Purpose: Despite the proliferation of numerous morphometric and anthropometric methods for sex identification based on linear, angular, and regional measurements of various parts of the body, these methods are subject to error due to the observer’s knowledge and expertise. This study aimed to explore the possibility of automated sex determination using convolutional neural networks (CNNs) based on lateral cephalometric radiographs. Materials and Methods: Lateral cephalometric radiographs of 1,476 Iranian subjects (794 women and 682 men) from 18 to 49 years of age were included. Lateral cephalometric radiographs were considered as a network input and output layer including 2 classes (male and female). Eighty percent of the data was used as a training set and the rest as a test set. Hyperparameter tuning of each network was done after preprocessing and data augmentation steps. The predictive performance of different architectures (DenseNet, ResNet, and VGG) was evaluated based on their accuracy in test sets. Results: The CNN based on the DenseNet121 architecture, with an overall accuracy of 90%, had the best predictive power in sex determination. The prediction accuracy of this model was almost equal for men and women. Furthermore, with all architectures, the use of transfer learning improved predictive performance. Conclusion The results confirmed that a CNN could predict a person’s sex with high accuracy. This prediction was independent of human bias because feature extraction was done automatically. However, for more accurate sex determination on a wider scale, further studies with larger sample sizes are desirable.

BACKGROUND: Fire service workers often implement multiple duties in the emergency conditions, with such duties being mostly conducted in various ambient temperatures. METHODS: The aim of the current study was to assess the firefighters' physiological responses, information processing, and working memory prior to and following simulated firefighting activities in three different hot environments. Seventeen healthy male firefighters performed simulated firefighting tasks in three separate conditions, namely (1) low heat (LH; 29–31°C, 55–60% relative humidity), (2) moderate heat (MH; 32–34°C, 55–60% relative humidity), and (3) severe heat (SH; 35–37°C, 55–60% relative humidity). It took about 45–50 minutes for each firefighter to finish all defined firefighting activities and the paced auditory serial addition test (PASAT). RESULTS: At the end of all the three experimental conditions, heart rate (HR) and tympanic temperature (TT) increased, while PASAT scores as a measure of information processing performance decreased relative to baseline. HR and TT were significantly higher at the end of the experiment in the SH (159.41 ± 4.25 beats/min; 38.22 ± 0.10°C) compared with the MH (156.59 ± 3.77 beats/min; 38.20 ± 0.10°C) and LH (154.24 ± 4.67 beats/min; 38.17 ± 0.10°C) conditions (p < 0.05). There was no significant difference in PASAT scores between LH and MH (p > 0.05). Nonetheless, there was a measurable difference in PASAT scores between LH and SH (p < 0.05). CONCLUSION: These consequences demonstrate that ambient temperature is effective in raising the physiological responses following firefighting activities. It is therefore argued that further increase of ambient temperature can impact firefighters' information processing and working memory during firefighting activity.
Automatic Data Processing* ; Emergencies ; Firefighters ; Fires ; Heart Rate ; Hot Temperature ; Humans ; Male ; Memory, Short-Term

Purpose: This study investigated the accuracy of laser-scanned models and 3-dimensional (3D) rendered cone-beam computed tomography (CBCT) compared to the gold standard (plaster casts) for linear measurements on dental arches. Materials and Methods: CBCT scans and plaster models from 30 patients were retrieved. Plaster models were scanned by an Emerald laser scanner (Planmeca, Helsinki, Finland). Sixteen different measurements, encompassing the mesiodistal width of teeth and both arches’ length and width, were calculated using various landmarks. Linear measurements were made on laser-scanned models using Autodesk Meshmixer software v. 3.0 (Autodesk, Mill Valley, CA, USA), on 3D-rendered CBCT models using OnDemand 3D v. 1.0 (Cybermed, Seoul, Korea) and on plaster casts by a digital caliper. Descriptive statistics, the paired t-test, and intra- and inter-class correlation coefficients were used to analyze the data. Results: There were statistically significant differences between some measurements on plaster casts and laser-scanned or 3D-rendered CBCT models (P<0.05). Molar mesiodistal width and mandibular anterior arch width deviated significantly different from the gold standard in both methods. The largest mean differences of laser-scanned and 3D-rendered CBCT models compared to the gold standard were 0.12±0.23 mm and 0.42±0.53 mm, respectively. Most of the mean differences were not clinically significant. The intra- and inter-class correlation results were acceptable for all measurements (>0.830) and between observers (>0.801). Conclusion The 3D-rendered CBCT images and laser-scanned models were useful and accurate alternatives to conventional plaster models. They could be used for clinical purposes in orthodontics and prostheses.

Purpose: Sex determination can be done by morphological analysis of different parts of the body. The mastoid region, with its anatomical location at the skull base, is ideal for sex identification. Statistical shape analysis provides a simultaneous comparison of geometric information on different shapes in terms of size and shape features. This study aimed to investigate the geometric morphometry of the inter-mastoid triangle as a tool for sex determination in the Iranian population. Materials and Methods: The coordinates of 5 landmarks on the mastoid process on the 80 cone-beam computed tomographic images (from individuals aged 17-70 years, 52.5% female) were registered and digitalized. The Cartesian x-y coordinates were acquired for all landmarks, and the shape information was extracted from the principal component scores of generalized Procrustes fit. The t-test was used to compare centroid size. Cross-validated discriminant analysis was used for sex determination. The significance level for all tests was set at 0.05. Results: There was a significant difference in the mastoid size and shape between males and females (P<0.05). The first 2 components of the Procrustes shape coordinates explained 91.3% of the shape variation between the sexes. The accuracy of the discriminant model for sex determination was 88.8%. Conclusion The application of morphometric geometric techniques will significantly impact forensic studies by providing a comprehensive analysis of differences in biological forms. The results demonstrated that statistical shape analysis can be used as a powerful tool for sex determination based on a morphometric analysis of the inter-mastoid triangle.

Purpose: This study aimed to compare the accuracy of 3-dimensional (3D) printed models derived from multidetector computed tomography (MDCT) and cone-beam computed tomography (CBCT) systems with different fields of view (FOVs). Materials and Methods: Five human dry mandibles were used to assess the accuracy of reconstructions of anatomical landmarks, bone defects, and intra-socket dimensions by 3D printers. The measurements were made on dry mandibles using a digital caliper (gold standard). The mandibles then underwent MDCT imaging. In addition, CBCT images were obtained using Cranex 3D and NewTom 3G scanners with 2 different FOVs. The images were transferred to two 3D printers, and the digital light processing (DLP) and fused deposition modeling (FDM) techniques were used to fabricate the 3D models, respectively. The same measurements were also made on the fabricated prototypes. The values measured on the 3D models were compared with the actual values, and the differences were analyzed using the paired t-test. Results: The landmarks measured on prototypes fabricated using the FDM and DLP techniques based on all 4 imaging systems showed differences from the gold standard. No significant differences were noted between the FDM and DLP techniques. Conclusion The 3D printers were reliable systems for maxillofacial reconstruction. In this study, scanners with smaller voxels had the highest precision, and the DLP printer showed higher accuracy in reconstructing the maxillofacial landmarks. It seemed that 3D reconstructions of the anterior region were overestimated, while the reconstructions of intra-socket dimensions and implant holes were slightly underestimated.

BACKGROUND: Labeled noise reduction (NR) data presented by manufacturers are considered one of the main challenging issues for occupational experts in employing hearing protection devices (HPDs). This study aimed to determine the actual NR data of typical HPDs using the objective fit testing method with a microphone in real ear (MIRE) method. METHODS: Five available commercially earmuff protectors were investigated in 30 workers exposed to reference noise source according to the standard method, ISO 11904-1. Personal attenuation rating (PAR) of the earmuffs was measured based on the MIRE method using a noise dosimeter (SVANTEK, model SV 102). RESULTS: The results showed that means of PAR of the earmuffs are from 49% to 86% of the nominal NR rating. The PAR values of earmuffs when a typical eyewear was worn differed statistically (p < 0.05). It is revealed that a typical safety eyewear can reduce the mean of the PAR value by approximately 2.5 dB. The results also showed that measurements based on the MIRE method resulted in low variability. The variability in NR values between individuals, within individuals, and within earmuffs was not the statistically significant (p > 0.05). CONCLUSION: This study could provide local individual fit data. Ergonomic aspects of the earmuffs and different levels of users experience and awareness can be considered the main factors affecting individual fitting compared with the laboratory condition for acquiring the labeled NR data. Based on the obtained fit testing results, the field application of MIRE can be employed for complementary studies in real workstations while workers perform their regular work duties.
Ear Protective Devices ; Ear* ; Hearing* ; Humans ; Methods ; Noise

PURPOSE: It has been proposed that using new prediction methods, such as neural networks based on dental data, could improve age estimation. This study aimed to assess the possibility of exploiting neural networks for estimating age by means of the pulp-to-tooth ratio in canines as a non-destructive, non-expensive, and accurate method. In addition, the predictive performance of neural networks was compared with that of a linear regression model. MATERIALS AND METHODS: Three hundred subjects whose age ranged from 14 to 60 years and were well distributed among various age groups were included in the study. Two statistical software programs, SPSS 21 (IBM Corp., Armonk, NY, USA) and R, were used for statistical analyses. RESULTS: The results indicated that the neural network model generally performed better than the regression model for estimation of age with pulp-to-tooth ratio data. The prediction errors of the developed neural network model were acceptable, with a root mean square error (RMSE) of 4.40 years and a mean absolute error (MAE) of 4.12 years for the unseen dataset. The prediction errors of the regression model were higher than those of the neural network, with an RMSE of 10.26 years and a MAE of 8.17 years for the test dataset. CONCLUSION: The neural network method showed relatively acceptable performance, with an MAE of 4.12 years. The application of neural networks creates new opportunities to obtain more accurate estimations of age in forensic research.
Cone-Beam Computed Tomography ; Dataset ; Forensic Dentistry ; Humans ; Linear Models ; Methods ; Neural Networks (Computer)

BACKGROUND: Elderly people's life is affected by multiple factors including social support, which is of the utmost importance. This study aimed to explore the association between social support and happiness as well as the impact of types of social support on happiness among elders. METHODS: This descriptive and analytical study was carried out on 411 elderly men and women referred to the retirement, cultural, and rehabilitation centers in Hamadan, west of Iran. Participants were selected by a multi-stage random sampling method. The research instrument included a questionnaire consisting of three parts: demographic information, the Oxford Argyle Happiness Inventory, and a Questionnaire derived from Social Support Theory. The questionnaire was completed through a self-report study. The collected data were analyzed using Pearson correlation coefficients, multiple linear regression, independent t-tests, and one-way analysis of variance in IBM SPSS Software ver. 22.0 (IBM Corp., Armonk, NY, USA). RESULTS: The mean for happiness was reported as 41.17±15.2. The values given for social support were 29.40±11.95 and for its dimensions were 7.53±3.89 and 13.70±4.90 for informational support and emotional support, respectively. Moreover, the mean value for appraisal support was 3.48±2.37 and was 4.70±2.56 for instrumental support. Multiple linear regression analysis revealed that social support and demographic variables could account for approximately 25% (R2=0.25) of changes in the variable of happiness. CONCLUSION: High social support could increase happiness among elders. The quality and quantity of social support can be taken into account as proper determinants and predictors of happiness among elders.
Aged* ; Female ; Happiness* ; Humans ; Iran* ; Linear Models ; Male ; Methods ; Rehabilitation Centers ; Retirement

Purpose: This study aimed to assess incidental abnormal findings in the paranasal sinuses and anatomical variations of the ostiomeatal complex (OMC) on magnetic resonance imaging (MRI) scans. Materials and Methods: MRI scans of 616 patients (mean age, 44.0±19.4 years) were evaluated. Prior to obtaining the MRI scans, a checklist of patients’ clinical symptoms was filled out after obtaining their consent. The Lund-Mackay classification was used to assess the paranasal sinuses and OMC. The prevalence of abnormal findings and their associations with patients’ age, sex, and subjective symptoms were analyzed by the chi-square test, independent-sample t-test, and analysis of variance. The level of significance was set at 0.05. Results: Abnormal findings in the paranasal sinuses were detected in 32.0% of patients, with a significantly higher prevalence in males (P<0.05), but no significant association with age (P>0.05). Epithelial thickening and retention cyst were the most common abnormal findings in the paranasal sinuses. According to the Lund-Mackay classification, 93% of the study population had normal sinuses (score<4). Concha bullosa and paradoxical concha were detected in 15.3% and 3.4%, respectively, with no significant association with the presence of septal deviation or Lund-Mackay classification (P>0.05). Conclusion Considering the relatively high prevalence of abnormal findings in the paranasal sinuses, it appears that clinical symptoms alone are not sufficient to diagnose sinusitis. A more accurate strategy would be to assess radiographic images of the paranasal sinuses and use a classification system. Sinusitis should be suspected in patients receiving a high score in this classification.

Purpose: The mastoid region is ideal for studying sexual dimorphism due to its anatomical position at the base of the skull. This study aimed to determine sex in the Iranian population based on measurements of the mastoid process using different data mining algorithms. Materials and Methods: This retrospective study was conducted on 190 3-dimensional cone-beam computed tomographic (CBCT) images of 105 women and 85 men between the ages of 18 and 70 years. On each CBCT scan, the following 9 landmarks were measured: the distance between the porion and the mastoidale; the mastoid length, height, and width; the distance between the mastoidale and the mastoid incision; the intermastoid distance (IMD); the distance between the lowest point of the mastoid triangle and the most prominent convex surface of the mastoid (MF); the distance between the most prominent convex mastoid point (IMSLD); and the intersecting angle drawn from the most prominent right and left mastoid point (MMCA). Several predictive models were constructed and their accuracy was compared using cross-validation. Results: The results of the t-test revealed a statistically significant difference between the sexes in all variables except MF and MMCA. The random forest model, with an accuracy of 97.0%, had the best performance in predicting sex. The IMSLD and IMD made the largest contributions to predicting sex, while the MMCA variable had the least significant role. Conclusion These results show the possibility of developing an accurate tool using data mining algorithms for sex determination in the forensic framework.