Clinical feasibility of deep learning-based automatic head CBCT image segmentation and landmark detection in computer-aided surgical simulation for orthognathic surgery.

The purpose of this ambispective study was to investigate whether deep learning-based automatic segmentation and landmark detection, the SkullEngine, could be used for orthognathic surgical planning. Sixty-one sets of cone beam computed tomography (CBCT) images were automatically inferred for midface, mandible, upper and lower teeth, and 68 landmarks. The experimental group included automatic segmentation and landmarks, while the control group included manual ones that were previously used to plan orthognathic surgery. The qualitative analysis of segmentation showed that all of the automatic results could be used for computer-aided surgical simulation. Among these, 98.4% of midface, 70.5% of mandible, 98.4% of upper teeth, and 93.4% of lower teeth could be directly used without manual revision. The Dice similarity coefficient was 96% and the average symmetric surface distance was 0.1 mm for all four structures. With SkullEngine, it took 4 minutes to complete the automatic segmentation and an additional 10 minutes for a manual touchup. The results also showed the overall mean difference between the two groups was 2.3 mm for the midface and 2.4 mm for the mandible. In summary, the authors believe that automatic segmentation using SkullEngine is ready for daily practice. However, the accuracy of automatic landmark digitization needs to be improved.

[Association between high expression of intrahepatic programmed death-1 and liver inflammation in patients with autoimmune hepatitis].

Objective: To investigate the expression of programmed death-1 (PD-1) in liver tissue and its association with liver pathology in patients with autoimmune hepatitis (AIH). Methods: A total of 54 AIH patients (38 in the active stage and 16 in the remission stage) were enrolled, and 9 healthy volunteers were enrolled as control group. Immunohistochemistry combined with quantitative image analysis was used to measure the expression of PD-1 in liver tissue. The t-test, rank sum test, one-way analysis of variance, least significant difference t-test, Mann-Whitney U test, and Pearson relation analysis were used for statistical analysis of different types of data. Results: The AIH group had a significantly higher positive rate of PD-1 in liver tissue than the control group (13.57%±6.84% vs 2.22%±0.66%, P < 0.01), and the patients in the active stage of AIH had a significantly higher positive rate of PD-1 in liver tissue than those in the remission stage (16.53%±7.72% vs 6.56%±3.16%, P < 0.01). The positive rate of PD-1 in liver tissue was 6.56%±3.16% in G0 group, 14.33%±5.08% in G1-2 group, and 19.23%±5.41% in G3-4 group (P < 0.01), but there was no significant difference in the positive rate of PD-1 between S0, S1-2, and S3-4 groups (P > 0.05). In AIH patients, the positive rate of PD-1 in liver tissue was positively correlated with the levels of total bilirubin, alanine aminotransferase, aspartate aminotransferase, and IgG (r = 0.665, 0.721, 0.711, and 0.813, all P < 0.01). Conclusion: AIH patients have regulated PD-1 expression in liver tissue, which is closely associated with liver inflammation and is not associated with fibrosis degree, suggesting that PD-1 is involved in the development and progression of inflammation in AIH patients.