scholarly journals Validating dental age estimation in Kenyan black children and adolescents using the Willems method

2020 ◽  
pp. 002580242097737
Author(s):  
Maria Cadenas de Llano-Pérula ◽  
Eunice Kihara ◽  
Patrick Thevissen ◽  
Donna Nyamunga ◽  
Steffen Fieuws ◽  
...  
Keyword(s):  
Age Estimation ◽  
Tooth Development ◽  
Absolute Error ◽  
Black Children ◽  
Specific Model ◽  
Permanent Teeth ◽  
Estimation Model ◽  
One Year ◽  
Willems Method ◽  
Age Prediction

Purpose This study aimed to validate the Willems Belgian Caucasian (Willems BC) age estimation model in a Kenyan sample, to develop and validate a Kenyan-specific (Willems KB) age estimation model and to compare the age prediction performances of both models. Methods Panoramic radiographs of 1038 (523 female, 515 male) Kenyan children without missing permanent teeth and without all permanent teeth fully developed (except third molars) were retrospectively selected. Tooth development of the seven lower-left permanent teeth was staged according to Demirjian et al. The Willems BC model, performed on a Belgian Caucasian sample and a constructed Kenyan-specific model (Willems KB) were validated on the Kenyan sample. Their age prediction performances were quantified and compared using the mean error (ME), mean absolute error (MAE) and root-mean-square error (RMSE). Results The ME with Willems BC method equalled zero. Hence, there was no systematic under- or overestimation of the age. For males and females separately, the ME with Willems BC was significantly different from zero, but negligible in magnitude (–0.04 and 0.04, respectively). Willems KB was found not to outperform Willems BC, since the MAE and RMSE were comparable (0.98 vs 0.97 and 1.31 vs 1.29, respectively). Although Willems BC resulted in a higher percentage of subjects with predicted age within a one-year difference of the true age (63.3% vs 60.4%, p=0.018), this cannot be considered as clinically relevant. Conclusion There is no reason to use a country-specific (Willems KB) model in children from Kenya instead of the original Willems (BC) model.

scholarly journals Age estimation based on Willems method versus new country-specific method in South African black children

2017 ◽  
Vol 132 (2) ◽  
pp. 599-607 ◽  
Author(s):  
Guy Willems ◽  
Sang-Seob Lee ◽  
Andre Uys ◽  
Herman Bernitz ◽  
Maria Cadenas de Llano-Pérula ◽  
...  
Keyword(s):  
South African ◽  
Age Estimation ◽  
Black Children ◽  
Specific Method ◽  
African Black ◽  
Willems Method ◽  
Country Specific

scholarly journals Demirjian’s Eight Teeth Method for Dental age Estimation in Nepalese Population

2021 ◽  
Vol 18 (4) ◽  
pp. 686-691
Author(s):  
Nuwadatta Subedi ◽  
Umesh Parajuli ◽  
Ishwari Sharma Paudel ◽  
Mukesh Mallik
Keyword(s):  
Age Estimation ◽  
Developmental Stages ◽  
Tooth Development ◽  
Absolute Error ◽  
Original Method ◽  
Regression Equations ◽  
Dental Age ◽  
Dental Age Estimation ◽  
Maturity Score ◽  
Statistical Program

Background: Demirjian’s method is widely used method for dental age estimation. This study was conducted with objectives of applying Demirjian’s 8 teeth method to estimate age in Nepalese Population and to determine Nepal-specific formulas.Methods: We had used the Orthopantomographs of Nepalese people of age above five and below 23 years. The radiographs were compared to the ‘Tooth Development Chart’ and each tooth studied was assigned with any one of the 10 developmental stages using Demirijian’s 8 teeth method and total maturity scores determined. Formulas were derived using regression analysis, wherein the total maturity score obtained for each individual was considered as the independent variable and the corresponding age as the dependent variable in the STATA 15.1 statistical program. Results: There was underestimation of age in both the sexes by the original method. Regression equations were derived for males and females separately for age five to 18 years and again after adding cases up to 23 years. The estimation was better for males up to 18 years [R2=0.94, Mean Absolute Error (MAE) 0.747 years and SD 0.644] than for females up to 18 years (R2 = 0.89, MAE 0.886 years and SD 0.925). The estimation was better for up to 18 years than for up to 23 years in both sexes.Conclusions: Demirijian’s 8 teeth method underestimated age in the study population and thus population specific equations based on the method are better for dental age estimation. The age estimation utilizing the equations from Nepalese population has given acceptable results.Keywords: Age estimation; demirijian’s method; dental age estimation; forensic age estimation; tooth development chart

Research on Age Estimation Algorithm Based on Structured Sparsity

2019 ◽  
Vol 33 (06) ◽  
pp. 1956006 ◽  
Author(s):  
Zijiang Zhu ◽  
Junshan Li ◽  
Yi Hu ◽  
Xiaoguang Deng
Keyword(s):  
Age Estimation ◽  
Age Distribution ◽  
Estimation Algorithm ◽  
Absolute Error ◽  
Sparse Learning ◽  
Estimation Model ◽  
Accumulation Index ◽  
Error Accumulation ◽  
Index Curve ◽  
Facial Images

In order to solve the inaccuracy of age estimation dataset and the imbalance of age distribution, this paper proposes an age estimation model based on the structured sparse learning. Firstly, the Multi-label representation of facial images is performed by age, and the age estimation model is trained by solving the model matrix. Finally, the correlation with all age labels is calculated according to the facial images and age estimation model to be tested, and the most correlated age is taken as the predicted age. This paper sets up a series of verification experiments, and analyzes the structured sparse age estimation model from several perspectives. The proposed algorithm has achieved good results in the evaluation of indexes such as the mean absolute error, accumulation index curve and convergence rate, and has designed the demo system to put the model into use. Facts prove that the age estimation model proposed in this paper may achieve a good estimation effect.

scholarly journals Age estimation based on Willems method versus country specific model in Saudi Arabia children and adolescents

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ali Alqerban ◽  
Muath Alrashed ◽  
Ziyad Alaskar ◽  
Khalid Alqahtani
Keyword(s):  
Children And Adolescents ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Specific Model ◽  
Permanent Teeth ◽  
Dental Age ◽  
Mean Error ◽  
Significant Difference ◽  
The Difference ◽  
Mandibular Teeth

Abstract Background The aims of this study were to create a method for estimation of dental age in Saudi children and adolescents based on the Willems model developed using the Belgian Caucasian (BC) reference data and to compare the ability of the two models to predict age in Saudi children. Methods Development of the seven lower left permanent mandibular teeth was staged in 1146 panoramic radiographs from healthy Saudi children (605 male, 541 female) without missing permanent teeth and without all permanent teeth fully developed (except third molars). The data were used to validate the Willems BC model and to construct a Saudi Arabian-specific (Willems SA) model. The mean error, mean absolute error, and root mean square error obtained from both validations were compared to quantify the variance in errors in the sample. Results The overall mean error for the Willems SA method was 0.023 years (standard deviation, ± 0.55), indicating no systematic underestimation or overestimation of age. For girls, the error using the Willems SA method was significantly lower but still negligible at 0.06 years. A small but statistically significant difference in total mean absolute error (11 days) was found between the Willems BC and Willems SA models when the data were compared independent of sex. The overall mean absolute error for girls was slightly lower for the Willems BC method than for the Willems SA method (1.33 years vs. 1.37 years). Conclusions The difference in ability to predict dental age between the Willems BC and Willems SA methods is very small, indicating that the data from the BC population can be used as a reference in the Saudi population.

scholarly journals Laporan Kasus: Dentinogenesis Imperfekta

2019 ◽  
Vol 16 (1) ◽  
pp. 12
Author(s):  
Fadli Ashar ◽  
Evellyn Islami Abdurokhman
Keyword(s):  
Autosomal Dominant ◽  
Tooth Development ◽  
Permanent Teeth ◽  
Dentinogenesis Imperfecta ◽  
Pulp Chamber ◽  
Restorative Treatment ◽  
Anterior Teeth ◽  
One Year ◽  
The One ◽  
Dentin Formation

Dentinogenesis imperfecta is the one of most common hereditary disorder of dentin formation, especially at histodifferentiation stage. It reported have to an incidences of 1 in 8000. Dentinogenesis imperfecta is an autosomal dominant, affecting both the formation and mineralizatin of dentin. CASE: A 20-year-old female patient came to integrasi A RSGMP UNSOED with crown fracture of anterior teeth 11, after eating a solid food. A permanent filling was conducted on 21 with the same cause one year ago. Intraoral examination showed attrition, yellowish and translucent on both maxillary and mandibullary teeths. Radiography showed that obliterated pulp chamber and shrinked root canal. Porcelain fused to metal crown was planned for this case. Conclusion: Dentinogenesis imperfecta is a hereditary disorder of tooth development. It caused the teeth to be discoloured (blue-grey or yellow-brown color), and fragile. Treatment planning for patient with dentinogenesis imperfecta is restorative treatment that can be apply to deciduous or permanent teeth, because exposed dentin will increase the risk of caries and infection.

The relationship of bovine rostral tooth development to age determined by post-mortem radiographic examination of cattle aged between 12 and 24 months

1981 ◽  
Vol 97 (1) ◽  
pp. 97-102 ◽  
Author(s):  
A. H. Andrews
Keyword(s):  
Age Estimation ◽  
Tooth Development ◽  
Permanent Teeth ◽  
Radiographic Examination ◽  
Post Mortem ◽  
Second Year Of Life ◽  
Second Year ◽  
Age Range ◽  
Relationship Of ◽  
The Relationship

SUMMARYResults of rostral tooth development determined both visually and by post-mortem radiographic examination were used to provide age ranges and median ages for stages which could be seen between 1 and 2 years old. These showed that intra-oral eruption of the permanent rostral teeth was of little value in age estimation during the second year of life. However, utilizing the stages of tooth development which could be seen radiographically the maximum age range before differentiation was 5 months (1 year 1 month from 1 year 6 months and 1 year 5 months from 1 year 10 months). Thus it appeared that rostral tooth radiography was of more use in age estimation than intraoral eruption of the permanent teeth.

scholarly journals Dental Age Assessment using Demirjian’s Eight Teeth Method and Willems Method in a Tertiary Hospital

2018 ◽  
Vol 56 (214) ◽  
pp. 912-916
Author(s):  
Nitin Kumar Agrawal ◽  
Lucina Hackman ◽  
Samarika Dahal
Keyword(s):  
Age Estimation ◽  
Tooth Development ◽  
Third Molar ◽  
Tertiary Hospital ◽  
Chronological Age ◽  
Criminal Prosecution ◽  
Dental Age ◽  
Dental Age Estimation ◽  
The Mean ◽  
Willems Method

Introduction: Age estimation is an important aspect in forensic anthropology, as it can aid in the identification of the deceased, and can be used in cases of immigration, child abuse and criminal prosecution in living individuals. Dental age estimation is considered reliable and accurate, since tooth development is least affected by environmental factors compared to somatic growth.Methods: In total, 150 pre-orthodontic treatment radiographs from healthy individuals were assessed. These individuals were aged between 8 to 19 years. Dental age for these individuals was calculated by two methods: Demirjian’s eight teeth method and Willems method. For Willems method, seven teeth on the left side of mandible (except the third molar) were staged according to Demirjian’s staging, and for Demirjian’s eight teeth method, all eight teeth were staged.Results: The mean chronological ages were 13.6961±1.94384 years in males and 13.9204±2.63541 years in females. The mean estimated ages by Demirjian’s eight teeth method were 12.1856±1.73478 years and 11.7906±2.32344 years in males and females respectively. Similarly, the mean estimated ages by Willems method were 12.8958±1.46838 years in males and 12.6926±2.27807 years in females.Conclusions: Willems method and Demirjian’s eight teeth method underestimated the chronological age in the given population. Both methods showed excellent correlation with chronological age indicating their applicability in dental age estimation, with development of population specific scores.

scholarly journals Impact of excessive alcohol abuse on age prediction using the VISAGE enhanced tool for epigenetic age estimation in blood

2021 ◽  
Author(s):  
Danuta Piniewska-Róg ◽  
Antonia Heidegger ◽  
Ewelina Pośpiech ◽  
Catarina Xavier ◽  
Aleksandra Pisarek ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Alcohol Abuse ◽  
Age Estimation ◽  
Forensic Genetics ◽  
Absolute Error ◽  
Control Group ◽  
Older Individuals ◽  
Alcohol Abusers ◽  
Epigenetic Age ◽  
Age Prediction

AbstractDNA methylation-based clocks provide the most accurate age estimates with practical implications for clinical and forensic genetics. However, the effects of external factors that may influence the estimates are poorly studied. Here, we evaluated the effect of alcohol consumption on epigenetic age prediction in a cohort of extreme alcohol abusers. Blood samples from deceased alcohol abusers and age- and sex-matched controls were analyzed using the VISAGE enhanced tool for age prediction from somatic tissues that enables examination of 44 CpGs within eight age markers. Significantly altered DNA methylation was recorded for alcohol abusers in MIR29B2CHG. This resulted in a mean predicted age of 1.4 years higher compared to the controls and this trend increased in older individuals. The association of alcohol abuse with epigenetic age acceleration, as determined by the prediction analysis performed based on MIR29B2CHG, was small but significant (β = 0.190; P-value = 0.007). However, the observed alteration in DNA methylation of MIR29B2CHG had a non-significant effect on age estimation with the VISAGE age prediction model. The mean absolute error in the alcohol-abusing cohort was 3.1 years, compared to 3.3 years in the control group. At the same time, upregulation of MIR29B2CHG expression may have a biological function, which merits further studies.

Estimation of Hemi-Abdominal-Based Free Flap Weight Using Two Computed Tomography-Derived Measurements

2021 ◽  
Author(s):  
Nicolas Greige ◽  
Bryce Liu ◽  
David Nash ◽  
Katie E. Weichman ◽  
Joseph A. Ricci
Keyword(s):  
Computed Tomography ◽  
Breast Reconstruction ◽  
Free Flap ◽  
Subcutaneous Tissue ◽  
Free Tissue Transfer ◽  
Absolute Error ◽  
Estimation Methods ◽  
Weight Estimation ◽  
Estimation Model ◽  
Split Sample

Abstract Background Accurate flap weight estimation is crucial for preoperative planning in microsurgical breast reconstruction; however, current flap weight estimation methods are time consuming. It was our objective to develop a parsimonious and accurate formula for the estimation of abdominal-based free flap weight. Methods Patients who underwent hemi-abdominal-based free tissue transfer for breast reconstruction at a single institution were retrospectively reviewed. Subcutaneous tissue thicknesses were measured on axial computed tomography angiograms at several predetermined points. Multivariable linear regression was used to generate the parsimonious flap weight estimation model. Split-sample validation was used to for internal validation. Results A total of 132 patients (196 flaps) were analyzed, with a mean body mass index of 31.2 ± 4.0 kg/m2 (range: 22.6–40.7). The mean intraoperative flap weight was 990 ± 344 g (range: 368–2,808). The full predictive model (R 2 = 0.68) estimated flap weight using the Eq. 91.3x + 36.4y + 6.2z – 1030.0, where x is subcutaneous tissue thickness (cm) 5 cm lateral to midline at the level of the anterior superior iliac spine (ASIS), y is distance (cm) between the skin overlying each ASIS, and z is patient weight (kg). Two-thirds split-sample validation was performed using 131 flaps to build a model and the remaining 65 flaps for validation. Upon validation, we observed a median percent error of 10.2% (interquartile range [IQR]: 4.5–18.5) and a median absolute error of 108.6 g (IQR: 45.9–170.7). Conclusion We developed and internally validated a simple and accurate formula for the preoperative estimation of hemi-abdominal-based free flap weight for breast reconstruction.

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