ABSTRACT
Background/purpose: The need for dental emergency (DE) services has increased in recent years. This study therefore investigated the characteristics of patients presenting with DEs in a medical center in southern Taiwan. Materials and methods: This was a retrospective study of 1964 adult patients who presented with a DE at the Kaohsiung Medical University Hospital in 2018. Medical records providing age, sex, time, day, past visit history, chief complaint, diagnosis, and treatment were collected and analyzed. Results: The results revealed that men constituted 52.4% of the patients with DEs, the average age was 45.6 years, and the age distribution peak was 20-29 years (26.5%). The peak period for the DE visit was between 17:00 and 24:00 (42.1%), and the peak day of the week was Sunday (27.4%), followed by Saturday (18.0%). The most common chief complaint was pain (49.8%), and the diagnoses were as follows: pulp-related problems (36.7%), periodontal-related problems (22.9%), trauma (22.2%), odontogenic infection (15.3%), postoperative complications (9.2%), and temporomandibular disorders (3.7%). Dental treatment and medication were prescribed for 51.9% of the patients with DE. The rate of patients recommended for further dental treatment was 86.8%, and the actual return rate was 40.1%. Conclusion: This study revealed that the top three reasons for adult DE visits were pulp-related problems, periodontal-related problems, and trauma. These results may be used as a reference for dentists who provide DE services.
ABSTRACT
AIM: To evaluate the effects of an at-home artificial intelligence (AI)-assisted dental monitoring application on treatment outcomes in patients with periodontitis. MATERIALS AND METHODS: Participants with periodontitis were recruited and randomly assigned to an AI (n = 16), AI and human counselling (AIHC; n = 17), or control (CG; n = 20) group. All participants received non-surgical periodontal treatment. We employed an AI-assisted tool called DENTAL MONITORING® (DM) intervention, a new technological AI monitoring product that utilizes smartphone cameras for intra-oral scanning and assessment. Patients in the AI and AIHC groups received additional (a) DM or (b) DM, respectively, with real-person counselling over 3 months. Periodontal parameters were collected at baseline and follow-ups. A mixed-design model analysed the follow-up effects over time. RESULTS: The AI and AIHC groups, respectively, exhibited greater improvement in probing pocket depth (PPD) (mean diff = -0.9 ± 0.4 and -1.4 ± 0.3, effect size [ES] = 0.76 and 1.98), clinical attachment level (mean diff = -0.8 ± 0.3 and -1.4 ± 0.3, ES = 0.84 and 1.77), and plaque index (mean diff = -0.5 ± 0.2 and - 0.7 ± 0.2, ES = 0.93 and 1.81) at 3-month follow-up than the CG did. The AIHC group had a greater reduction in PPD (ES = 0.46) and clinical attachment level (ES = 0.64) at the 3-month follow-up compared with the AI group. CONCLUSIONS: Using AI monitoring at home had a positive effect on treatment outcomes for patients with periodontitis. Patients who received AI-assisted health counselling exhibited better treatment outcomes than did patients who received AI monitoring alone.