The study aimed at developing a deep-learning (DL)-based algorithm to predict the virtual soft tissue profile after mandibular advancement surgery, and to compare its accuracy with the mass tensor model (MTM). Subjects who underwent mandibular advancement surgery were enrolled and divided into a training group and a test group. The DL model was trained using 3D photographs and CBCT data based on surgically achieved mandibular displacements (training group). Soft tissue simulations generated by DL and MTM based on the actual surgical jaw movements (test group) were compared with soft-tissue profiles on postoperative 3D photographs using distance mapping in terms of mean absolute error in the lower face, lower lip, and chin regions. 133 subjects were included - 119 in the training group and 14 in the test group. The mean absolute error for DL-based simulations of the lower face region was 1.0 ± 0.6 mm and was significantly lower (p = 0.02) compared with MTM-based simulations (1.5 ± 0.5 mm). CONCLUSION: The DL-based algorithm can predict 3D soft tissue profiles following mandibular advancement surgery. With a clinically acceptable mean absolute error. Therefore, it seems to be a relevant option for soft tissue prediction in orthognathic surgery. Therefore, it seems to be a relevant options.

The use of artificial intelligence (AI) in dentistry is rapidly evolving and could play a major role in a variety of dental fields. This study assessed patients’ perceptions and expectations regarding AI use in dentistry. An 18-item questionnaire survey focused on demographics, expectancy, accountability, trust, interaction, advantages and disadvantages was responded to by 330 patients; 265 completed questionnaires were included in this study. Frequencies and differences between age groups were analysed using a two-sided chi-squared or Fisher’s exact tests with Monte Carlo approximation. Patients’ perceived top three disadvantages of AI use in dentistry were (1) the impact on workforce needs (37.7%), (2) new challenges on doctor–patient relationships (36.2%) and (3) increased dental care costs (31.7%). Major expected advantages were improved diagnostic confidence (60.8%), time reduction (48.3%) and more personalised and evidencebased disease management (43.0%). Most patients expected AI to be part of the dental workflow in 1–5 (42.3%) or 5–10 (46.8%) years. Older patients (> 35 years) expected higher AI performance standards than younger patients (18–35 years) (p < 0.05). Overall, patients showed a positive attitude towards AI in dentistry. Understanding patients’ perceptions may allow professionals to shape AI-driven dentistry in the future.