Exfoliative cytology for age estimation: A correlative study in different age groups

2018 ◽  
Vol 5 (1) ◽  
pp. 25
Author(s):  
ThambuKeerthi Priyadharshini ◽  
Vadivel Ilayaraja ◽  
Nalliappan Ganapathy ◽  
Andamuthu Yamunadevi ◽  
Janardhanam Dineshshankar ◽  
...  
Keyword(s):  
Age Estimation ◽  
Age Groups ◽  
Exfoliative Cytology ◽  
Correlative Study

scholarly journals Two sources of bias explain errors in facial age estimation

2018 ◽  
Vol 5 (10) ◽  
pp. 180841 ◽  
Author(s):  
Colin W. G. Clifford ◽  
Tamara L. Watson ◽  
David White
Keyword(s):  
Social Interactions ◽  
Age Estimation ◽  
Estimation Error ◽  
Age Groups ◽  
Large Database ◽  
Police Investigations ◽  
Immediate Experience ◽  
Improving Accuracy ◽  
Facial Age ◽  
Age Estimates

Accurate age estimates underpin our everyday social interactions, the provision of age-restricted services and police investigations. Previous work suggests that these judgements are error-prone, but the processes giving rise to these errors are not understood. Here, we present the first systematic test of bias in age estimation using a large database of standardized passport images of heterogeneous ages ( n = 3948). In three experiments, we tested a range of perceiver age groups ( n = 84), and found average age estimation error to be approximately 8 years. We show that this error can be attributed to two separable sources of bias. First, and accounting for the vast majority of variance, our results show an assimilative serial dependency whereby estimates are systematically biased towards the age of the preceding face. Second, younger faces are generally perceived to be older than they are, and older faces to be younger. In combination, these biases account for around 95% of variance in age estimates. We conclude that perception of age is modulated by representations that encode both a viewer's recent and normative exposure to faces. The finding that age perception is subject to strong top-down influences based on our immediate experience has implications for our understanding of perceptual processes involved in face perception, and for improving accuracy of age estimation in important real-world tasks.

scholarly journals Oral Squamous Cells and Age Estimation in Exfoliative Cytology with Hematoxylin and Eosin Stain– A Quantitative Study

2021 ◽  
pp. 323-329
Author(s):  
B. Keerthana ◽  
R. Priyadharshini ◽  
Palati Sinduja
Keyword(s):  
Age Estimation ◽  
Morphological Changes ◽  
Pearson Correlation ◽  
Premalignant Lesions ◽  
Size Difference ◽  
P Value ◽  
Exfoliative Cytology ◽  
Age Variation ◽  
Epithelial Layers ◽  
Hematoxylin And Eosin Stain

Introduction: Exfoliative cytology in age estimation is a simple, painless, less invasive collection of exfoliative cells from epithelial layers, used as a diagnostic aid for age estimation. The oral cavity is an ideal site for exfoliative epithelial cells with a physiological turnover of cells, turnover decreases as the age increases show age variation with cellular morphological changes. Age estimation is one of the important factors to identify an individual and also helps to know the chronological age of a person. Aim: To analyze and estimate the age from buccal smear and comparing the average cellular size under Image morphometric analysis. Materials and Methods: Buccal mucosal smears are taken using a wooden spatula in gentle motion of scraping and smeared on a clean glass slide and fixed in 95% ethanol immediately after smearing a minimum of around 15 minutes and stained with Haematoxylin and eosin stain. After staining, the cells were observed by microscope and measured by a paint tool. Pearson correlation analysis was done using SPSS software. Results: The cell and nuclear size difference values observed using a Pearson correlation coefficient were statistically significant with p value<0.05 revealing that there is shrinkage in cells with increase in age. Conclusion:  Exfoliative cytology is a successful and vastly growing technology that is used for the detection of premalignant lesions. 

Foot Age Estimation System from Walking Dynamics Based on Fuzzy Logic

2014 ◽  
Vol 18 (4) ◽  
pp. 489-498 ◽  
Author(s):  
Takahiro Takeda ◽  
◽  
Yoshitada Sakai ◽  
Syoji Kobashi ◽  
Kei Kuramoto ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Pressure Distribution ◽  
Age Estimation ◽  
Age Groups ◽  
Absolute Error ◽  
Elderly Age ◽  
Age Related ◽  
Gait Features ◽  
Walking Dynamics ◽  
Estimation System

This paper describes a foot-age estimation system based on fuzzy logic. The foot-age is one of age related indexes, and it shows the degree of aging by the gait condition. The system estimates the foot-age from sole pressure distribution change during walking. The sole pressure distribution is acquired by a mat-type load distribution sensor. Our estimation system extracts four gait features from sole pressure data, and calculates fuzzy degrees for young age,middle age and elderly age groups from these gait features. The footage of the walking person on the sensor is calculated by fuzzy MIN-MAX center of gravity method. In our experiment, we employed 93 male and 132 female volunteers, and the system estimated their foot-ages with low mean absolute error for their true ages. Additionally, we developed a diagnosis system based on estimated foot-age.

Accuracy of Cameriere, Haavikko, and Willems radiographic methods on age estimation on Bosnian–Herzegovian children age groups 6–13

2010 ◽  
Vol 125 (2) ◽  
pp. 315-321 ◽  
Author(s):  
Ivan Galić ◽  
Marin Vodanović ◽  
Roberto Cameriere ◽  
Enita Nakaš ◽  
Elizabeta Galić ◽  
...  
Keyword(s):  
Age Estimation ◽  
Age Groups

scholarly journals Estimation of Age Using Dimensions of Apical Foramina of Teeth

2021 ◽  
Vol 10 (10) ◽  
pp. 705-710
Author(s):  
Aklesha Behera ◽  
Archana Santhanam ◽  
Herald J. Sherlin ◽  
Gifrina Jayaraj ◽  
Don K.R.
Keyword(s):  
Age Estimation ◽  
Age Groups ◽  
Age Determination ◽  
Forensic Odontology ◽  
Estimation Methods ◽  
P Value ◽  
Age Group ◽  
Human Beings ◽  
Mean Diameter ◽  
The Mean

BACKGROUND Forensic odontology is an ever growing and a flourishing science. The science deals in criminal laws by using dental knowledge. People who practice forensic odontology are called as forensic dentists. They are asked to determine the age, sex, race, occupation, previous dental history and various other determinants like DNA verification in unidentified human beings. Teeth are a very special tissue in the human body. Teeth are the only tissue which can survive through extreme climatic and environmental conditions like heat, cold etc. hence many a times retains its morphology. Age estimation has been studied by many researchers but age estimation methods have been incapable of providing the age estimation in older age groups. The study aims to determine the age using the dimensions of the apical foramen from middle age to old age groups. METHODS A descriptive ex vivo study, was done from December 2019 to March 2020. Freshly extracted teeth were collected and cleaned using hydrogen peroxide. The teeth selected were single rooted teeth like incisors, canine and premolars in the age group of 30 to 40 years, 41 - 50 years, 51 - 60 years and 61 - 70 years. Apical one third of the teeth was sectioned, mounted over a slide and placed perpendicular to the lens of the stereomicroscope. Using Mag Vision and Image J software, photomicrographs and measurements were taken. Data procured was tabulated and statistical analysis was done using Statistical Package for the Social Sciences (SPSS) version 20. RESULTS In 30 - 40 years age group, the mean diameter of all single rooted teeth was 0.35 mm. Among the 41 - 50 years age group, the mean diameter was 0.43mm. In 51 - 60 years age group it was 0.41mm and in the 61 - 70 years age group, the mean diameter was 0.36 mm. Pearson chi square P value for incisor is 0.280, canine is 0.223 and premolar is 0.326. CONCLUSIONS Age estimation can be done using physiological dimensions of apical foramen. From the present study, dimensions of apical foramen decreases with increasing age due to cementum deposition. KEY WORDS Age Determination, Apical Foramen Diameter, Single Rooted Teeth

scholarly journals Exfoliative cytology: A possible tool in age estimation in forensic odontology

2015 ◽  
Vol 7 (1) ◽  
pp. 63 ◽  
Author(s):  
Vijay Wadhwan ◽  
KaveriSurya Khanna ◽  
Anshi Jain ◽  
Amit Gupta ◽  
DeviCharan Shetty
Keyword(s):  
Age Estimation ◽  
Forensic Odontology ◽  
Exfoliative Cytology

Conservation, Ecology, and Management of Catfish: The Second International Symposium

2011 ◽  
Keyword(s):  
Age Estimation ◽  
Early Life ◽  
Age Groups ◽  
Early Life History ◽  
Blue Catfish ◽  
Conservation Ecology ◽  
Flathead Catfish ◽  
Daily Increments ◽  
Daily Ring ◽  
Management And Conservation

<em>Abstract</em>.—We validated methods for estimating daily age of age-0 blue catfish <em>Ictalurus furcatus</em> and flathead catfish <em>Pylodictis olivaris</em>. Larvae of each species were reared in the laboratory and were sampled approximately every 10 d over a 4-month period. Five individuals of each species were randomly selected for daily age estimation from each of nine age-groups, ranging from 40 to 119 d posthatch for blue catfish and 20–121 d posthatch for flathead catfish. Mean daily ring count and known age were related for both species, indicating that daily ring deposition occurred in the otoliths of these fishes. Daily increment counts were accurate through 60 d posthatch for blue catfish and through 72 d posthatch for flathead catfish, with mean deviance of estimated age within 1 d of the known age. For both species, daily increments could be enumerated for older fish; however, accuracy decreased with age. We encourage researchers to utilize our aging technique to estimate hatch dates, the timing and frequency of hatching, and early growth rates of catfishes in wild populations. Such early life history information will be valuable in enhancing the management and conservation of important catfish populations.

Accepting final counts from repeat readings of otoliths: should a common criterion apply to the age estimation of fish?

2010 ◽  
Vol 61 (10) ◽  
pp. 1171 ◽  
Author(s):  
Ross J. Marriott ◽  
Bruce D. Mapstone ◽  
Aaron C. Ballagh ◽  
Leanne M. Currey ◽  
Ann Penny ◽  
...  
Keyword(s):  
Count Data ◽  
Age Estimation ◽  
Age Groups ◽  
Multiple Criteria ◽  
Older Age ◽  
Explanatory Model ◽  
Probability Of Error ◽  
Exact Agreement ◽  
Common Criterion ◽  
Age Estimates

Multiple readings of otoliths are often carried out to assess the repeatability and reliability of increment counts for estimating fish age. Various criteria have been used to assign or discard age estimates from repeated counts when discrepancies occur although the reasons for doing so are usually not stated or justified. Trends in relative frequencies (percentage disagreement, PD) and magnitudes (inter-read discrepancy, IRD) of otolith-count discrepancies were explored for 15 species of fish collected from a range of locations around Australia to explore generality in the best explanatory model(s) for otolith-count discrepancies and, hence, the most appropriate criterion for accepting or rejecting age estimates from multiple-count data. Increasing discrepancies with increasing age, according to a constant per-increment probability of error, was the best-approximating model for 9 of the 15 species for PD data but for only two species for IRD data. Our results indicated disproportionately higher rates of rejection of estimates from older age groups if exact agreement between repeated counts was required for age acceptance. Results varied with the reader, region and the method of otolith reading, indicating that multiple criteria for accepting or rejecting counts from multiple readings may be required among or even within species.

scholarly journals Evaluation of Dental Age in Nepali Children using Demirjian’s 7-Teeth Teeth Method

2018 ◽  
Vol 7 (2) ◽  
pp. 37-40
Author(s):  
Rinky Nyachhyon
Keyword(s):  
Age Estimation ◽  
Age Groups ◽  
P Value ◽  
Dental Age ◽  
Dental Age Estimation ◽  
Tooth Formation ◽  
The Mean ◽  
Mean Differences ◽  
Demirjian’S Method ◽  
Dental Maturity

Introduction: Age estimation can be based on tooth formation stages seen on the radiographs. Demirjian’s method is widely used to assess age of individuals with developing dentition. There are not many documented studies of age estimation amongst Nepali population.Objective: To assess the applicability of Demirjian’s method for dental age estimation in Nepali children.Materials & Method: The sample of the study consisted of 186 subjects between 7-16 years of age. Dental age estimation was based on Demirjian’s method and scored by a single observer.Result: The mean and mean differences in chronological and dental ages were calculated, which revealed the overestimation in age groups of 7 and 9 years, whereas there were underestimations in other age groups. The mean differences in age groups 12-16 were highly significant (p-Value <0.05) whereas the mean difference in age groups 7-11 years were not statistically significant.Conclusion: Since there is underestimation range from 0.5 years to 1.23 years, the standards of dental maturity described by Demirjian may not be suitable for Nepali children.

scholarly journals Facial age estimation using SFTA and deep neural network

2018 ◽  
Vol 7 (2) ◽  
pp. 281
Author(s):  
Deepa Nagarajan ◽  
T. Sasipraba
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Age Estimation ◽  
Deep Neural Network ◽  
Age Groups ◽  
Histogram Equalization ◽  
Age Group ◽  
Final Layer ◽  
Feature Values ◽  
Facial Age

This paper construes the toils in facial age estimation in images. The fact that manual age estimation is indeed hard rising out the urge for digital age estimation. To make estimation precise many works have been carried out by considering a lot of constraints. In this paper, facial age estimation is done more accurately. SFTA method is used for feature extraction and meticulous results are obtained for all age groups. Histogram equalization is done using the Otsu algorithm and three layered Deep Neural Network is used to classify the age group. In a Deep neural network, softmax normalization is done in the final layer to preserve the outlier values. By extracting 45 feature values concerning color and gradient, key point descriptor, orientation, shape and texture better estimation are obtained.

Sign in / Sign up

Export Citation Format

Share Document