, students were asked to mark. AI recorded and scored students answers. A questionnaire study was conducted for the perception of students in terms of validity and reliability regarding assessment and evaluation for each methods.
Results: 50 undergraduate students (26 female,24 male) answered 7questions, 5-point Likert type. The conformity to the normal distribution was evaluated with the Shapiro-Wilk test and the graphical approach (Normal Q-Q Plot). The values did not conform to the normal distribution. As a result of the reliability analysis performed for the measurement tool, the Cronbach’s Alpha coefficient was found 0.828. Wilcoxon Test was used to test the significance of the difference between each methods. There is a statistically significant difference between the mean values of evaluation measurements(p=0.014). AI was higher than the mean of evaluation measurement values compared to SL.
Conclusion: AI models have performed very well in measurement and evaluation in oral radiology learning.

Keywords

artificial intelligence, anatomic landmarks, dental restoration, dental education, panoramic image

References

• 1. Serrano CM, Wesselink PR, Vervoorn JM. First experiences with patient-centered training in virtual reality. J Dent Educ. 2020 Jan 23;84(05):607-614.
• 2. Kerkstra RL, Rustagi KA, Grimshaw AA, Minges KE. Dental education practices during COVID‐19: A scoping review. Journal of dental education. 2022 Jan 03;86(5):546-573.
• 3. Ramnanan CJ, Pound LD. Advances in medical education and practice: student perceptions of the flipped classroom.Adv Med Educ Pract. 2017 Jan 13;8:63-73.
• 4. Davis BG. Tools for Teaching. 2nd ed. Sanfrancisco, CA: John Wiley & Sons; 2009
• 5. Hendricson WD, Andrieu SC, Chadwick DG, Chmar JE, Cole JR, George MC, et al. Educational strategies associated with development of problem-solving, critical thinking, and self-directed learning. J Dent Educ. 2006;70(9):925-936.
• 6. Obermeyer Z. Emanuel EJ. Predicting the Future-Big Data, Machine Learning, and Clinical Medicine. N. Engl. J. Med. 2016 Sep 29; 375(13):1216–1219.
• 7. Thurzo A, Urbanová W, Novák B, Czako L, Siebert T, Stano P, Varga I. Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis. In Healthcare 2022 July 8;10(7): 1269
• 8. Faber, J.; Faber, C.; Faber, P. Artificial Intelligence in Orthodontics. APOS Trends Orthod. 2019; 9(4):201-205.
• 9. Tanikawa C, Yamashiro T. Development of Novel Artificial Intelligence Systems to Predict Facial Morphology after Orthognathic Surgery and Orthodontic Treatment in Japanese Patients. Sci. Rep. 2021 August 21;11(1):1-11.
• 10. MacHoy ME, Szyszka-Sommerfeld L, Vegh A, Gedrange T, Woźniak K. The Ways of Using Machine Learning in Dentistry. Adv. Clin. Exp. Med. 2020 Mar 01; 29(3):375-384.
• 11. Choi JW, Park H, In-Hwan Kim BS, Kim N, Kwon SM, Lee JY. Surgery-First Orthognathic Approach to Correct Facial Asymmetry: Artificial Intelligence–Based Cephalometric Analysis. Plast. Reconstr. Surg. 2022 Feb 23; 149(3): 496e–499e.
• 12. Joshi VK. Dental Treatment Planning and Management for the Mouth Cancer Patient. Oral Oncol. 2010 June; 46(6): 475–479.
• 13. Lee JH, Kim DH, Jeong SN, Choi SH. Detection and Diagnosis of Dental Caries Using a Deep Learning-Based Convolutional Neural Network Algorithm. J. Dent. 2018 October;77:106–111.
• 14. Schwendicke F, Rossi JG, Göstemeyer G, Elhennawy K, Cantu AG, Gaudin R, Chaurasia A, Gehrung S, Krois J. Cost-Effectiveness of Artificial Intelligence for Proximal Caries Detection. J. Dent. Res. 2020 Nov 16;100(4):369–376.
• 15. Farhadian M, Shokouhi P, Torkzaban P. A Decision Support System Based on Support Vector Machine for Diagnosis of Periodontal Disease. BMC Res. Notes 2020 July 13;13(1):337.
• 16. Chen WP, Chang SH, Tang CY, Liou ML, Tsai SJJ, Lin YL. Composition Analysis and Feature Selection of the Oral Microbiota Associated with Periodontal Disease. BioMed Res. Int. 2018 Nov 15;2018: 3130607.
• 17. Boreak, N. Effectiveness of Artifcial Intelligence Applications Designed for Endodontic Diagnosis, Decision-Making, and Prediction of Prognosis: A Systematic Review. J. Contemp. Dent. Pract. 2020;21(8):926-934.
• 18. Keskin C, Keles A. Digital Applications in Endodontics: An Update and Review. J. Exp. Clin. Med. 2021 May 19;38(S2):168-174.
• 19. Shafi N, Bukhari F, Iqbal W, Almustafa KM, Asif M, Nawaz Z. Cleft Prediction before Birth Using Deep Neural Network. Health Inform. J. 2020 April 14; 26(4): 2568-2585.
• 20. Bernauer SA, Zitzmann NU, Joda T. The Use and Performance of Artificial Intelligence in Prosthodontics: A Systematic Review. Sensors. 2021 October 5;21(19):6628.
• 21. Cabanes-Gumbau G, Palma JC, Kois JC, Revilla-León M. Transferring the Tooth Preparation Finish Line on Intraoral Digital Scans to Dental Software Programs: A Dental Technique. J. Prosthet. Dent. 2022 Jan 5;in press.
• 22. Logozzo S, Franceschini G, Kilpela A, Caponi M, Governi L, Blois L. A Comparative Analysis of Intraoral 3d Digital Scanners for Restorative Dentistry. Internet J. Med. Technol. 2011;5(1):1–18.
• 23. Oğuz Eİ, Kılıçarslan MA, Ocak M, Bilecenoğlu B, Ekici Z. Marginal and Internal Fit of Feldspathic Ceramic CAD/CAM Crowns Fabricated via Different Extraoral Digitization Methods: A Micro-Computed Tomography Analysis. Odontology. 2020 October 26;109(2):440-447.
• 24. Kaplan A, Cao H, FitzGerald JM, Iannotti N, Yang E, Kocks JWH, et al. Artificial Intelligence/Machine Learning in Respiratory Medicine and Potential Role in Asthma and COPD Diagnosis. J. Allergy Clin. Immunol. Pract. 2021 June;9(6):2255–2261.
• 25. Matava C, Pankiv E, Ahumada L, Weingarten B, Simpao A. Artificial Intelligence, Machine Learning and the Pediatric Airway. Paediatr. Anaesth. 2019 Dec 17;30(3): 264–268.
• 26. Monterubbianesi R, Tosco V, Vitiello F, Orilisi G, Fraccastoro F, Putignano A, Orsini G. Augmented, Virtual and Mixed Reality in Dentistry: A Narrative Review on the Existing Platforms and Future Challenges. Appl. Sci. 2022 Jan 15;12(2):877.
• 27. Murray T. Authoring intelligent tutoring systems: an analysis of the state of the art. International Journal of Artificial Intelligence in Education. 1999;10:98-129.
• 28. Feeney L, Reynolds PA, Eaton KA, Harper J. A description of the new technologies used in transforming dental education. British Dental Journal; 2008 Jan 12;204(1):19-28.
• 29. Khanna SS, Dhaimade AP. Artificial Intelligence: Transforming Dentistry Today. Indian J Basic Appl Med Res. 2017 June;6(3):161–7.
• 30. Hwang JJ, Jung YH, Cho BH, Heo MS. An overview of deep learning in the field of dentistry. Imaging Sci Dent. 2019 March 25;49(1):1-7.
• 31. Bas B, Ozgonenel O, Ozden B, Bekcioglu B, Bulut E, Kurt M. Use of artificial neural network in differentiation of subgroups of temporomandibular internal derangements: a preliminary study. J Oral Maxillofac Surg. 2012 Jan;70(1):51–59.
• 32. Shaban M, Khurram SA, Fraz MM, Alsubaie N, Masood I, Mushtaq S, et al. A novel digital score for abundance of tumour infiltrating lymphocytes predicts disease free survival in oral squamous cell carcinoma. Sci Rep. 2019 September 16;9(1):1-13.
• 33. Bychkov D, Linder N, Turkki R, Nordling S, Kovanen PE, Verrill C, et al. Deep learning based tissue analysis predicts outcome in colorectal cancer. Sci Rep. 2018 Feb 21;8(1):1-11.
• 34. Sur J, Bose S, Khan F, Dewangan D, Sawriya E, Roul A. Knowledge, attitudes, and perceptions regarding the future of artificial intelligence in oral radiology in India: A survey. Imaging Science in Dentistry. 2020 Sep 16; 50(3):193–198.
• 35. Yau HT, Tsou LS, Tsai MJ. Octree-based virtual dental training system with a haptic device. Computer-Aided Design & Applications; 2013 Aug 05;3(1-4):415-424.
• 36. Murray T. “Authoring intelligent tutoring system for visual classification problem solving. Artificial intelligence in medicine. 2006 Jan;36(1):85-117.
• 37. Crowley R, Medvedeva O. An intelligent tutoring system for visual classification problem solving. Artificial Intelligence in Medicine. 2006 Jan; 36(1):85-117.
• 38. Ferro AS, Nicholson K & Koka S. Innovative trends in implant dentistry training and education: a narrative review. Journal of Clinical Medicine. 2019 October 04;8(10):1618.