Age Prediction Method Research Articles

Hybrid deep model for brain age prediction in MRI with improved chi-square based selected features

Ageing and its related health conditions bring many challenges not only to individuals but also to society. Various MRI techniques are defined for the early detection of age-related diseases. Researchers continue the prediction with the involvement of different strategies. In that manner, this research intends to propose a new brain age prediction model under the processing of certain steps like preprocessing, feature extraction, feature selection, and prediction. The initial step is preprocessing, where improved median filtering is proposed to reduce the noise in the image. After this, feature extraction takes place, where shape-based features, statistical features, and texture features are extracted. Particularly, Improved LGTrP features are extracted. However, the curse of dimensionality becomes a serious issue in this aspect that shrinks the efficiency of the prediction level. According to the “curse of dimensionality,” the number of samples required to estimate any function accurately increases exponentially as the number of input variables increases. Hence, a feature selection model with improvement has been introduced in this paper termed an improved Chi-square. Finally, for prediction purposes, a Hybrid classifier is introduced by combining the models like Bi-GRU and DBN, respectively. In order to enhance the effectiveness of the hybrid method, Upgraded Blue Monkey Optimization with Improvised Evaluation (UBMOIE) is introduced as the training system by tuning the optimal weights in both classifiers. Finally, the performance of the suggested UBMIOE-based brain age prediction method was assessed over the other schemes to various metrics.

Prediction of the age of abalones based on machine learning algorithms

Abalone is an important seafood, widely used in food, medicine, and other fields. The age of abalone is one of the important factors that determine its quality and market value. However, the traditional age determination method requires the dissection of abalone, which is time-consuming and expensive. Therefore, it is important to find a fast and work out age prediction method. This article uses a machine learning algorithm to predict the age of abalone. The authors collected data on characteristics such as sex, length, diameter, height, and weight for 4177 abalone observations. This data set is admirably large. In the following study, the authors compare the effects of prediction using different machine learning algorithms, including linear regression, decision trees, random forests, and support vector machines. It is worth mentioning that the authors have done sufficient research and evaluation of these algorithms to find out the best prediction scheme. The results show that the random forest algorithm is the best, and its average absolute error is only 1.44 years. The performance of random forest algorithm is the best.

Dynamic evolution of brain structural patterns in liver transplantation recipients: a longitudinal study based on 3D convolutional neuronal network model.

To evaluate the dynamic evolution process of overall brain health in liver transplantation (LT) recipients, we employed a deep learning-based neuroanatomic biomarker to measure longitudinal changes of brain structural patterns before and 1, 3, and 6months after surgery. Because of the ability to capture patterns across all voxels from a brain scan, the brain age prediction method was adopted. We constructed a 3D-CNN model through T1-weighted MRI of 3609 healthy individuals from 8 public datasets and further applied it to a local dataset of 60 LT recipients and 134 controls. The predicted age difference (PAD) was calculated to estimate brain changes before and after LT, and the network occlusion sensitivity analysis was used to determine the importance of each network in age prediction. The PAD of patients with cirrhosis increased markedly at baseline (+ 5.74years) and continued to increase within one month after LT (+ 9.18years). After that, the brain age began to decrease gradually, but it was still higher than the chronological age. The PAD values of the OHE subgroup were higher than those of the no-OHE, and the discrepancy was more obvious at 1-month post-LT. High-level cognition-related networks were more important in predicting the brain age of patients with cirrhosis at baseline, while the importance of primary sensory networks increased temporarily within 6-month post-LT. The brain structural patterns of LT recipients showed inverted U-shaped dynamic change in the early stage after transplantation, and the change in primary sensory networks may be the main contributor. • The recipients' brain structural pattern showed an inverted U-shaped dynamic change after LT. • The patients' brain aging aggravated within 1month after surgery, and the subset of patients with a history of OHE was particularly affected. • The change of primary sensory networks is the main contributor to the change in brain structural patterns.

Accurate age estimation from blood samples of Han Chinese individuals using eight high-performance age-related CpG sites

Age-related CpG sites (AR-CpGs) are currently the most promising biomarkers for forensic age estimation. In our previous studies, we first validated the age correlation of seven reported AR-CpGs in blood samples of Chinese Han population. Subsequently, we screened some good age predictors from blood samples of Chinese Han population, and built pyrosequencing-based age prediction models. However, it is still important to select a set of high-performance AR-CpGs in a specific racial group and establish a simple and efficient method for accurate age estimation for forensic purpose. In this study, eight AR-CpGs, namely chr6: 11,044,628 (ELOVL2), cg06639320 (FHL2), chr1: 207,823,723 (C1orf132), cg19283806 (CCDC102B), cg14361627 (KLF14), cg17740900 (SYNE2), cg07553761 (TRIM59), and cg26947034, were selected based on our previous studies, and a multiplex methylation SNaPshot assay was developed to investigate DNA methylation levels at these AR-CpGs in 529 blood samples (aged 2-82years) from Han Chinese population. All selected CpG sites showed strong age correlation with the correlation coefficient (r) from 0.8363 to 0.9251. Multiple linear regression (MLR) and support vector regression (SVR) age prediction models were simultaneously established to fit change characteristics of DNA methylation levels of eight AR-CpGs with the age in 374 donors' blood samples. The MLR model enabled age prediction with R2 = 0.923, mean absolute error (MAE) = 3.52, while the SVR model enabled age prediction with R2 = 0.935, MAE = 2.88. One hundred fifty-five independent samples were used as a validation set to test the two models' performance, and the prediction MAE for the validation set was 3.71 and 3.34 for the MLR and SVR models, respectively. For the MLR and SVR models, the correct prediction rate at ± 5years reached a high level of 79.35% and 83.23%, respectively. In general, these statistical parameters indicated that the SVR model outperformed the MLR model in age prediction of the Han Chinese population. In addition, our method provides sufficient sensitivity in forensic applications and allows for 100% efficiency when examining bloodstains kept in room conditions for up to 43days. These results indicate that our multiplex methylation SNaPshot assay is a reliable, effective, and accurate method for age prediction in blood samples from the Chinese Han population.

Forensic age prediction and age classification for critical age thresholds via 3.0T magnetic resonance imaging of the knee in the Chinese Han population.

To assess the performance of knee MRI for forensic age prediction and classification for 12-, 14-, 16-, and 18-year thresholds. The ossification stages of distal femoral epiphyses and proximal tibial epiphyses were assessed using an integrated staging system by Schmeling et al. and Kellinghaus et al. for knee 3.0T MRI with T1-weighted turbo spin-echo (T1-TSE) in sagittal orientation among 852 Chinese Han individuals (483 males and 369 females) aged 7-30 years. Regression models for age prediction were constructed and their performances were evaluated based on mean absolute deviation (MAD) values. In addition, the performances of age classification were assessed using receiver operating characteristic (ROC) analyses. The intra- and inter-observer agreement levels were very good (κ > 0.80). The complete fusion of those two types of epiphyses took place before 18.0 years in our study participants. The minimum MAD values were 2.51 years (distal femur) and 2.69 years (proximal tibia) in males, and 2.75 years (distal femur) and 2.87 years (proximal tibia) in females. The specificity values of constructed prediction models were all above 90% for the 12-, 14-, and 16-year thresholds, compared to the 74.8-84.6% for the 18-year threshold. Better performances of age prediction and classification were observed in males by distal femoral epiphyses. Ossification stages via 3.0T MRI of the knee with T1-TSE sequence using an integrated staging system could be a reliable noninvasive method for age prediction or for age classification for 12-, 14-, and 16-year thresholds, especially in males by distal femoral epiphyses. However, assessments based on the full bony fusion of the distal femoral epiphysis and proximal tibial epiphysis seemed not reliable for age classification for the 18-year threshold in the Chinese Han population.

Some tips on age estimation using DNA methylation in saliva samples as an index across the Japanese and Indonesian ethnicities

Age estimation of unidentified bodies is of marked importance in forensic medicine. In previous studies, the analysis of DNA methylation in body fluids led to the identification of several age-related CpG sites in genes such as EDARADD and FHL2. However, limited information is available on whether interethnic differences may affect the age prediction results. In the present study, we examined the effect of ethnicity on the age prediction method based on methylation scores, which were determined via methylation-sensitive high-resolution melting. We found that there was a significant difference in methylation scores between Japanese and Indonesian participants of early 20 s group, and that the nationality coefficient was significant for age estimation when applying the existing method for the analysis of the methylation status of EDARADD and FHL2. This suggests that when using certain biochemical indicators as a predictor of age, the effects of ethnicity on DNA methylation should be considered to improve the accuracy of the estimation.

Region of Interest Localization for Bone Age Estimation Using Whole-Body Bone Scintigraphy

In the past decade, deep learning has been applied to various medical image analysis tasks. Skeletal bone age estimation is clinically important as it can help prevent age-related illness and pave the way for new anti-aging therapies. Recent research has applied deep learning techniques to the task of bone age assessment and achieved positive results. In this paper, we propose a bone age prediction method using a deep convolutional neural network. Specifically, we first train a classification model that automatically localizes the most discriminative region of an image and crops it from the original image. The regions of interest are then used as input for a regression model to estimate the age of the patient. The experiments are conducted on a whole-body scintigraphy dataset that was collected by Chonnam National University Hwasun Hospital. The experimental results illustrate the potential of our proposed method, which has a mean absolute error of 3.35 years. Our proposed framework can be used as a robust supporting tool for clinicians to prevent age-related diseases.

A Computational Complexity-Based Method for Predicting Scholars’ Ages through Articles’ Information

Many scholars have conducted in-depth research on the evaluation and prediction of scholars’ scientific impact and meanwhile discovered various factors that affect the success of scholars. Among all these relevant factors, scholars’ ages have been universally acknowledged as one of the most important factors for it can shed light on many practical issues, e.g., finding supervisors, discovering rising stars, and research funding or award applications. However, due to the inaccessibility or the privacy issues of acquiring scholars’ personal data, there is little research to explore the true ages of scholars currently. Alternatively, scholars’ publications’ information can be obtained through various digital libraries. Inspired by this fact, we propose a novel scholar’s age prediction method based on their articles’ information. Our method first classifies factors that affect scholars’ ages into intuitive and complex types according to their computational complexity and then apply machine learning algorithms to predict the ages of scholars based on these factors. The experimental results on the real dataset demonstrate that our method can effectively predict the true ages of scholars. Given that there is no completely accurate dataset because of the continuous publication of academic papers, we then apply our method on the incomplete dataset. Nevertheless, our method still has high prediction accuracy in such situations.

Individuals Age Determination from Human Dental Pulp Through DNA Analysis by PCR.

Objectives Age estimation in forensic odontology is having a great importance in recent times because of the request by court or other government authorities so that immigrants whose real age is unknown should not suffer unfair disadvantages because of their supposed age, and so that all legal procedures to which an individual's age is relevant can be properly followed. Purpose The present study was planned to be conducted on pulp tissue and dental hard tissues derived from individuals for DNA isolation and age determination . Materials and Methods The present study was an experimental single-blinded study consisting of 30 extracted teeth categorized into three groups as follows: Group A: 10 to 20 years, Group B: 21 to 30 years, Group C: 31 to 40 years. DNA was isolated from the pulp of each tooth and quantitative polymerase chain reaction (qPCR) for calculating telomere length was performed. Results With increase in age, the length of telomere gets shortened which will be helpful in analyzing the age of the person when morphological and biological remnants are not available except the tooth. Conclusion The present study found that estimation of human age based on the relative TL measured by the real-time quantitative PCR may be a useful method for age prediction, especially when there is no morphologic information in the biological sample. This is the first study to accesses the age of a person by telomere length using dental pulp.

14086 A new multimodal age prediction image analysis method from hands images of different age groups by neural network model

Several papers on age prediction using face images have been published while very limited studies on hands aging have been conducted. Hands aging has a great impact on perceived age and is becoming increasingly important to physicians and their patients. To fill this gap, a novel age prediction method based on the hand images is developed. Age prediction models based on face images are well investigated mostly because of the persistence of massive datasets like IMDB-WIKI containing more than 500,000 face images. We captured face and hands images of 994 Indian women with age ranging from 20 to 60 years with different skin tones by VISIA CR. We developed two deep neural network architectures for the face and back of the hands, that can be used separately or in conjunction for age prediction. A novel neural network architecture works on hand images and predicts age based on the seven specified areas of the hand image such as central and bottom part of the backhand, knuckles areas of fingers. Predictions for all these seven areas are aggregated for the final age determination. This method based only on hands images showed a great association between actual age and predicted age with mean average error (MAE) of 5.89 years. We got MAE of 6.8 years for face-based age predictor. Our research can help to measure the complex effects of anti-aging products or any dermatologic procedures after treating aging hands objectively and as part of a multimodal age predictor.

Bone Age Prediction Method Based on Convolutional Neural Network

How to accurately observe whether children are developing normally has always been one of the focuses of medical research. Based on the convolutional neural network, this paper proposed a bone age prediction method. Firstly, the hand bone data set was constructed. Secondly, the prior frame required by the neural network was obtained by K-means clustering. Finally, fine-tuning, training, detecting and cropping were performed to achieve bone age classification. The experimental results show that our method achieves the smallest average error and has strong practicability.

A New Robust Epigenetic Model for Forensic Age Prediction.

Forensic DNA phenotyping refers to an emerging field of forensic sciences aimed at the prediction of externally visible characteristics of unknown sample donors directly from biological materials. The aging process significantly affects most of the above characteristics making the development of a reliable method of age prediction very important. Today, the so-called "epigenetic clocks" represent the most accurate models for age prediction. Since they are technically not achievable in a typical forensic laboratory, forensic DNA technology has triggered efforts toward the simplification of these models. The present study aimed to build an epigenetic clock using a set of methylation markers of five different genes in a sample of the Italian population of different ages covering the whole span of adult life. In a sample of 330 subjects, 42 selected markers were analyzed with a machine learning approach for building a prediction model for age prediction. A ridge linear regression model including eight of the proposed markers was identified as the best performing model across a plethora of candidates. This model was tested on an independent sample of 83 subjects providing a median error of 4.5 years. In the present study, an epigenetic model for age prediction was validated in a sample of the Italian population. However, its applicability to advanced ages still represents the main limitation in forensic caseworks.

DNA methylation-based age prediction using cell separation algorithm

The age of each individual can be predicted based on the alteration rule of DNA methylation with age. In this paper, an age prediction method is developed in order to solve multivariate regression problems from DNA methylation data, by optimizing the artificial neural network (ANN) model using a new proposed algorithm named the Cell Separation Algorithm (CSA). The CSA imitates cell separation action by using a differential centrifugation process involving multiple centrifugation steps and increasing the rotor speed in each step. The CSA performs similar to the centrifugal force in separating the solutions based on their objective function in different steps, with velocity increasing in each step. Firstly, 25 test functions are used to test the CSA. Secondly, the CSA is examined on three forms of age prediction problems from two body fluids (blood and saliva). The healthy blood samples, diseased blood samples and saliva samples are used to test the method's capability. The results of the CSA are compared not only with other methods proposed in previous studies, but also with the results from stochastic gradient descent (SGD), ADAM, and genetic algorithm (GA). The model results of CSA are extremely better than the four methods proposed in previous works that have not used ANN training process. The CSA also outperformed SGD, ADAM that employ the ANN model without ANN optimization by meta-heuristics. The CSA results are comparable (even superior) to the GA model which takes the advantages of both ANN and meta-heuristics.

Local and global features fusion to estimate expression invariant human age

Human beings can easily estimate the age or age group of a person from a facial image where as this capability is not prominent in machines. This problem becomes more complex due to presence of different facial expressions and age progression. In this paper, we introduced a novel method for age prediction using combination of local and global features. After detecting the face from image, we partition the facial image in 16 * 16 non-overlapping blocks and apply grey-level co-occurrence matrix (GLCM) on these blocks. After calculating four facial parts (eyes, forehead, left cheek and right cheek) from facial image, features from second local feature Gabor filter are obtained. Histogram of oriented gradients has been used as a global feature for feature extraction from complete face image. Experimental results show that fusion of local and global features perform better than existing approaches and reported 6.31 years mean absolute error (MAE) on PAL dataset.

Validation of near infrared spectroscopy as an age-prediction method for plastics

This work has two aims. Firstly, to validate the ability of experimental models derived through near infrared spectroscopy for acrylonitrile butadiene styrene (ABS), low-density polyethylene (LDPE), polyethylene terephthalate (PET), and polypropylene (PP) in predicting polymers’ aging; focusing on the degree of oxidation. Secondly, to assess the reliability of non-invasive age-predictive models on waste plastic samples and on mechanically recycled samples. Aging time, temperature and number of extrusion cycles were selected as independent variables to build the aging-prediction models, where they were calibrated on samples subjected to controlled conditions. The accuracy of the prediction models was assessed on external samples (aged under known conditions) through the cross correlation technique and the Root Mean Square Error (RMSE). The models exhibited good collinearity for the aging temperature and the number of extrusion cycles factors for all tested polymers, but not for the aging time factor. The RSME value of the aging time factor was far from zero for all polymers. Plastic waste samples provided analogous results; the aging time estimation was mostly negative in value. The estimations of aging time and number of extrusion cycles were always positive in values, where the most reasonable aging factor estimation was the number of extrusion cycles.

Sagittal suture maturation: Morphological reorganization, relation to aging, and reliability as an age-at-death indicator.

The sagittal suture (SS) is assumed to be an initial site for the commencement of cranial suture closure as well as the most frequent spot of isolated craniosynostosis. The present study aimed to inspect the reorganization of the SS at the microlevel to assess the relation between its closure and aging and to establish whether it could be used as a reliable indicator in age-at-death prediction. The SS was investigated in 68 dry contemporary adult male skulls of known age-at-death. An additional series of 20 skulls was used for verification. The skulls were scanned using a micro-computed tomography system. The SS closure degree was assessed along the three bone layers on cross-sectional tomograms by using a scoring scale. In the entirely open SS, the bone edges consist of compact bone and are widely separated. With SS maturation, the bone edges come into contact, and the remodeling process leads to a decrease in the sutural area and bone homogenization across all three layers. SS closure is an irregular process roughly related to aging, beginning in the early 20s, reaching its peak at about 30 years of age and abating in the late 40s. Although related to aging, SS closure is not a simple function of it. Rather, the underlying factors inducing and managing this process are multifaceted and complex. Although the etiology of SS maturation remains unclear, it is reasonable to use SS closure cautiously and only as a supportive method for age prediction.

Neural Networks in Video-Based Age and Gender Recognition on Mobile Platforms

The paper considers the use of convolutional neural networks for the concurrent recognition of the gender and age of a person by video records of his face. The emphasis is on the incorporation of the approach into mobile video analytics systems. We have investigated the fusion of decisions obtained during the processing of each video frame, including the use of the classifier committee based on Dempster-Shafer theory. We propose the novel age prediction method using the evaluation of the expectation of the most probable ages. We have compared existing neural-net models with a specially trained modification of the MobileNet convolution network with two outputs. The experimental results are given for such data collections as Kinect, IJB-A, Indian Movie and EmotiW. As compared with other conventional methods, our approach makes it possible to increase the age and gender recognition accuracy by 2–5% and 5–10% respectively.

Validity of Demirjian 8-teeth method for age estimation by orthopantomogram – A prospective study

Background: Demirjian et al. (1973) put forth a method of age prediction, which used seven mandibular teeth on the left side. The technique has been widely applied but revealed variations in age estimates in other populations, including Indians. The aims and objectives of our study were to validate the Demirjian 8-teeth method using orthopantomogram to estimate the reliability of age estimation after incorporating the third molar and to predict the accuracy of age estimation using Indian-specific formulae. Materials and Methods: The study was conducted on the local population with 49 subjects (mean = 18, f = 31) less than 18 years of age. The soft copies of the radiographs of selected subjects were retrieved from the computer attached to the digital orthopantomogram machine (Orthophos XG5; Sirona Dental Systems). Staging of each tooth of left mandibular side was done using Demirjian 8-teeth method. Dental age was calculated using the modified formula given by Acharya et al. and statistical methods were used to compare it with chronological age. Results: The overall mean absolute error (MAE) of the 49 subjects was 0.911 ± 1.65. The highest MAE was in the 13-year age group (1.82 years) while it was lowest in the 18-year age group (0.43 ± 1.10 years). In all, 15 subjects (30.6%) had an error in age estimation which is less than 1 year and 12 subjects (24.5%) had an error of more than 2 years The accuracy of age estimation was found to be better in 16–18 years age group as compared to younger age group, probably because of better development of the third molar by this age. Conclusion: The reasonably good results obtained in this study assert the use of Demirjian's 8-teeth method with Indian cubic functions in the local population.

DNA methylation-based age prediction from various tissues and body fluids.

Aging is a natural and gradual process in human life. It is influenced by heredity, environment, lifestyle, and disease. DNA methylation varies with age, and the ability to predict the age of donor using DNA from evidence materials at a crime scene is of considerable value in forensic investigations. Recently, many studies have reported age prediction models based on DNA methylation from various tissues and body fluids. Those models seem to be very promising because of their high prediction accuracies. In this review, the changes of age-associated DNA methylation and the age prediction models for various tissues and body fluids were examined, and then the applicability of the DNA methylation-based age prediction method to the forensic investigations was discussed. This will improve the understandings about DNA methylation markers and their potential to be used as biomarkers in the forensic field, as well as the clinical field.

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

The aims of our study were to develop new maturity scores for dental age estimation in South African black children according to the Willems method, which was developed based on Belgian Caucasian (BC) reference data (Willems et al. J Forensic Sci 46(4):893-895, 2001), and to compare age prediction performance of both methods. A total of 986 panoramic radiographs of healthy South African black (SAB) children (493 males and 493 females) in the age range of 4.14 to 14.99years (mean age 10.06years) were selected for obtaining developmental staging scores (according to Demirjian et al. Hum Biol 45(2):211-227, 1973). Willems BC methodology was applied to develop new country-specific maturity scores (Willems SAB). Age prediction performance of Willems BC and Willems SAB was compared. On average, Willems BC renders acceptable results with an overestimation of chronological age of 0.06years (SD 0.88years) in SAB children. Compared to Willems SAB, the overall mean absolute error was slightly higher with Willems BC (0.62 and 0.68years, respectively), but this was not significant in males. Also, the root mean squared error was marginally higher in Willems BC. The new age prediction method developed in South African black children was found to be better compared to Willems BC, although the difference seems to be small and clinically not relevant, especially in males.