PURPOSE: The purpose of this systematic review was to compare the cost-effectiveness and PROMs between digitally and conventionally fabricated complete dentures.
MATERIAL AND METHODS: An electronic search of publications from 2011 to mid-2023 was established using PubMed/Medline, EBSCOhost, and Google Scholar. Retrospective, prospective, randomized controlled, and randomized crossover clinical studies on at least 10 participants were included. A total of 540 articles were identified and assessed at the title, abstract, and full article level, resulting in the inclusion of 14 articles. Data on cost, number of visits, patient satisfaction, and oral health-related quality of life were examined and reported.
RESULTS: The systematic review included 572 digitally fabricated complete dentures and 939 conventionally fabricated complete dentures inserted in 1300 patients. Digitally fabricated complete dentures require less clinical time with a lower total cost, despite higher material costs compared with the conventional fabrication technique. Digitally and conventionally fabricated complete dentures were found to have significant effects on mastication efficiency, comfort, retention, stability, ease of cleaning, phonetics, and overall patient satisfaction, as well as social disability, functional limitation, psychological discomfort, physical pain, and handicap.
CONCLUSIONS: Digitally fabricated complete dentures are more cost-effective than conventionally fabricated dentures. There are various impacts of conventionally and digitally fabricated complete dentures on PROMs, and they are not better than one another.
METHODS: A stone dental cast was scanned with a laboratory scanner as a reference, with 11 scans performed by an IOS, SMP_2A, and SMP_3A. In 3D analysis, trueness and precision were evaluated through superimposition with the reference scan and within each group, respectively, using the best-fit algorithm of Geomagic Wrap software (3D Systems, Inc., Rock Hill, SC). Trueness in linear discrepancy was assessed by comparing the occlusal-cervical and mesiodistal dimensions of reference teeth (canine, premolar, and molar), intercanine width, and intermolar width on the digital casts to measurements of the stone cast, while precision was measured using the coefficient of variance. Differences between groups were analyzed using the Friedman test, followed by the Dunn-Bonferroni post hoc test with a significance level set at 0.05.
RESULTS: IOS exhibited significantly lower trueness than SMP_2A (p = 0.003) with significantly greater width discrepancies on canines (p = 0.001) and molars (p < 0.001). Discrepancy patterns differed among the three scanning methods. The IOS showed greater discrepancies on the occlusal surfaces of posterior teeth. While SMP_3A demonstrated higher variation on the palatal surfaces and interproximal areas of posterior teeth. For precision, SMP_3A (p = 0.028) and SMP_2A (p = 0.003) showed a significantly lower precision in 3D analysis, but a comparable reproducibility in linear measurement to IOS.
CONCLUSION: TRIOS IOS (3Shape, Copenhagen, Denmark) exhibited lower trueness in 3D and linear accuracy analyses for complete-arch scans. The positions of the smartphone significantly enhanced trueness at the undercut region. SMP_2A and SMP_3A can be a potential alternative for precise linear measurement in complete-arch scans with selective use.
METHODS: Tooth wear status of NPC survivors were clinically assessed using the Exact Tooth Wear Index. A tooth was graded to have severe wear when more than one-third of its buccal/occlusal/lingual surface had dentine loss. At the subject-level, percentages of anterior/posterior/all teeth with severe wear were calculated. Age, number of teeth, flow-rate/buffering capacity/pH of stimulated whole (SWS) and parotid (SPS) saliva's were collected. Correlation and multiple-linear regression tests were performed at the significance level α = 0.05.
RESULT: Sixty-eight participants (mean age of 60.0 ± 8.9), 697 anterior and 686 posterior teeth were examined with a mean of 10-years post-radiotherapy. Severe tooth wear was found in 63 (92.6 percent) participants, 288 anterior and 83 posterior teeth. The mean percentage of anterior/posterior/all teeth with severe wear were 42.3 ± 28.1, 14.5 ± 19.9 and 30.0 ± 21.7. Anterior teeth, particularly the incisal surface of central incisors were most affected. The mean flow-rate of SWS and SPS were 0.1 ± 0.1 ml/min and 0.03 ± 0.07 ml/min respectively. Thirty (44.1 percent) and 48 (70.6 percent) participants were found to have low/no buffering capacity of SWS and SPS respectively. Multiple-regression analyses revealed the SWS flow-rate was associated with the percentage of anterior teeth with severe wear (p=0.03).
CONCLUSION: Anterior tooth wear is a significant dental problem among NPC survivors and was associated with hypo-salivation.
CLINICAL SIGNIFICANCE: Patients with hypo-salivation should be being monitored for tooth wear particularly on the anterior teeth.
OBJECTIVE: This study aims to determine the prevalence of musculoskeletal disorders among dentists, explore the risk factors and identify the ergonomic preventive measures for dental professionals.
METHODS: Articles published between 2008-2020 were searched in scientific databases (MEDLINE, PubMed, Scopus and Cochrane Library). The Critical Appraisal Skills Programme Systematic Review Checklist was used to assess the quality of the studies.
RESULTS: Eighteen studies were found to be suitable in the final review. Relevant data was extracted and summarized from the included studies. The annual prevalence of musculoskeletal disorders in any body site ranged between 68% and 100%. The most predominant regions for musculoskeletal disorders among dental professionals were identified to be the lower back (29% to 94.6%), shoulder (25% to 92.7%), and neck (26% to 92%). The most frequently reported risk factors of MSDs were the individual characteristic female gender (57.1%), followed by awkward working postures (50%), long working experience (50%) and being dental specialists (42.9%). Several preventive measures were identified as the most effective ways in preventing MSDs, the use of magnification (40%) and regular physical activity (40%).
CONCLUSIONS: This review reported a high prevalence of musculoskeletal disorders (MSD) among dentists. It critically updates and adds the latest evidence on occupational ergonomics among dentists.
PURPOSE: The purpose of this study was to develop and validate a novel instrument, termed the questionnaire on perceived prosthodontic treatment needs (PPTN), that assesses perceived prosthodontic treatment needs in adults.
MATERIAL AND METHODS: The PPTN was developed following a literature review, consultation with healthcare workers, and patient interviews. It included 15 questions and a self-rated need for prosthodontic treatment, categorized on a Likert scale. A cross-sectional descriptive study was completed on 193 dental patients seeking or receiving prosthodontic treatment.
RESULTS: Three perceived prosthodontic treatment need factors were identified (psychosocial impact, esthetic concern, and function) by using exploratory factor analysis. A higher PPTN score indicated greater perceived prosthodontic treatment needs. The identified factors represent 67.8% of the variance with eigenvalues of >1. The PPTN had a high degree of internal consistency and reliability, as the final questionnaire received a Cronbach alpha of 0.75 and an intraclass coefficient of 0.75 with a 95% confidence interval of 0.68 to 0.80 (F(192, 576)=3.94, P
METHODS: Frontal view intraoral photographs fulfilling selection criteria were collected. Along the gingival margin, the gingival conditions of individual sites were labelled as healthy, diseased, or questionable. Photographs were randomly assigned as training or validation datasets. Training datasets were input into a novel artificial intelligence system and its accuracy in detection of gingivitis including sensitivity, specificity, and mean intersection-over-union were analysed using validation dataset. The accuracy was reported according to STARD-2015 statement.
RESULTS: A total of 567 intraoral photographs were collected and labelled, of which 80% were used for training and 20% for validation. Regarding training datasets, there were total 113,745,208 pixels with 9,270,413; 5,711,027; and 4,596,612 pixels were labelled as healthy, diseased, and questionable respectively. Regarding validation datasets, there were 28,319,607 pixels with 1,732,031; 1,866,104; and 1,116,493 pixels were labelled as healthy, diseased, and questionable, respectively. AI correctly predicted 1,114,623 healthy and 1,183,718 diseased pixels with sensitivity of 0.92 and specificity of 0.94. The mean intersection-over-union of the system was 0.60 and above the commonly accepted threshold of 0.50.
CONCLUSIONS: Artificial intelligence could identify specific sites with and without gingival inflammation, with high sensitivity and high specificity that are on par with visual examination by human dentist. This system may be used for monitoring of the effectiveness of patients' plaque control.