Treffer: Implementation of machine learning models as a quantitative evaluation tool for preclinical studies in dental education.
Title:
Implementation of machine learning models as a quantitative evaluation tool for preclinical studies in dental education.
Authors:
Oguzhan A; Department of Restorative Dentistry, Faculty of Dentistry, Ege University, Izmir, Turkey., Peskersoy C; Department of Restorative Dentistry, Faculty of Dentistry, Ege University, Izmir, Turkey., Devrimci EE; Department of Restorative Dentistry, Faculty of Dentistry, Ege University, Izmir, Turkey., Kemaloglu H; Department of Restorative Dentistry, Faculty of Dentistry, Ege University, Izmir, Turkey., Onder TK; Department of Mechanical Engineering, ARQUQ Project Partnership, Izmir, Turkey.
Source:
Journal of dental education [J Dent Educ] 2025 Mar; Vol. 89 (3), pp. 383-397. Date of Electronic Publication: 2024 Sep 26.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Wiley-Blackwell Country of Publication: United States NLM ID: 8000150 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1930-7837 (Electronic) Linking ISSN: 00220337 NLM ISO Abbreviation: J Dent Educ Subsets: MEDLINE
Imprint Name(s):
Publication: 2020- : Hoboken, NJ : Wiley-Blackwell
Original Publication: [Washington, etc.] American Assn. of Dental Schools.
Original Publication: [Washington, etc.] American Assn. of Dental Schools.
MeSH Terms:
References:
Lugassy D, Levanon Y, Pilo R, et al. Predicting the clinical performance of dental students with a manual dexterity test. PLoS One. 2018;13(3):e0193980.
Tuncer D, Arhun N, Yamanel K, Celik C, Dayangac B. Dental students’ ability to assess their performance in a preclinical restorative course: comparison of students’ and faculty members’ assessments. J Dent Educ. 2015;79:658‐664.
Nassar HM, Tekian A. Computer simulation and virtual reality in undergraduate operative and restorative dental education: a critical review. J Dent Educ. 2020;84(7):812‐829.
Park SE, Anderson NK, Karimbux NY. OSCE and case presentations as active assessments of dental student performance. J Dent Educ. 2016;80(3):334‐338.
Tricio JA, Woolford MJ, Escudier MP. Fostering dental students' academic achievements and reflection skills through clinical peer assessment and feedback. J Dent Educ. 2016;80(8):914‐923.
Alfakhry G, Mustafa K, Alagha MA, et al. Peer‐assessment ability of trainees in clinical restorative dentistry: can it be fostered? BDJ Open. 2022;8(1):22‐29.
AlHumaid J, Tantawi ME, Al‐Ansari AA, Al‐Harbi FA. Agreement in scoring preclinical dental procedures: impact on grades and instructor‐related determinants. J Dent Educ. 2016;80(5);553‐562.
Fine P, Leung A, Tonni I, Louca C. Dental teacher feedback and student learning: a qualitative study. Dent J. 2023;11(7):164‐176.
Callan RS, Cooper JR, Young NB, Mollica AG, Furness AR, Looney SW. Inter‐ and intrarater reliability using different software versions of E4D compare in dental education. J Dental Educ. 2015;79(6):711‐718.
Bandiaky ON, Lopez S, Hamon L, Clouet R, Soueidan A, Le Guehennec L. Impact of haptic simulators in preclinical dental education: a systematic review. J Dental Educ. 2024:88(3):366‐379.
Hori M, Uematsu Y, Kato A, et al. Identification method for dental alloy type using a cosine similarity program: a preliminary investigation. Dent Mater J. 2023;42(5):723‐731.
Rahimi MH, Vinayahalingam S, Mahmoudinia E, et al. Superresolution of dental panoramic radiographs using deep learning: a pilot study. Diagnostics. 2023;13(5):996‐1009.
Rodrigues P, Nicolau F, Norte M, et al. Preclinical dental students’ self‐assessment of an improved operative dentistry virtual reality simulator with haptic feedback. Sci Rep. 2023;13(1):2823‐2841.
Golkari A, Sabokseir A, Pakshir HR, Dean MC, Sheiham A, Watt RG. A comparison of photographic, replication and direct clinical examination methods for detecting developmental defects of enamel. BMC Oral Health. 2011;11:16‐23.
Peng J, Shi C, Laugeman E, et al. Implementation of the structural SIMilarity (SSIM) index as a quantitative evaluation tool for dose distribution error detection. Med Phys. 2020;47(4):1907‐1919.
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE Trans Imag Process. 2004;13(4):600‐612.
van der Walt S, Schönberger JL, Nunez‐Iglesias J, et al. Scikit‐image: image processing in Python. PeerJ. 2014;2:453‐472.
Virtanen P, Gommers R, Oliphant TE, et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods. 2020;17(3):261‐272.
Chambers DW, Labarre EE. Why professional judgment is better than objective description in dental faculty evaluations of student performance. J Dent Educ. 2014;78(5):681‐693.
Saeed SG, Bain JL, Khoo E, Siqueira WL, van der Hoeven R. Should attendance for preclinical simulation and clinical education be mandatory. J Dent Educ. 2021;85(10);1655‐1663.
Thistlethwaite JE, Davies D, Ekeocha S, et al. The effectiveness of case‐based learning in health professional education. A BEME systematic review: BEME Guide No. 23. Med Teach. 2012;34(6);e421‐e444.
Rung A, George R. A systematic literature review of assessment feedback in preclinical dental education. Eur J Dent Educ. 2021;25(1);135‐150.
Patil S, Bhandi S, Awan KH, et al. Effectiveness of haptic feedback devices in preclinical training of dental students‐a systematic review. BMC Oral Health. 2023;23(1):739‐751.
Urbankova A. Impact of computerized dental simulation training on preclinical operative dentistry examination scores. J Dental Educ. 2010;74(4):402‐409.
Shah DY, Dadpe AM, Kalra DD, Garcha VP. Videotaped feedback method to enhance learning in preclinical operative dentistry: An experimental study. J Dental Educ. 2015;79(12);1461‐1466.
Uoshima K, Akiba N, Nagasawa M. Technical skill training and assessment in dental education. Jap Dent Sci Rev. 2021;57:160‐163.
Chambers DW. Board‐to‐board consistency in initial dental licensure examinations. J Dental Educ. 2011;75(10):1310‐1315.
Unal A, Unal Z. An examination of K‐12 teachers' assessment beliefs and practices in relation to years of teaching experience. Geo Educ Res. 2019;16(1):4‐13.
Ali K, Barhom N, Tamimi F, Duggal M. ChatGPT‐A double‐edged sword for healthcare education? Implications for assessments of dental students. Eur J Dent Educ. 2024;28(1):206‐211.
Yamakami SA, Nagai M, Chutinan S, Ohyama H. 3D Digital technology as an alternative educational tool in preclinical dentistry. Eur J Dent Educ. 2022;26(4):733‐740.
Sly MM, Barros JA, Streckfus CF, Arriaga DM, Patel SA. Grading Class I preparations in preclinical dental education: E4D compare software vs. the traditional standard. J Dental Educ. 2017;81(12):1457‐1462.
Zhang X, Liang Y, Li W, et al. Development and evaluation of deep learning for screening dental caries from oral photographs. Oral Dis. 2022;28(1):173‐181.
Kaddoura S, Popescu DE, Hemanth JD. A systematic review on machine learning models for online learning & examination systems. PeerJ Comput Sci. 2022;8:986‐995.
Saghiri MA, Vakhnovetsky J, Samadi E, Amanabi M, Morgano SM, Kredisi CE. Renewing dentistry education with artificial intelligence. J Calif Dent Assoc. 2023;51:1‐11.
Egger J, Gsaxner C, Pepe A, et al. Medical deep learning–a systematic meta‐review. Comput Methods Programs Biomed. 2022;221:106874.
August SE, Tsaima A. Artificial intelligence and machine learning: an instructor's exoskeleton in the future of education. in: Ryoo J, Winkelmann K, eds. Innovative Learning Environments in STEM Higher Education. Springer; 2021:79‐105.
Sandra L, Lumbangaol F, Matsuo T. Machine learning algorithm to predict student's performance: a systematic literature review. TEM J. 2021;10:1919‐1927.
Kim CS, Samaniego CS, Sousa Melo SL, Brachvogel WA, Baskaran K, Rulli D. Artificial intelligence (A.I.) in dental curricula: Ethics and responsible integration. J Dent Educ. 2023;87(11):1570‐1573.
Thurzo A, Strunga M, Urban R, Surovková J, Afrashtehfar KI. Impact of artificial ıntelligence on dental education: A review and guide for curriculum update. Educ Scie. 2023;13(2):150.
Rao GKL, Mokhtar N. Dental education in the information age: Teaching dentistry to generation Z learners using an autonomous smart learning environment. in: Garcia MB, Lopez C, Mildred V, de Almeida Pereira RP, eds. Handbook of Research on Instructional Technologies in Health Education and Allied Disciplines. 1st ed. IGI Global;2023:243‐264.
Chen SL, Zhou HS, Chen TY, et al. Dental shade matching method based on hue, saturation, value color model with machine learning and fuzzy decision. Sens Mater. 2020;32(10):3185‐3207.
Umme S, Akter M, Uddin MS. Image quality assessment through FSIM, SSIM, MSE and PSNR—a comparative study." J Comput Commun. 2019;7(3):8‐18.
Frackiewicz M, Szolc G, Palus H. An improved SPSIM index for image quality assessment. Symmetry. 2021;13(3):518‐531.
Yan F, Min C. An improved method of SSIM based on visual regions of interest. In: IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Ningbo, China, 2015:1‐4.
Zuo Z, Lan X, Deng L, Yao S, Wang X. An improved medical image compression technique with lossless region of interest. Optik. 2015;126(21):2825‐2831.
Silwal S, Wang H, Maldonado D. Assessment of random‐noise contamination in digital images via testing on wavelet coefficients. Statist Interf. 2013;6:117‐135.
Pambrun JF, Noumeir R. Limitations of the SSIM quality metric in the context of diagnostic imaging. In: IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada. 2015:2960‐2963.
Tuncer D, Arhun N, Yamanel K, Celik C, Dayangac B. Dental students’ ability to assess their performance in a preclinical restorative course: comparison of students’ and faculty members’ assessments. J Dent Educ. 2015;79:658‐664.
Nassar HM, Tekian A. Computer simulation and virtual reality in undergraduate operative and restorative dental education: a critical review. J Dent Educ. 2020;84(7):812‐829.
Park SE, Anderson NK, Karimbux NY. OSCE and case presentations as active assessments of dental student performance. J Dent Educ. 2016;80(3):334‐338.
Tricio JA, Woolford MJ, Escudier MP. Fostering dental students' academic achievements and reflection skills through clinical peer assessment and feedback. J Dent Educ. 2016;80(8):914‐923.
Alfakhry G, Mustafa K, Alagha MA, et al. Peer‐assessment ability of trainees in clinical restorative dentistry: can it be fostered? BDJ Open. 2022;8(1):22‐29.
AlHumaid J, Tantawi ME, Al‐Ansari AA, Al‐Harbi FA. Agreement in scoring preclinical dental procedures: impact on grades and instructor‐related determinants. J Dent Educ. 2016;80(5);553‐562.
Fine P, Leung A, Tonni I, Louca C. Dental teacher feedback and student learning: a qualitative study. Dent J. 2023;11(7):164‐176.
Callan RS, Cooper JR, Young NB, Mollica AG, Furness AR, Looney SW. Inter‐ and intrarater reliability using different software versions of E4D compare in dental education. J Dental Educ. 2015;79(6):711‐718.
Bandiaky ON, Lopez S, Hamon L, Clouet R, Soueidan A, Le Guehennec L. Impact of haptic simulators in preclinical dental education: a systematic review. J Dental Educ. 2024:88(3):366‐379.
Hori M, Uematsu Y, Kato A, et al. Identification method for dental alloy type using a cosine similarity program: a preliminary investigation. Dent Mater J. 2023;42(5):723‐731.
Rahimi MH, Vinayahalingam S, Mahmoudinia E, et al. Superresolution of dental panoramic radiographs using deep learning: a pilot study. Diagnostics. 2023;13(5):996‐1009.
Rodrigues P, Nicolau F, Norte M, et al. Preclinical dental students’ self‐assessment of an improved operative dentistry virtual reality simulator with haptic feedback. Sci Rep. 2023;13(1):2823‐2841.
Golkari A, Sabokseir A, Pakshir HR, Dean MC, Sheiham A, Watt RG. A comparison of photographic, replication and direct clinical examination methods for detecting developmental defects of enamel. BMC Oral Health. 2011;11:16‐23.
Peng J, Shi C, Laugeman E, et al. Implementation of the structural SIMilarity (SSIM) index as a quantitative evaluation tool for dose distribution error detection. Med Phys. 2020;47(4):1907‐1919.
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE Trans Imag Process. 2004;13(4):600‐612.
van der Walt S, Schönberger JL, Nunez‐Iglesias J, et al. Scikit‐image: image processing in Python. PeerJ. 2014;2:453‐472.
Virtanen P, Gommers R, Oliphant TE, et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods. 2020;17(3):261‐272.
Chambers DW, Labarre EE. Why professional judgment is better than objective description in dental faculty evaluations of student performance. J Dent Educ. 2014;78(5):681‐693.
Saeed SG, Bain JL, Khoo E, Siqueira WL, van der Hoeven R. Should attendance for preclinical simulation and clinical education be mandatory. J Dent Educ. 2021;85(10);1655‐1663.
Thistlethwaite JE, Davies D, Ekeocha S, et al. The effectiveness of case‐based learning in health professional education. A BEME systematic review: BEME Guide No. 23. Med Teach. 2012;34(6);e421‐e444.
Rung A, George R. A systematic literature review of assessment feedback in preclinical dental education. Eur J Dent Educ. 2021;25(1);135‐150.
Patil S, Bhandi S, Awan KH, et al. Effectiveness of haptic feedback devices in preclinical training of dental students‐a systematic review. BMC Oral Health. 2023;23(1):739‐751.
Urbankova A. Impact of computerized dental simulation training on preclinical operative dentistry examination scores. J Dental Educ. 2010;74(4):402‐409.
Shah DY, Dadpe AM, Kalra DD, Garcha VP. Videotaped feedback method to enhance learning in preclinical operative dentistry: An experimental study. J Dental Educ. 2015;79(12);1461‐1466.
Uoshima K, Akiba N, Nagasawa M. Technical skill training and assessment in dental education. Jap Dent Sci Rev. 2021;57:160‐163.
Chambers DW. Board‐to‐board consistency in initial dental licensure examinations. J Dental Educ. 2011;75(10):1310‐1315.
Unal A, Unal Z. An examination of K‐12 teachers' assessment beliefs and practices in relation to years of teaching experience. Geo Educ Res. 2019;16(1):4‐13.
Ali K, Barhom N, Tamimi F, Duggal M. ChatGPT‐A double‐edged sword for healthcare education? Implications for assessments of dental students. Eur J Dent Educ. 2024;28(1):206‐211.
Yamakami SA, Nagai M, Chutinan S, Ohyama H. 3D Digital technology as an alternative educational tool in preclinical dentistry. Eur J Dent Educ. 2022;26(4):733‐740.
Sly MM, Barros JA, Streckfus CF, Arriaga DM, Patel SA. Grading Class I preparations in preclinical dental education: E4D compare software vs. the traditional standard. J Dental Educ. 2017;81(12):1457‐1462.
Zhang X, Liang Y, Li W, et al. Development and evaluation of deep learning for screening dental caries from oral photographs. Oral Dis. 2022;28(1):173‐181.
Kaddoura S, Popescu DE, Hemanth JD. A systematic review on machine learning models for online learning & examination systems. PeerJ Comput Sci. 2022;8:986‐995.
Saghiri MA, Vakhnovetsky J, Samadi E, Amanabi M, Morgano SM, Kredisi CE. Renewing dentistry education with artificial intelligence. J Calif Dent Assoc. 2023;51:1‐11.
Egger J, Gsaxner C, Pepe A, et al. Medical deep learning–a systematic meta‐review. Comput Methods Programs Biomed. 2022;221:106874.
August SE, Tsaima A. Artificial intelligence and machine learning: an instructor's exoskeleton in the future of education. in: Ryoo J, Winkelmann K, eds. Innovative Learning Environments in STEM Higher Education. Springer; 2021:79‐105.
Sandra L, Lumbangaol F, Matsuo T. Machine learning algorithm to predict student's performance: a systematic literature review. TEM J. 2021;10:1919‐1927.
Kim CS, Samaniego CS, Sousa Melo SL, Brachvogel WA, Baskaran K, Rulli D. Artificial intelligence (A.I.) in dental curricula: Ethics and responsible integration. J Dent Educ. 2023;87(11):1570‐1573.
Thurzo A, Strunga M, Urban R, Surovková J, Afrashtehfar KI. Impact of artificial ıntelligence on dental education: A review and guide for curriculum update. Educ Scie. 2023;13(2):150.
Rao GKL, Mokhtar N. Dental education in the information age: Teaching dentistry to generation Z learners using an autonomous smart learning environment. in: Garcia MB, Lopez C, Mildred V, de Almeida Pereira RP, eds. Handbook of Research on Instructional Technologies in Health Education and Allied Disciplines. 1st ed. IGI Global;2023:243‐264.
Chen SL, Zhou HS, Chen TY, et al. Dental shade matching method based on hue, saturation, value color model with machine learning and fuzzy decision. Sens Mater. 2020;32(10):3185‐3207.
Umme S, Akter M, Uddin MS. Image quality assessment through FSIM, SSIM, MSE and PSNR—a comparative study." J Comput Commun. 2019;7(3):8‐18.
Frackiewicz M, Szolc G, Palus H. An improved SPSIM index for image quality assessment. Symmetry. 2021;13(3):518‐531.
Yan F, Min C. An improved method of SSIM based on visual regions of interest. In: IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Ningbo, China, 2015:1‐4.
Zuo Z, Lan X, Deng L, Yao S, Wang X. An improved medical image compression technique with lossless region of interest. Optik. 2015;126(21):2825‐2831.
Silwal S, Wang H, Maldonado D. Assessment of random‐noise contamination in digital images via testing on wavelet coefficients. Statist Interf. 2013;6:117‐135.
Pambrun JF, Noumeir R. Limitations of the SSIM quality metric in the context of diagnostic imaging. In: IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada. 2015:2960‐2963.
Contributed Indexing:
Keywords: deep machine learning; dental education; haptic skills; reliability; validity
Substance Nomenclature:
0 (Composite Resins)
8049-85-2 (Dental Amalgam)
8049-85-2 (Dental Amalgam)
Entry Date(s):
Date Created: 20240927 Date Completed: 20250424 Latest Revision: 20250424
Update Code:
20250424
DOI:
10.1002/jdd.13722
PMID:
39327675
Database:
MEDLINE
Weitere Informationen
Purpose and Objective: Objective, valid, and reliable evaluations are needed in order to develop haptic skills in dental education. The aim of this study is to investigate the validity and reliability of the machine learning method in evaluating the haptic skills of dentistry students.
Materials and Methods: One-hundred fifty 6th semester dental students have performed Class II amalgam (C2A) and composite resin restorations (C2CR), in which all stages were evaluated with Direct Observation Practical Skills forms. The final phase was graded by three trainers and supervisors separately. Standard photographs of the restorations in the final stage were taken from different angles in a special setup and transferred to the Python program which utilized the Structural Similarity algorithm to calculate both the quantitative (numerical) and qualitative (visual) differences of each restoration. The validity and reliability analyses of inter-examiner evaluation were tested by Cronbach's Alpha and Kappa statistics (p = 0.05).
Results: The intra-examiner reliability between Structural Similarity Index (SSIM) and examiners was found highly reliable in both C2A (α = 0.961) and C2CR (α = 0.856). The compatibility of final grades given by SSIM (53.07) and examiners (56.85) was statistically insignificant (p > 0.05). A significant difference was found between the examiners and SSIM when grading the occlusal surfaces in C2A and on the palatal surfaces of C2CR (p < 0.05). The concordance of observer assessments was found almost perfect in C2A (κ = 0.806), and acceptable in C2CR (κ = 0.769).
Conclusion: Although deep machine learning is a promising tool in the evaluation of haptic skills, further improvement and alignments are required for fully objective and reliable validation in all cases of dental training in restorative dentistry.
(© 2024 American Dental Education Association.)