Clinical Evaluation for Space Maintainer after Unilateral Loss of Primary First Molar in the Early Mixed Dentition Stage

Background and Objectives: Controversy exists regarding the need for a space maintainer after early unilateral loss of a primary first molar. This study aimed to assess the need for a space maintainer after unilateral loss of a primary first molar in the early mixed dentition period. Materials and Methods. In this cross-sectional study, fifty children between 6 and 8 years who had lost a primary first molar unilaterally later than 6 months ago were randomly selected. Midline deviation, molar and canine relationships at both sides, facial growth pattern, and the amount of space loss were all assessed. Data were analyzed using SPSS version 25 via the one-sample t-test, paired t-test, and linear regression (alpha = 0.05). Results. The mean amount of space loss was 1.36 ± 0.78 mm (1.32 mm in the maxilla and 1.40 mm in the mandible). Time since tooth extraction and facial pattern had significant correlations with space loss P < 0.05 . Conclusion. In this particular age group, it is imperative to precisely assess the related factors such as the facial pattern and time since tooth extraction to decide about the placement of a space maintainer for a prematurely lost primary first molar.

Lower Incisor Positions in Different Malocclusions and Facial Pattern

Aims: The position of lower incisor has been of significant concern when seeking orthodontic treatment plan, it has been recognized as one of diagnostic key and play an important in the development of normal occlusion and facial pattern. This study was aimed at the determination of lower incisor position and its possible association with different sagittal malocclusions and facial patterns. Study Design: Descriptive Cross-sectional Study Place and Duration of Study: Department of Orthodontics Institute of Dentistry Liaquat University Medical and Health Sciences (LUMHS) Jamshoro between June 2019 to July 2020. Methodology: Ninety-seven pre-treatment lateral cephalometric radiographies were taken, and they were classified sagittally into skeletal class I, II and III, and vertically into normodivergent, hyperdivergent and hypodivergent facial pattern using ANB and SNMP, respectively. Lower incisor position was assessed by means of FMIA and IMPA. One way analysis of variance (ANOVA) was applied to check any association between lower incisor position and sagittal malocclusion and facial pattern. P value of 0.05 was considered statistically significant. Results: The study sample consisted of 97 patients. The mean age of the patients was 25.55 SD ±3.93. The mean SNMP value was 28.84 SD ±7.354 and mean ANB value 4.809 SD ±3.85. Mean Incisor position based on IMPA was 98.598 SD ± 9.413 and FMIA 54 SD ±9.995. Intra and interobserver reliability was assessed with intraclass correlation coefficient values ranging between 0.825 and 0.990 respectively. The ANOVA test results showed significant relationship between the sagittal malocclusion and incisor position with P value .036. The Bonferroni analysis indicated that statistically significant association existed in term of lower incisor position between Class II and Class III malocclusion with P value .047. Test results further indicated that there is no significant difference in the position of lower incisor in relation to facial pattern with P value .355. Conclusions: Statistically significant association was found between lower incisor position with respect to the sagittal malocclusion. However, no significant association was found between facial pattern and lower incisor position.

Maximum Voluntary Molar and Incisor Biting Force and Morphological Variables in Subjects With Different Vertical Skeletal Patterns

Objective: To estimate the maximum voluntary molar biting force (MBF) and incisor biting force (IBF) and their relationship to morphological variables in subjects with different vertical skeletal patterns. Materials and Methods: Maximum voluntary MBF, IBF, and morphological variables were recorded in 120 subjects (60 males and 60 females) with skeletal class I pattern in the age range of 14 to 24 years. All subjects were divided into 3 groups: Normodivergent, hypodivergent, and hyperdivergent, according to the maxillomandibular plane angle and Jarabak ratio. Bite force measurements were undertaken using a custom-made portable digital gnathodynamometer on the left and the right sides of the jaw in the molar and incisor regions during maximal clenching. Statistical analysis was performed using independent t-test, chi-square test, and ANOVA test using SPSS version 22.0.0.0 software. Results: MBF and IBF are influenced by gender with higher values obtained for male subjects in all groups in the following order: hypodivergent > normodivergent > hyperdivergent. No significant changes were seen with morphological variables in different groups. Conclusion: Molar and incisor biting forces are highest in hypodivergent subjects and least in hyperdivergent subjects as a reflection of jaw morphology and muscular efficiency. In all groups, males exhibit higher force values than females, underlining a strong gender influence on biting force and facial pattern.

A DETAILED REVIEW ON FINGERPRINT DOOR LOCK SYSTEM

Passwords and Tokens are extremely vulnerable and are easily stolen or lost. A poor password is one of the most common causes of security and data breaches. Hacker attacks target even the strongest passwords. Resetting the password takes a long time and can cause the employee to lose productivity. Biometrics can be used to solve the problem. It is the method of recognizing or confirming individuals based on physiological or behavioral features such as the iris, fingerprints, facial pattern, DNA, speech patterns, and so on. The concept of distinguishing individuals based on their fingerprints goes back thousands of years. It first became famous in the 1970’s. The detection and authentication of fingerprints is the method of fingerprint identification. Fingerprint identification is the most commonly used biometric. This research paper explains the main characteristics of fingerprints and how the Automatic Minutiae Detection process works, as well as comparing 2D and 3D fingerprint recognition

Robot movement controller based on dynamic facial pattern recognition

In terms of movement, mobile robots are equipped with various navigation techniques. One of the navigation techniques used is facial pattern recognition. But Mobile robot hardware usually uses embedded platforms which have limited resources. In this study, a new navigation technique is proposed by combining a face detection system with a ram-based artificial neural network. This technique will divide the face detection area into five frame areas, namely top, bottom, right, left, and neutral. In this technique, the face detection area is divided into five frame areas, namely top, bottom, right, left, and neutral. The value of each detection area will be grouped into the ram discriminator. Then a training and testing process will be carried out to determine which detection value is closest to the true value, which value will be compared with the output value in the output pattern so that the winning discriminator is obtained which is used as the navigation value. In testing 63 face samples for the Upper and Lower frame areas, resulting in an accuracy rate of 95%, then for the Right and Left frame areas, the resulting accuracy rate is 93%. In the process of testing the ram-based neural network algorithm pattern, the efficiency of memory capacity in ram, the discriminator is 50%, assuming a 16-bit input pattern to 8 bits. While the execution time of the input vector until the winner of the class is under milliseconds (ms).

Applicability of the Tanaka-Johnston method for the estimation of the mesiodistal diameter of canines and premolars in patients of 12-18 years old

The Tanaka-Johnston method is used worldwide to predict the diameter of canines and premolars not erupted for the convenience of not needing boards or x-rays for use. However, in recent years researchers from several countries have shown that when used in a different population for which it was designed, it can overestimate or underestimate the values. In Cuba, where the facial pattern of the population differs from the ideal for this method, it has been highly used, but there are very few studies where the reliability or accuracy of the predictions of the same is validated. Therefore, the objective of this research is to determine the applicability of the Tanaka-Johnston method for estimating the mesiodistal diameter of canines and premolars in patients aged 12-18 years. A descriptive and cross-cutting study was conducted from June 2019 to January 2020 with a population of 140 patients of both sexes between 12 and 18 years of age from Cuba. Measurements were made of the mesiodistal widths of the lower incisors, all canines, and premolars. Frequency distributions were made to the variables studied and the results were presented in statistical tables. The t-Student statistical test was used to verify significant differences. The main results obtained were that the Tanaka-Johnston method tends to overestimate the values for the female sex and underestimate them for the male, both between 0.2 and 0.3 mm, but this difference is not significant. It is concluded that the Tanaka-Johnston method can be applied in the population studied for the prediction of the mesiodistal width of unerupted canines and premolars.

Considerations for vertical control with microimplants in a idiopathic condylar resorption patient: A case report

Idiopathic condylar resorption (ICR) is a rare, destructive temporomandibular joint disease characterised by progressive resorption of the condyles. This case report presents a record of an orthodontically treated patient with ICR with favourable posttreatment remodelling of the condyles. An 18-year-old woman sought treatment for ICR. A severe Class II high-angle facial pattern with resorption of bilateral condyles was evident. The treatment plan was determined after careful examination of condylar radiographs and comprised forward rotation of the mandible through full-arch intrusion with microimplants after extraction of the four premolars. The treatment was completed in 35 months, and the patient was noted to have a straight profile, good interdigitation and slightly increased condylar volume. Two years after retention, the condyles were stable, and the patient’s profile and occlusion remained acceptable despite a mild relapse of the mandibular position. ICR was successfully corrected with orthodontic treatment. Counter-clockwise mechanics applied during the ICR remission period not only improved facial aesthetics but were also suitable for condylar unloading.

PENGENALAN EKSPRESI RAUT WAJAH BERBASIS JARINGAN SARAF TIRUAN BACKPROPAGATION DENGAN METODE PRINCIPAL COMPONENT ANALYSIS

The development of artificial neural networks is related to statistical and biometric analysis which is one of the applications that can require artificial neural network models. Recognition of facial patterns is an important part of identifying a person. The face can be divided into areas such as the nose, eyes and mouth. Face pattern recognition is a research area that can be applied to the principal component analysis (PCA) method. The training process carried out by the eigenface calculation uses PCA and the results of this study show that facial pattern recognition based on the proportion of memorization and generalization for the use of the method without PCA is better than facial pattern recognition using PCA. Pattern recognition without using the PCA method, the level of memorization and generalization reaches 100% at the 40th iteration and 0.0099 error with a learning rate and momentum of 0.8, while facial pattern recognition using the PCA method, the memorization and generalization level reaches 100% in the iteration. to -1000 and error 0.00103 with learning rate and momentum 0.9.

Presensi Kelas Berbasis Pola Wajah, Senyum dan Wi-Fi Terdekat dengan Deep Learning

Students' attendance in class is often mandatory in education and becomes a benchmark for assessing students. Sometimes there are still fraudulent practices by students to achieve minimum attendance. From the administrative perspective, a paper-based presence system is potentially wasteful and extends the administrative stage because it requires manual recapitulation. This study aims to design a class attendance application based on facial pattern recognition, smile, and closest Wi-Fi. The method used in this research is a deep learning approach with CNN based architecture, FaceNet, to recognize faces. In addition to facial images, the system will also validate the attendance with location and time data. Location data is obtained from matching SSID from the database, and time data is taken when the user sends attendance data through API. This attendance system consists of three applications: web, mobile, and services installed on a mini-computer, which are integrated to sending attendance data to the academic system automatically. As confirmation, students are required to smile selfies to strengthen the validity of their presence. The testing model's accuracy results are 92.6%, while for live testing accuracy the model obtained 66.7%.