Dental age estimation using the pulp-to-tooth ratio in canines by neural networks

Maryam Farhadian,Fatemeh Salemi,Samira Saati,Nika Nafisi 대한영상치의학회 2019 Imaging Science in Dentistry Vol.49 No.1

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.

Dental age estimation using the pulp-to-tooth ratio in canines by neural networks

Farhadian, Maryam,Salemi, Fatemeh,Saati, Samira,Nafisi, Nika Korean Academy of Oral and Maxillofacial Radiology 2019 Imaging Science in Dentistry Vol.49 No.1

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.

Comparison of data mining algorithms for sex determination based on mastoid process measurements using cone-beam computed tomography

Farhadian, Maryam,Salemi, Fatemeh,Shokri, Abbas,Safi, Yaser,Rahimpanah, Shahin Korean Academy of Oral and Maxillofacial Radiology 2020 Imaging Science in Dentistry Vol.50 No.4

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.

Predicting 5-Year Survival Status of Patients with Breast Cancer based on Supervised Wavelet Method

Maryam Farhadian,Hossein Mahjub,Jalal Poorolajal,Abbas Moghimbeigi,Muharram Mansoorizadeh 질병관리본부 2014 Osong Public Health and Research Persptectives Vol.5 No.6

Objectives: Classification of breast cancer patients into different risk classes is very important in clinical applications. It is estimated that the advent of highdimensional gene expression data could improve patient classification. In this study, a new method for transforming the high-dimensional gene expression data in a low-dimensional space based on wavelet transform (WT) is presented. Methods: The proposed method was applied to three publicly available microarray data sets. After dimensionality reduction using supervised wavelet, a predictive support vector machine (SVM) model was built upon the reduced dimensional space. In addition, the proposed method was compared with the supervised principal component analysis (PCA). Results: The performance of supervised wavelet and supervised PCA based on selected genes were better than the signature genes identified in the other studies. Furthermore, the supervised wavelet method generally performed better than the supervised PCA for predicting the 5-year survival status of patients with breast cancer based on microarray data. In addition, the proposed method had a relatively acceptable performance compared with the other studies. Conclusion: The results suggest the possibility of developing a new tool using wavelets for the dimension reduction of microarray data sets in the classification framework.

Sex determination from lateral cephalometric radiographs using an automated deep learning convolutional neural network

Khazaei Maryam,Mollabashi Vahid,Khotanlou Hassan,Farhadian Maryam 대한영상치의학회 2022 Imaging Science in Dentistry Vol.52 No.3

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.

Sex determination from lateral cephalometric radiographs using an automated deep learning convolutional neural network

Khazaei, Maryam,Mollabashi, Vahid,Khotanlou, Hassan,Farhadian, Maryam Korean Academy of Oral and Maxillofacial Radiology 2022 Imaging Science in Dentistry Vol.52 No.-

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.

Accuracy of maxillofacial prototypes fabricated by different 3-dimensional printing technologies using multi-slice and cone-beam computed tomography

Yousefi, Faezeh,Shokri, Abbas,Farhadian, Maryam,Vafaei, Fariborz,Forutan, Fereshte Korean Academy of Oral and Maxillofacial Radiology 2021 Imaging Science in Dentistry Vol.51 No.1

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.

The Association between Social Support and Happiness among Elderly in Iran

Babak Moeini,Majid Barati,Maryam Farhadian,Milad Heydari Ara 대한가정의학회 2018 Korean Journal of Family Medicine Vol.39 No.4

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 impactof 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-stagerandom sampling method. The research instrument included a questionnaire consisting of three parts: demographicinformation, the Oxford Argyle Happiness Inventory, and a Questionnaire derived from Social SupportTheory. The questionnaire was completed through a self-report study. The collected data were analyzed usingPearson correlation coefficients, multiple linear regression, independent t-tests, and one-way analysis of variancein 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±11.95and 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 approximately25% (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 supportcan be taken into account as proper determinants and predictors of happiness among elders.

Effect of different surface treatments on shear bond strength of ceramic brackets to old composite

Homa Farhadifard,Loghman Rezaei-Soufi,Maryam Farhadian,Parisa Shokouhi 한국생체재료학회 2020 생체재료학회지 Vol.24 No.4

Background: At present, the demand for orthodontic treatment is on the rise. On the other hand, evidence shows that the bond strength of composite resins to old composite restorations is often unreliable. Therefore, the aim of this in vitro study was to assess the effect of different surface treatments on shear bond strength (SBS) of ceramic brackets to old composite restorations. Methods: In this in vitro experimental study, 60 nano-hybrid composite discs were fabricated. For aging, the discs were incubated in deionized water at 37 °C for 1 month. Next, they underwent 4 different surface treatments namely acid etching with 37% phosphoric acid, sandblasting, grinding, and Er,Cr:YSGG laser irradiation. Ceramic brackets were then bonded to the discs and underwent SBS testing. Results: The maximum mean SBS value was obtained in the grinding group (9.16 ± 2.49 MPa), followed by the sandblasting (8.13 ± 2.58 MPa) and laser (6.57 ± 1.45 MPa) groups. The minimum mean SBS value was noted in the control group (5.07 ± 2.14 MPa). Conclusion: All groups except for the control group showed clinically acceptable SBS. Therefore, grinding, sandblasting, and Er,Cr:YSGG laser are suggested as effective surface treatments for bonding of ceramic orthodontic brackets to aged composite.

Assessment of the accuracy of laser-scanned models and 3-dimensional rendered cone-beam computed tomographic images compared to digital caliper measurements on plaster casts

Yousefi, Faezeh,Shokri, Abbas,Zahedi, Foozie,Farhadian, Maryam Korean Academy of Oral and Maxillofacial Radiology 2021 Imaging Science in Dentistry Vol.51 No.-

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.