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

Abstract 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 CIELab ( L *, a *, b *) coordinates were measured from three adjacent 1 × 3 mm 2 zones within the gingival region of 18 volunteers using a spectrophotometer (Crystaleye, Olympus). Zirconia‐based prosthetic specimens (1.5‐mm thickness) for the upper right central incisor were digitally designed and milled, with one fabricated using conventional visual shade matching (CZR; Kuraray Noritake Dental) by experienced technicians and the other using a ResNet‐based shade prediction system. Δ E 00 values were calculated relative to natural gingiva using the CIEDE2000 formula. Δ E 00 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 The ResNet‐based system demonstrated significantly lower overall Δ E 00 (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 The ResNet‐based system demonstrated statistically better shade‐matching performance compared to experienced dental technicians for gingival shade matching, both in subjective evaluation and objective Δ E 00 measurement, particularly in the middle and lower gingival zones. However, Δ E 00 values for both methods exceeded clinical acceptability.