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The background of the study highlighted the common occurrence of post-endodontic pain and the need for effective pain management strategies. Materials and Methods: Fifty patients were randomly assigned to two groups: the cold laser therapy (CLT) group and the traditional methods group (TMG). The CLT group received laser treatment at the affected area immediately following endodontic treatment, whereas the TMG received standard pain management techniques such as analgesics. Pain levels were assessed using a visual analog scale at baseline and at 6, 12, 24, and 48 hours post treatment. Pain scores were analyzed using appropriate statistical methods, including analysis of variance, and P values were calculated to determine the significance of differences between groups. Results: This study found significant differences in post-endodontic pain levels between CLT and standard procedures. At 6 hours post treatment, the CLT group had a mean pain score of 2.1 ± 0.8, whereas the TMG had a mean pain score of 3.8 ± 1.2 (P = 0.012). Pain levels in the CLT group decreased with time, with scores decreasing to 1.5 ± 0.6 at 12 hours, 1.2 ± 0.5 at 24 hours, and 0.9 ± 0.4 at 48 hours post treatment. In contrast, the TMG had greater pain scores at each time point: 3.2 ± 1.0, 2.9 ± 1.1, 2.5 ± 0.9, and 2.1 ± 0.8 at 12, 24, and 48 hours post treatment, respectively. At 12, 24, and 48 hours post treatment, P values of 0.021, 0.036, and 0.004, respectively, indicated significant differences. Conclusion: In managing post-endodontic pain, CLT demonstrated superior efficacy compared to traditional methods. [ABSTRACT FROM AUTHOR]

Background: Orthodontic treatment planning involves the precise assessment of dental and skeletal anomalies, which can be facilitated by AI-enhanced diagnostic tools. Materials and Methods: A total of 100 orthodontic cases were included in this RCT. Patients were randomly assigned to two groups: an AI-enhanced diagnostic group and a traditional diagnostic group. The AI-enhanced diagnostic group underwent orthodontic assessment with the aid of AI-powered software, which provided automated cephalometric analysis, 3D model evaluations, and treatment suggestions. The traditional diagnostic group received conventional diagnostic assessments by orthodontists. The primary outcome measures included treatment planning accuracy, treatment time, and patient satisfaction. Secondary outcomes included the number of appointments required and treatment cost. Results: The AI-enhanced diagnostic group demonstrated a significantly higher accuracy in treatment planning compared to the traditional diagnostic group (P < 0.05). The AI group also required fewer appointments (mean ± SD: 10.2 ± 2.1 vs. 12.8 ± 3.4) and had a shorter treatment time (mean ± SD: 14.6 ± 3.2 months vs. 18.9 ± 4.5 months) (P < 0.001 for both comparisons). Additionally, patient satisfaction scores were higher in the AI group (mean ± SD: 9.2 ± 0.6 vs. 8.1 ± 0.8) (P < 0.001). However, the AI-enhanced diagnostic group had a slightly higher treatment cost. Conclusion: AI-enhanced diagnostic tools significantly enhance the accuracy of treatment planning in orthodontic cases, leading to reduced treatment time, fewer appointments, and increased patient satisfaction. [ABSTRACT FROM AUTHOR]