scholarly journals Age and sex estimation based on pulp cavity volume using cone beam computed tomography: development and validation of formulas in a Brazilian sample

2019 ◽  
Vol 48 (7) ◽  
pp. 20190053 ◽  
Author(s):  
Vanessa M Andrade ◽  
Rocharles C Fontenele ◽  
Andreia CB de Souza ◽  
Casimiro AP de Almeida ◽  
Andrea CD Vieira ◽  
...  

Objectives: To develop and validate formulas for age and sex estimation based on the pulp cavity volume of teeth using cone beam CT. Methods: The sample was composed of 116 cone beam CT scans from Brazilian individuals of both sexes, ranging in age from 13 to 70 years. A total of 232 teeth (upper central incisors and canines) were evaluated. Two calibrated examiners determined pulp cavity volumes using the ITK-SNAP software. Pearson’s correlation test was used to assess the correlation between chronological age and pulp volume. Linear and logistic regression models were developed for age and sex estimation, respectively, and were validated in another sample of 72 teeth. Results: Pearson’s correlation coefficients between age and pulp volume were negative and significant (p < 0.0001) for both teeth (r = −0.8782 for central incisors and r = −0.8738 for canines). The age estimation formulas showed good determination coefficients (adjusted R² = 0.7614 to 0.8367). For sex estimation, when the age was known, the coefficients were also good (adjusted R² = 0.649 to 0.812). However, when the age was unknown, the coefficients of the sex estimation formulas were low (adjusted R² = 0.047 to 0.393). Validation showed high accuracy of age estimation in individuals older than 35 years, as well as high accuracy of sex estimation when the age was known. Conclusions: Our formulas provided excellent results and can be applied to the Brazilian population. The best results were observed for age estimation in females and for sex estimation when the age was known.

2021 ◽  
Vol 10 (19) ◽  
pp. 4431
Author(s):  
Chung-Yi Yang ◽  
Yi-Ju Pan ◽  
Yen Chou ◽  
Chia-Jung Yang ◽  
Ching-Chung Kao ◽  
...  

Background: The performance of chest radiography-based age and sex prediction has not been well validated. We used a deep learning model to predict the age and sex of healthy adults based on chest radiographs (CXRs). Methods: In this retrospective study, 66,643 CXRs of 47,060 healthy adults were used for model training and testing. In total, 47,060 individuals (mean age ± standard deviation, 38·7 ± 11·9 years; 22,144 males) were included. By using chronological ages as references, mean absolute error (MAE), root mean square error (RMSE), and Pearson’s correlation coefficient were used to assess the model performance. Summarized class activation maps were used to highlight the activated anatomical regions. The area under the curve (AUC) was used to examine the validity for sex prediction. Results: When model predictions were compared with the chronological ages, the MAE was 2·1 years, RMSE was 2·8 years, and Pearson’s correlation coefficient was 0·97 (p < 0·001). Cervical, thoracic spines, first ribs, aortic arch, heart, rib cage, and soft tissue of thorax and flank seemed to be the most crucial activated regions in the age prediction model. The sex prediction model demonstrated an AUC of > 0·99. Conclusion: Deep learning can accurately estimate age and sex based on CXRs.


2011 ◽  
Author(s):  
Daniel J. Mirota ◽  
Ali Uneri ◽  
Sebastian Schafer ◽  
Sajendra Nithiananthan ◽  
Douglas D. Reh ◽  
...  

2017 ◽  
Vol 20 (1) ◽  
pp. 69-77
Author(s):  
Juana R. Delgadillo Avila DDS, MSc, PhD ◽  
Manuel A. Mattos-Vela DDS, MSc, PhD

The aim of the present study was the determine the location of the mental foramen and accessories, their relationships with the alveolar and basal rims found in Peruvians adults. A descriptive, transverse and retrospective study was carried out. The sample was composed of 100 cone beam CT scans of patients, between the ages of 20 to 55 years of age, these patients were attended at the clinic of the Faculty of Dentistry of Mayor National University of San Marcos UNMSM. Were considered tomograms of mandibular dentate patients taken during 2015, classified according to age and sex, identifying in them the location of the mental foramen and accesories in relation to a lower tooth, according to the classification of Al Jasser- Nwoku.The distance of the mental foramen on the left side to the alveolar ridge had an average of 12.62 mm and on the right side it had a mean of 12.90 mm and the distance from the mandibular ridge on the left side showed an average of 14.14 mm and on the right side was 13.91 mm. The relationship of the mental foramen to the teeth was located below the second mandibular premolar. The 14% presented accessory hole, predominating position 4 (at the level of the second premolar).


2017 ◽  
Vol 20 (1) ◽  
pp. 69-77
Author(s):  
Juana R. Delgadillo Avila DDS, MSc, PhD ◽  
Manuel A. Mattos-Vela DDS, MSc, PhD

The aim of the present study was the determine the location of the mental foramen and accessories, their relationships with the alveolar and basal rims found in Peruvians adults. A descriptive, transverse and retrospective study was carried out. The sample was composed of 100 cone beam CT scans of patients, between the ages of 20 to 55 years of age, these patients were attended at the clinic of the Faculty of Dentistry of Mayor National University of San Marcos UNMSM. Were considered tomograms of mandibular dentate patients taken during 2015, classified according to age and sex, identifying in them the location of the mental foramen and accesories in relation to a lower tooth, according to the classification of Al Jasser- Nwoku.The distance of the mental foramen on the left side to the alveolar ridge had an average of 12.62 mm and on the right side it had a mean of 12.90 mm and the distance from the mandibular ridge on the left side showed an average of 14.14 mm and on the right side was 13.91 mm. The relationship of the mental foramen to the teeth was located below the second mandibular premolar. The 14% presented accessory hole, predominating position 4 (at the level of the second premolar).


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