Developing an All‐Digital Workflow for Dental Skills Assessment: Part II, Surface Analysis, Benchmarking, and Grading

Abstract

In Part I of this study, evidence was presented that visual inspection (VI) of dental student waxups exhibited low precision (ICC=0.332) and accuracy, resulting in questionable grade validity. VI inappropriately assesses morphology in part due to oversimpliied grading rubrics and inappropriate application of ratio measurement to ordinal data. The aim of Part II of this study was to develop, apply, and compare with VI a digital assessment worklow and report the outcomes. After inclusion criteria were met, 67 (83%) student waxups were scanned with an intraoral scanner and analyzed using open source surface comparison software. Each was digitally compared to the homologous standard typodont tooth. Percentage contour variation within a stipulated tolerance was computed. The acceptable tolerance was derived from clinical literature reporting how contour deviations impact the biological and biomechanical stability of teeth and periodontium. Surface roughness of each waxup was also computed and compared with the reference teeth. A formula for digital assessment was developed and applied to each waxup. On average, digital grades exceeded the VI assessments, while correcting individual errors for greater accuracy and precision (ICC=0.866). Students were able to easily apply the digital worklow for visualization and accurate self-evaluation. In this study, a digital grading worklow was developed that used portable data formats and freely available open source analysis software, so the method could be introduced at any dental school where intraoral scanning is available.

Digital Undergraduate Education in Dentistry: A Systematic Review

“…Especially in the field of motor skills training, digital software tools can be used to evaluate the manual abilities of potential candidates for the dental curriculum, to analyze students' preclinical preparations, to enable self-assessment, and to enhance the quality of education. The objective and exact nature of these digital evaluations helps to improve students' visualization, provides immediate feedback, and enhances instructor evaluation and student self-evaluation and self-correction [43,94]. Students can learn to self-assess their work with self-reflection and faculty guidance in conjunction with a specially designed digital evaluation tool [48].…”

Big Data in Dental Research and Oral Healthcare

“…Especially in the field of motor skills training, digital software tools can be used to evaluate the manual abilities of potential candidates for the dental curriculum, to analyze students' preclinical preparations, to enable self-assessment, and to enhance the quality of education. The objective and exact nature of these digital evaluations helps to improve students' visualization, provides immediate feedback, and enhances instructor evaluation and student self-evaluation and self-correction [43,94]. Students can learn to self-assess their work with self-reflection and faculty guidance in conjunction with a specially designed digital evaluation tool [48].…”

Comparison between students’ self‐assessment, and visual and digital assessment techniques in dental anatomy wax‐up grading

“…One previous study in the literature aimed to determine the ideal software tolerance; Mcpherson et al in 2015 compared visual grades to digital grades with a wide range of tolerances (200–800) at 25‐µm increments 11 . A few other studies aimed at revealing correlations between visual grades and digital grades at pre‐determined tolerance values of 250, 300, 350, 380, 400 and 500 µm with varying results 9,11,14‐16 . Several considerations are at play when determining the “ideal tolerance”: 1. different institutions follow different grading criteria and 2. the method used to generate the “master model” against which the samples are being graded.…”