International journal of dentistry · 2024
Dental Cavity Grading: Comparing Algorithm Reliability and Agreement with Expert Evaluation.
СтатьяНазвание и аннотация: Перевод в очереди
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Qutieshat Abubaker, Salem Abdurahman, Kyranides Melina N
Аннотация
AIM: The current study introduces a novel, algorithm-based software developed to objectively evaluate dental cavity preparations. The software aims to provide an alternative or complement to traditional, subjective assessment methods used in operative dentistry education.
MATERIALS AND METHODS: The software was tested on cavity preparations carried out by 70 participants on artificial molar teeth. These cavities were also independently assessed by an experienced academic panel. The software, using 3D imaging, calculated cavity dimensions and assigned an error score based on deviation from ideal measurements. Statistical analyses included sensitivity, specificity, positive predictive value, negative predictive value, Cohen's kappa, the intraclass correlation coefficient (ICC3k), Spearman's rho, Kendall's tau correlation coefficients, and a confusion matrix.
RESULT: The software demonstrated a high degree of accuracy and agreement with the panel assessments. The average software and panel scores were 64.1 and 60.91, respectively. Sensitivity (0.98) was high, specificity (0.55) was moderate, and the ICC3k value (0.857) indicated a strong agreement between the software and the panel. Further, Spearman's rho (0.73) and Kendall's tau (0.56) suggested a strong correlation between the two grading methods.
CONCLUSION: The results support the algorithm-based software as a valid and reliable tool for dental cavity preparation assessments. The software's potential use in dental education is promising, though future research is necessary to validate and optimize this technology for wider application.
MATERIALS AND METHODS: The software was tested on cavity preparations carried out by 70 participants on artificial molar teeth. These cavities were also independently assessed by an experienced academic panel. The software, using 3D imaging, calculated cavity dimensions and assigned an error score based on deviation from ideal measurements. Statistical analyses included sensitivity, specificity, positive predictive value, negative predictive value, Cohen's kappa, the intraclass correlation coefficient (ICC3k), Spearman's rho, Kendall's tau correlation coefficients, and a confusion matrix.
RESULT: The software demonstrated a high degree of accuracy and agreement with the panel assessments. The average software and panel scores were 64.1 and 60.91, respectively. Sensitivity (0.98) was high, specificity (0.55) was moderate, and the ICC3k value (0.857) indicated a strong agreement between the software and the panel. Further, Spearman's rho (0.73) and Kendall's tau (0.56) suggested a strong correlation between the two grading methods.
CONCLUSION: The results support the algorithm-based software as a valid and reliable tool for dental cavity preparation assessments. The software's potential use in dental education is promising, though future research is necessary to validate and optimize this technology for wider application.