Performance of a residual neural network system versus dental technicians for gingival porcelain shade matching

PURPOSE: This study aimed to compare the accuracy of a residual neural network (ResNet) system with experienced dental technicians for gingival shade prediction. MATERIALS AND METHODS: were statistically analyzed using paired t-tests (overall comparison) and repeated measures analyses (regional comparison). One-sample t-tests compared values to acceptability (AT = 2.8) and perceptibility (PT = 1.1) thresholds (α = 0.05). RESULTS: (4.169 ± 2.048) compared to technicians (5.625 ± 1.967; p < 0.001), although both exceeded the acceptability threshold (AT = 2.8). Regionally, ResNet outperformed technicians in the middle (3.486 ± 1.310 vs. 5.724 ± 2.074; p < 0.001) and lower zones (3.509 ± 2.142 vs. 5.023 ± 1.883; p = 0.007), but not in the upper zone (5.511 ± 1.978 vs. 6.129 ± 1.886; p = 0.317). CONCLUSIONS: values for both methods exceeded clinical acceptability.