Cephalometric analysis performance discrepancy between orthodontists and an artificial intelligence model using lateral cephalometric radiographs

Abstract

PurposeThe purpose of the present clinical study was to compare the Ricketts and Steiner cephalometric analysis obtained by two experienced orthodontists and artificial intelligence (AI)‐based software program and measure the orthodontist variability.Materials and MethodsA total of 50 lateral cephalometric radiographs from 50 patients were obtained. Two groups were created depending on the operator performing the cephalometric analysis: orthodontists (Orthod group) and an AI software program (AI group). In the Orthod group, two independent experienced orthodontists performed the measurements by performing a manual identification of the cephalometric landmarks and a software program (NemoCeph; Nemotec) to calculate the measurements. In the AI group, an AI software program (CephX; ORCA Dental AI) was selected for both the automatic landmark identification and cephalometric measurements. The Ricketts and Steiner cephalometric analyses were assessed in both groups including a total of 24 measurements. The Shapiro–Wilk test showed that the data was normally distributed. The t‐test was used to analyze the data (α = 0.05).ResultsThe t‐test analysis showed significant measurement discrepancies between the Orthod and AI group in seven of the 24 cephalometric parameters tested, namely the corpus length (p = 0.003), mandibular arc (p < 0.001), lower face height (p = 0.005), overjet (p = 0.019), and overbite (p = 0.022) in the Ricketts cephalometric analysis and occlusal to SN (p = 0.002) and GoGn‐SN (p < 0.001) in the Steiner cephalometric analysis. The intraclass correlation coefficient (ICC) between both orthodontists of the Orthod group for each cephalometric measurement was calculated.ConclusionsSignificant discrepancies were found in seven of the 24 cephalometric measurements tested between the orthodontists and the AI‐based program assessed. The intra‐operator reliability analysis showed reproducible measurements between both orthodontists, except for the corpus length measurement.Clinical SignificanceThe artificial intelligence software program tested has the potential to automatically obtain cephalometric analysis using lateral cephalometric radiographs; however, additional studies are needed to further evaluate the accuracy of this AI‐based system.

Reliability and Agreement of Free Web-Based 3D Software for Computing Facial Area and Volume Measurements

“…During this test, four measurements surfaced with statistically noteworthy p-values, prompting further investigation into their skewness and kurtosis metrics, which ultimately were ascertained to be within the conventional thresholds for a normal distribution. Consequently, there was no significant evidence to suggest a deviation from normality across the dataset [28,29,60,61].…”

“…A nonsignificant result (p-value greater than alpha level) indicates that the normality assumption has not been violated. If the data significantly deviate from a normal distribution, the results of the ICC may not be reliable [28,29].…”

“…If the p-value is greater than the alpha level, the null hypothesis of equal variances is not rejected, suggesting that the assumption of homoscedasticity is reasonable. A significant result from Levene's test indicates that the variances are not equal (heteroscedasticity), which is a violation of one of the key assumptions for certain statistical tests, including some types of ICCs [29,31].…”