ObjectivesTo compare traditional plaster casts, digital models and 3D printed copies of dental plaster casts based on various criteria. To determine whether 3D printed copies obtained using open source system RepRap can replace traditional plaster casts in dental practice. To compare and contrast the qualities of two possible 3D printing options – open source system RepRap and commercially available 3D printing.Design and settingsA method comparison study on 10 dental plaster casts from the Orthodontic department, Department of Stomatology, 2nd medical Faulty, Charles University Prague, Czech Republic.Material and methodsEach of 10 plaster casts were scanned by inEos Blue scanner and the printed on 3D printer RepRap [10 models] and ProJet HD3000 3D printer [1 model]. Linear measurements between selected points on the dental arches of upper and lower jaws on plaster casts and its 3D copy were recorded and statistically analyzed.Results3D printed copies have many advantages over traditional plaster casts. The precision and accuracy of the RepRap 3D printed copies of plaster casts were confirmed based on the statistical analysis. Although the commercially available 3D printing enables to print more details than the RepRap system, it is expensive and for the purpose of clinical use can be replaced by the cheaper prints obtained from RepRap printed copies.ConclusionsScanning of the traditional plaster casts to obtain a digital model offers a pragmatic approach. The scans can subsequently be used as a template to print the plaster casts as required. Using 3D printers can replace traditional plaster casts primarily due to their accuracy and price.

The laser therapy was effective in the improvement of the range of temporomandibular disorders (TMD) and promoted a significant reduction of pain symptoms.

BackgroundStudies of children with very low birth weight (VLBW, 1,000–1,500 g) and extremely low birth weight (ELBW, less than 1,000 g) indicate that this population seems to be at increased risk of autism spectrum disorder (ASD).MethodsParents of 101 VLBW and ELBW children (age 2 years, corrected for prematurity) agreed to participate in the study and signed informed consents; however, parents of only 75 children (44 boys, 31 girls) completed the screening questionnaires. The screening battery included the Modified Checklist for Autism in Toddlers (M-CHAT), Communication and Symbolic Behavior Scales Developmental Profile Infant-Toddler Checklist (CSBS-DP-ITC), and the Infant/Toddler Sensory Profile (ITSP). Children with disabilities were excluded. All children who screened positive on any of the screening tools were subsequently invited for a detailed assessment.ResultsThirty-two children (42.7%) screened positive on at least one of the screening questionnaires. The screening tool with the most positive results was the CSBS-DP-ITC (26 positive screens), followed by the M-CHAT (19 positive screens) and the ITSP (11 positive screens). Of the 32 children who tested positive, 19 participated in the detailed follow-up assessment. A diagnosis of ASD was confirmed in eight of the 19 children. ASD prevalence, calculated from those 19 children and those with negative screening results (43 children), yielded a prevalence of 12.9% in the sample. The difference in frequency of positive screens between the tests was significant (P=0.011). In pair comparisons, ITSP was found to be significantly less positive than CSBS-DP-ITC (P=0.032). No significant differences were found between the M-CHAT and CSBS-DP-ITC or between the M-CHAT and ITSP.ConclusionThe results strongly support the hypothesis of an increased prevalence of autism in children with a birth weight less than 1,500 g.

BackgroundPreterm children seem to be at increased risk for autism spectrum disorders (ASD).MethodsParents of 157 children with birth weights less than 1,500 g (age 2 years, corrected for prematurity; 88 boys, 69 girls) completed screening questionnaires. The screening battery included the Modified Checklist for Autism in Toddlers (M-CHAT), Communication and Symbolic Behavior Scales Developmental Profile Infant-Toddler Checklist (CSBS-DP-ITC), and the Infant/Toddler Sensory Profile (ITSP). Children with disabilities were excluded. All children who screened positive on any of the screening tools were subsequently assessed by clinical examination including the Autism Diagnostic Observation Schedule.ResultsFifty-six children (35.7%) screened positive on at least one of the parental screening questionnaires. Of the 56 children who tested positive, 33 participated in the detailed clinical follow-up assessment. A diagnosis of ASD was confirmed in 13 of the 33 children. The ASD prevalence was 9.7% of the sample. Analysis of children with and without an ASD diagnosis found significant differences relative to gestational age (26.9 weeks vs 28.3 weeks, P=0.033) and length of the stay in hospital (89.5 days vs 75.4 days, P=0.042). The screening tool with the most positive results was CSBS-DP-ITC (42 positive screens [PS]), followed by M-CHAT (28 PS), and ITSP (22 PS). Differences in the frequency of PS among the tests were significant (P=0.008). CSBS-DP-ITC had the highest sensitivity (0.846), followed by M-CHAT (0.692) and ITSP (0.462).ConclusionOur results indicate a higher prevalence of autism in children with birth weights <1,500 g at 2 years of age compared to the general population prevalence. The ASD diagnosis was associated with shorter gestation times and longer hospital stays. Our findings support the simultaneous use of more than one screening tests in order to increase screening sensitivity.

The purpose of this study is (1) to introduce a new approach for edge detection in orthopantograms (OPGs) and an improved automatic parameter selector for common edge detectors, (2) to present a comparison between our novel approach with common edge detectors and (3) to provide faster outputs without compromising quality. A new approach for edge detection based on statistical measures was introduced: (1) a set of N edge detection results is calculated from a given input image and a selected type of edge detector, (2) N correspondence maps are constructed from N edge detection results, (3) probabilities and average probabilities are computed, (4) an overall correspondence is evaluated for each correspondence map and (5) the correspondence map providing the best overall correspondence is taken as the result of edge detection procedure. A comparison with common edge detectors (the Roberts, Prewitt, Sobel, Laplacian of the Gaussian and Canny methods) with various parameter settings (304 combinations for each test image) was carried out. The methods were assessed objectively [edge mismatch error (EME), modified Hausdorff distance (MHD) and principal component analysis] and subjectively by experts in dentistry and based on time demands. The suitability of the new approach for edge detection in OPGs was confirmed by experts. The current conventional methods in edge detection in OPGs are inadequate (none of the tested methods reach an EME value or MHD value below 0.1). Our proposed approach for edge detection shows promising potential for its implementation in clinical dentistry. It enhances the accuracy of OPG interpretation and advances diagnosis and treatment planning.