Age Estimation Error Research Articles

FACIAL IMAGE CAPTIONING USING DNN

Facial analysis, encompassing emotion, age, and gender detection, shows potential in various applications such as human-computer interaction, business, security, and health. This study delves into the development and evaluation of a deep neural network (DNN) model for facial emotion, age, and gender detection. Utilizing a convolutional neural network (CNN) architecture trained on diverse datasets for each task, our model proves effective in predicting facial features. The accuracy of needs assessment is X%, the marginal error (MAE) of age estimation is Y years, and the accuracy of gender classification is Z%.

Artificial Intelligence Assessment of Biological Age From Transthoracic Echocardiography: Discrepancies with Chronologic Age Predict Significant Excess Mortality

Age and sex can be estimated using artificial intelligence on the basis of various sources. The aims of this study were to test whether convolutional neural networks could be trained to estimate age and predict sex using standard transthoracic echocardiography and to evaluate the prognostic implications. The algorithm was trained on 76,342 patients, validated in 22,825 patients, and tested in 20,960 patients. It was then externally validated using data from a different hospital (n=556). Finally, a prospective cohort of handheld point-of-care ultrasound devices (n=319; ClinicalTrials.gov identifier NCT05455541) was used to confirm the findings. A multivariate Cox regression model was used to investigate the association between age estimation and chronologic age with overall survival. The mean absolute error in age estimation was 4.9years, with a Pearson correlation coefficient of 0.922. The probabilistic value of sex had an overall accuracy of 96.1% and an area under the curve of 0.993. External validation and prospective study cohorts yielded consistent results. Finally, survival analysis demonstrated that age prediction ≥5years vs chronologic age was associated with an independent 34% increased risk for death during follow-up (P<.001). Applying artificial intelligence to standard transthoracic echocardiography allows the prediction of sex and the estimation of age. Machine-based estimation is an independent predictor of overall survival and, with further evaluation, can be used for risk stratification and estimation of biological age.

Xception transfer learning with early stopping for facial age estimation

The rapid development of deep learning attracts more attention to the analysis of person's face images. Deep learning methodsof facial age estimation are more effective compared to methods based on anthropometric models, models of active appearance, texture models, subspace of aging patterns. However, deep learning networks require more computing power to process images. Pre-trained models do not need a large training set and their training time is less. However, the parameters obtained as a result of transfer learning of the pre-training network significantly affect its efficiency. It is also necessary to take into account the properties of the processed images, in particular, the conditions under which they were obtained.Recently, the facial age estimation is implemented in applications in devices with limited resources of computing, for example, in smartphones. The memory size and power consumption of such applications are limited by the computing power of mobile devices. In addition, when photographing a person's face with a smartphone camera, it is very difficult to ensure the uniform lighting. The aim of the research is reducing the error of facial age estimation from uneven illuminated images by applying an early stopping of transfer learning of the Xception network. The proposed technique of transfer learning includes an early stopping of training, if the improvement of the results is not observed within a certain number of epochs. Then the network weights from the epoch with the lowest validation loss are saved. As a result of the proposed technique applying, the average absolute error of age estimation was about five years from unevenly illuminated test images. A number of parameters of the used in this case Xceptionnetwork is less than that of other deep learning neural networks which solved the age estimation problem. Then applying of the Xception network reduces the resource consumption of devices with limited computing power. Prospects for further research are reducing the unevenness of facial image lighting to decrease the error of age estimation. Also, to reduce the computing resources, it is promising to use fast transforms in the Xception convolutional layers.

Individual age determination based on computed tomography knee analysis using artificial neural networks and computer vision: Preliminary results

BACKGROUND: Currently, studies have focused on the modernization of existing methods of forensic age assessment (bone and skeletal) through the active use of modern methods of medical imaging (e.g., computed tomography) and artificial intelligence for their analysis. This approach enables the creation of new methods for assessing biological age, which is characterized by increased accuracy and reproducibility.&#x0D; AIM: To develop and test an algorithm for predicting the biological age of an individual based on computed tomography analysis of the knee joint using artificial neural networks and computer vision.&#x0D; MATERIALS AND METHODS: This observational retrospective transverse (one time) study analyzed computed tomography scans (334) of the knee joint performed in the Departments of Radiation Diagnostics of the Priorov Central Institute for Trauma and Orthopedics, Vreden National Medical Center for Traumatology and Orthopedics, between 2018 and 2021. The study enrolled persons of both sexes aged 13–45 years. Cases of developmental abnormalities, knee injuries, signs of general connective tissue pathology were excluded. Research methods include the use of intelligent information technologies (a formalized set of mathematical and software solutions).&#x0D; RESULTS: Based on the experiments conducted, an algorithm for assessing age according to the computed tomography scans of the knee joint has been developed. The main components of the developed system are as follows: a preprocessing module, an intelligent computing core, a data analysis module, a three-dimensional reconstruction module, a property extraction module, and a final age assessment module. The essence of the proposed method is the simultaneous use of artificial neural networks and clearly formalized mathematical procedures for calculating the properties of the epiphyseal line. To obtain the results and conduct primary experimental studies that confirmed the feasibility, correctness, and operability of the method, software using the YOLOv5 neural network was developed. The result of the error matrix analysis after training shows a probability of correct recognition of the order of 80%. Verification of experimental studies was performed on 46 cases. At present, the age estimation error is approximately 1 year for children and adolescents.&#x0D; CONCLUSIONS: The experimental results have confirmed the adequacy of the age estimates obtained to the actual age of the individual and, consequently, the applicability of the proposed method in forensic medical institutions. The proposed method is currently implemented as a set of software components with subsequent manual integration of automatically calculated data. The plan was to supplement the database of computed tomography images to increase the training sample and the accuracy of age prediction.

SVTNet: Automatic bone age assessment network based onTW3method and vision transformer

AbstractThis study aims to develop a proficient and clinically applicable algorithm that can accurately assess bone age. This algorithm is based on the principles of the Tanner‐Whitehouse 3 (TW3) integral approach, and aims to achieve efficiency, scalability, and interpretability. We developed a model for bone age prediction in children. The model was tested on a pediatric dataset from a tertiary care hospital consisting of left‐hand radiographs of children between the age of 0 and 18. Our model consists of removing the arm portion using a pre‐trained YOLO network, localizing 37 key points in the hand bone portion using a spatial configuration network, and segmenting the original image through 20 of these points to obtain 20 fixed‐size patches. Finally, each of the 20 bone images is classified by training a visual transformer (ViT) model. In this study, a hybrid network, SVTNet, was developed that incorporates visual transformers to obtain estimates of bone age in the carpal (C series) and metacarpal (RUS series) bones. The sum of the clinical TW3 scoring region scores and bone maturity scores were utilized to determine the bone age for each corresponding region. The performance of the algorithm was evaluated in terms of both training and testing by evaluating 3871 left hand X‐ray micrographs obtained from a tertiary hospital in China. The results showed that the average absolute error of bone age estimation was 0.50 years for the RUS series of bones and 0.47 years for the C series of bones. The main contribution of this study is to propose, for the first time, a ViT‐based bone age assessment method that automates the entire process of the TW3 algorithm and is clinically interpretable, with predictive accuracy comparable to that of an experienced orthopedic surgeon.

Inaccurate fossil placement does not compromise tip‐dated divergence times

AbstractTime‐scaled phylogenies underpin the interrogation of evolutionary processes across deep timescales, as well as attempts to link these to Earth's history. By inferring the placement of fossils and using their ages as temporal constraints, tip dating under the fossilized birth–death (FBD) process provides a coherent prior on divergence times. At the same time, it also links topological and temporal accuracy, as incorrectly placed fossil terminals should misinform divergence times. This could pose serious issues for obtaining accurate node ages, yet the interaction between topological and temporal error has not been thoroughly explored. We simulate phylogenies and associated morphological datasets using methodologies that incorporate evolution under selection, and are benchmarked against empirical datasets. We find that datasets of 300 characters and realistic levels of missing data generally succeed in inferring the correct placement of fossils on a constrained extant backbone topology, and that true node ages are usually contained within Bayesian posterior distributions. While increased fossil sampling improves the accuracy of inferred ages, topological and temporal errors do not seem to be linked: analyses in which fossils resolve less accurately do not exhibit elevated errors in node age estimates. At the same time, inferred divergence times are biased, probably due to a mismatch between the FBD prior and the shape of our simulated trees. While these results are encouraging, suggesting that even fossils with uncertain affinities can provide useful temporal information, they also emphasize that palaeontological information cannot overturn discrepancies between model priors and the true diversification history.

Comparison of Transcerebellar Diameter with Biparietal Diameter on Ultrasound for Gestational Age Measurement in Third Trimester of Pregnancy Using First Day of Last Menstrual Period for Actual Period of Gestation

Objective: to evaluate how well biparietal diameter and transcerebellar diameter performed in estimating the gestational age of pregnant women in their third trimester. Study Design: Cross-sectional study. Place and Duration of Study: Armed Forces Institute of radiology and Imaging, Rawalpindi Pakistan, Feb to Aug 2022. Methodology: There were 120 pregnant women who went to the Obstetrics and Gynecology Departmental walk-in clinic or Emergency room between weeks 28 and 40 of their pregnancies. Ultrasound was done on all patients that had been preselected after a complete history and physical examination had been completed; transcerebellar diameter (TCD) and biparietal diameter (BPD) were measured and compared with LMP. Results: Mean age of the participants recruited in the study was 27.08±1.45 years. There was a significant increase in mean BPD from 28 weeks of gestation (68.01±7.4 mm) to 36 weeks of gestation (89.77±2.35 mm). During 28 weeks of gestation, the mean TCD was 30.3±1.49 mm, whereas at 36 weeks it peaked at 48.1±1.21 mm. By looking at the median gap between real and estimated GA by BPD, we find that when real GA rises, the magnitude of the age estimation error decreases significantly. The error was 3.22±0.17 days for GA at 28 weeks, 2.48±0.09 days at 34 weeks, and 2.18±0.01 days at 36 weeks. A statistically significant (p&lt;0.001) shift in the mean error was observed. .......... Conclusion: Both TCD and BPD were shown to be helpful in this study's context, however statistically speaking, TCD was superior to BPD. ...........

Errors in age estimates of North Atlantic minke whales when counting growth zones in bulla tympanica

Age estimation of common minke whales (Balaenoptera acutorostrata) has always been difficult, and the accuracy of the current method of counting growth layer groups (GLGs) in the periosteal zone of the tympanic bulla has been questioned. To evaluate this method, two readers aged three sections from each of right and left bulla tympanica from 35 male and 57 female North Atlantic minke whales. A single age estimate with variance was calculated for each whale using General Linear Mixed Model Poisson based regression, and this estimate was compared with the number of ovulations and body length to evaluate the bias of the age determination method. The results showed a poor fit between age and number of ovulations with R2 = 0.0014. Bias was estimated to be a 37% underestimate of ‘true’ age assuming an ovulation rate of one per year and age at sexual maturity of eight years. Precision of the bulla age estimates was lower than those of Antarctic minke whales aged using the earplug method. The high bias reduces the applicability of the bulla method in routine age-determination with a management objective. Other age determination methods for the species should be improved or developed to ensure proper monitoring of demography and life history for the North Atlantic minke whale.

High performance for bone age estimation with an artificial intelligence solution

PurposeThe purpose of this study was to compare the performance of an artificial intelligence (AI) solution to that of a senior general radiologist for bone age assessment. Material and methodsAnteroposterior hand radiographs of eight boys and eight girls from each age interval between five and 17 year-old from four different radiology departments were retrospectively collected. Two board-certified pediatric radiologists with knowledge of the sex and chronological age of the patients independently estimated the Greulich and Pyle bone age to determine the standard of reference. A senior general radiologist not specialized in pediatric radiology (further referred to as “the reader”) then determined the bone age with knowledge of the sex and chronological age. The results of the reader were then compared to those of the AI solution using mean absolute error (MAE) in age estimation. ResultsThe study dataset included a total of 206 patients (102 boys of mean chronological age of 10.9 ± 3.7 [SD] years, 104 girls of mean chronological age of 11 ± 3.7 [SD] years). For both sexes, the AI algorithm showed a significantly lower MAE than the reader (P < 0.007). In boys, the MAE was 0.488 years (95% confidence interval [CI]: 0.28–0.44; r2 = 0.978) for the AI algorithm and 0.771 years (95% CI: 0.64–0.90; r2 = 0.94) for the reader. In girls, the MAE was 0.494 years (95% CI: 0.41–0.56; r2 = 0.973) for the AI algorithm and 0.673 years (95% CI: 0.54–0.81; r2 = 0.934) for the reader. ConclusionThe AI solution better estimates the Greulich and Pyle bone age than a general radiologist does.

LCA-GAN: Low-Complexity Attention-Generative Adversarial Network for Age Estimation with Mask-Occluded Facial Images

Facial-image-based age estimation is being increasingly used in various fields. Examples include statistical marketing analysis based on age-specific product preferences, medical applications such as beauty products and telemedicine, and age-based suspect tracking in intelligent surveillance camera systems. Masks are increasingly worn for hygiene, personal privacy concerns, and fashion. In particular, the acquisition of mask-occluded facial images has become more frequent due to the COVID-19 pandemic. These images cause a loss of important features and information for age estimation, which reduces the accuracy of age estimation. Existing de-occlusion studies have investigated masquerade masks that do not completely occlude the eyes, nose, and mouth; however, no studies have investigated the de-occlusion of masks that completely occlude the nose and mouth and its use for age estimation, which is the goal of this study. Accordingly, this study proposes a novel low-complexity attention-generative adversarial network (LCA-GAN) for facial age estimation that combines an attention architecture and conditional generative adversarial network (conditional GAN) to de-occlude mask-occluded human facial images. The open databases MORPH and PAL were used to conduct experiments. According to the results, the mean absolution error (MAE) of age estimation with the de-occluded facial images reconstructed using the proposed LCA-GAN is 6.64 and 6.12 years, respectively. Thus, the proposed method yielded higher age estimation accuracy than when using occluded images or images reconstructed using the state-of-the-art method.

Evaluation of the suitability of the Greulich and Pyle atlas in estimating age for the Botswana population using hand and wrist radiographs of young Botswana population

The applicability of Greulich and Pyle atlas for estimating age for court cases that require age estimation amongst the Botswana population is often questioned because the atlas has been adopted without being evaluated for Botswana. It has long been established that accuracies of such age estimation methods are dependent on different factors such as the general health and nutrition of the population. We present a study aimed at determining the level of accuracy of the Greulich and Pyle atlas at estimating age for the Botswana population. It is the first time that this type of study is being performed for Botswana. The results will be used to confirm if there is need to develop specific age estimation standards for Botswana. A total of 140 x-ray image samples were obtained from local hospitals for patients aged between 5 and 18 years. The sample images were compared with the Greulich and Pyle age estimation standards. In forensic age estimations, there are several important age limits which are considered. The legal age of maturity for Botswana is 18 years. This age limit is very important in determining whether offenders should be tried as children or adults and at the same time, in determining if victims are minors or adults. It also forms the basis for sentencing of offenders for crimes committed against children. For the important age group 15 ≤ 18 the mean skeletal age difference was found to be 0.94 (statistically significant) indicating that age was underestimated for males. This could be a problem where a perpetrator’s age is underestimated as a result not sentenced accordingly as they are mistaken for a minor. The mean skeletal age difference for females was found to be 0.64 years, indicating that age was underestimated by up to 0.64 years for females while the mean skeletal age difference was found to be 0.50 years for males in overall. This indicated that the Greulich and Pyle atlas underestimated age by 0.50 years overall. Significant statistical difference was observed between the mean chronological age and the mean estimated age as the p-values were found to be 0.03 for females and 0.05 for males. Based on these findings, it can be concluded that the Greulich and Pyle atlas can be used to estimate age but with caution as there is a possibility of errors. There is need for this atlas to be revised to reduce errors in age estimation for the Botswana context.

Semi-supervised automatic dental age and sex estimation using a hybrid transformer model.

Teeth-based age and sex estimation is an important task in mass disasters, criminal scenes, and archeology. Although various methods have been proposed, most of them are subjective and influenced by observers' experiences. In this study, we aimed to develop a deep learning model for automatic dental age and sex estimation from orthopantomograms (OPGs) and compare to manual methods. A large dataset of 15,195 OPGs (age range, 16 ~ 50years; mean age, 29.65years ± 9.36 [SD]; 10,218 females) was used to train and test a hybrid deep learning model which is a combination of convolutional neural network and transformer model. The final performance of this model was evaluated on additional independent 100 OPGs and compared to the manual method for external validation. In the test of 1413 OPGs, the mean absolute error (MAE) of age estimation was 2.61years by this model. The accuracy and the area under the receiver operating characteristic curve (AUC) of sex estimation were 95.54% and 0.984. The heatmap indicated that the crown and pulp chamber of premolars and molars contain the most age-related information. In the additional independent 100 OPGs, this model achieved an MAE of 3.28years for males and 3.79years for females. The accuracy of this model was much higher than that of the manual models. Therefore, this model has the potential to assist radiologists in automated age and sex estimation.

Изменение экспрессии мРНК панели генов в слизистой оболочке толстой кишки в процессе естественного старения

Background: during natural aging, cellular and molecular changes in the colonic mucosa contribute to chronic damage and the development of various neoplasia, including colorectal cancer (CRC). Aim: to assess the age peculiarities of mRNA expression in samples of normal mucosa from various parts of the colon. Patients and Methods: the study included 95 patients. Normal colonic mucosa biopsies were derived during videocolonoscopy from different parts of the colon, i.e., the right colon (n=91), left colon (n=95), and rectum (n=87). In total, 273 samples were examined. The relative level of mRNA expression of 63 genes potentially involved in various pathways of CRC carcino-genesis was evaluated by reverse transcription polymerase chain reaction. Results: a linear regression model for predicting passport age, including data on gene ex-pression, was characterized by moderate reliability (R2 0.45, DW test 1.54). Age estimation er-rors in the model arise proportionally to its increase, which is associated with the accumulation of epigenetic changes. Maximum accuracy in age prediction is typical for rectal mucosa samples. The first statistically significant division (primary classification node) of patients by age in indi-viduals over or under 60 years provides the level of TERT gene expression. Telomerase activity in association with HLA-G5 gene overexpression is typical in older patients, whereas HLA-G5 gene hypoexpression and simultaneous granulysin (GNLY) overexpression are typical in patients younger than 60 years. A molecular cascade involving a decrease in telomerase activity due to TERT hypoexpression with simultaneous overexpression of the cell cycle regulator and tumor suppressor P14ARF is also typical for normal colonic mucosa in patients aged 60 years and over. Conclusion: our findings support molecular genetic changes in the colonic mucosa that occur with age. The specifics of gene expression involved in CRC carcinogenesis are the basis for assessing CRC risk. KEYWORDS: gene expression, mRNA, RT-PCR, normal colon mucosa, age, epigenetic changes, colorectal cancer. FOR CITATION: Goncharov S.V., Bozhenko V.K., Zakharenko M.V., Chaptykov A.A., Kulinich T.M., Solodkiy V.A. Changes in mRNA expression of colon mucosa genes during natural aging. Russian Medical Inquiry. 2023;7(6):347–352 (in Russ.). DOI: 10.32364/2587-6821-2023-7-6-2.

Age estimation through facial images using Deep CNN Pretrained Model and Particle Swarm Optimization

There has been a lot of recent study on age estimates utilizing different optimization techniques, architecture models, and diverse strategies with some variations. However, accuracy improvement in age estimation studies remains a challenge due to the inability of traditional approaches to effectively capture complex facial features and variations. Therefore, this study investigates the usage of Particle Swarm Optimization in Deep CNN models to improve accuracy. The focus of the study is on exploring different feature extractors for the age estimation task, utilizing pre-trained CNN models such as VGG16, VGG19, ResNet50, and Xception. The proposed approach utilizes PSO to optimize the hyperparameters of a custom output layer for age detection in regression. The PSO algorithm searches for the optimal combination of model hyperparameters that minimize the age estimation error. This study shows that fine-tuning a model can lead to improvements in its performance, with the VGG19 model achieving the best performance after fine-tuning. Additionally, the PSO process was able to find sets of hyperparameters that were on par or even better than the initial hyperparameters. The best result can be seen in VGG19 architecture with loss of 86.181, MAE of 6.693, and MAPE of 38.462. Out of the twelve experiments conducted, it was observed that the utilization of Particle Swarm Optimization (PSO) offered distinct advantages in terms of achieving better results for age estimation. However, it is important to note that the execution time for these experiments was considerably longer when employing PSO.

Boosting algorithm improves the accuracy of juvenile forensic dental age estimation in southern China population

Age estimation based on the mineralized morphology of teeth is one of the important elements of forensic anthropology. To explore the most suitable age estimation protocol for adolescents in the South China population, 1477 panoramic radiograph images of people aged 2–18 years in the South were collected and staged by the Demirjian mineralization staging method. The dental ages were estimated using the parameters of the Demirjian and Willems. Mathematical optimization and machine learning optimization were also performed in the data processing process in an attempt to obtain a more accurate model. The results show that the Willems method was more accurate in the dental age estimation of the southern China population and the model can be further optimized by reassigning the model through a nonintercept regression method. The machine learning model presented excellent results in terms of the efficacy comparison results with the traditional mathematical model, and the machine learning model under the boosting framework, such as gradient boosting decision tree (GBDT), significantly reduced the error in dental age estimation compared to the traditional mathematical method. This machine learning processing method based on traditional estimation data can effectively reduce the error of dental age estimation while saving arithmetic power. This study demonstrates the effectiveness of the GBDT algorithm in optimizing forensic age estimation models and provides a reference for other regions to use this parameter for age estimation model establishment, and the lightweight nature of machine learning offers the possibility of widespread forensic anthropological age estimation.

Tooth Cementum Thickness as a Method of Age Estimation in the Forensic Context

Simple SummaryIn a forensic context, it may be necessary to estimate the age at death of an individual from his or her skeletal remains. The age at death constitutes an essential identification parameter, along with sex, ancestry, stature, and a few other biological characteristics that can help to trace the biological profile of the deceased. Although there is an extensive number of works in the literature on this topic, traditional anthropological methods encounter considerable difficulty in estimating the age of an adult based on skeletal remains. This difficulty increases with aging mainly due to the overlapping of pathological processes. In this study, we tested the reliability of a relatively simple histological methodology to estimate an individual′s age based on tooth roots cementum and developed new equations from individuals with known age and sex. In conclusion, our results confirm the importance of teeth in the estimation of age, suggesting that the proposed method may be effective, especially for younger individuals.Estimating age at death is a key element in the process of human identification of skeletal remains. The interest in dental cementum stems from its increase in thickness throughout life and, at the same time, from the fact it should not be affected by remodeling processes. Since the age assessment is particularly difficult in adults when using traditional anthropological methods on the skeleton, we tested a dental method based on maximum cementum thickness and developed new regression equations. We microscopically analyzed the histological sections of dental roots from a sample of 108 permanent teeth with known age and sex. Age at the time of dental extraction was in the range of 18–84 years. Our findings show that there were no differences in thickness between sexes, dental arch, and mono- and pluriradicular teeth. Separate regression equations were developed for individuals in the whole age range and individuals under 45 years. The equations were then tested on a hold-out sample from the same Mediterranean population demonstrating higher reliability for the equation developed for those under 45. Conversely, due to the increased error in age estimation in individuals over 45, this method should be used with caution in the forensic context when skeletal remains presumably belong to elderly individuals.

Estimation of calendar age based on autonomic cardiovascular function by applying machine learning techniques

Abstract Aging is accompanied by changes in the cardiovascular physiology that promote the development of age-related diseases. This paper presents a modern approach to quantify the physiological effects of age on the cardiovascular system by applying modern machine learning techniques to several indicators of autonomic cardiovascular function. In 885 healthy subjects, 33 different indices were calculated on resting state electrocardiogram and continuous blood pressure recordings. Based on those parameters, five different approaches were applied in order to reconstruct the calendar age of healthy subjects, i.e., linear regression (LR), neural network (NN), Gaussian process regression (GPR), support vector regression (SVR), and relevance vector regression (RVR). Hyper parameters of machine learning methods were optimized via grid search. After 20 repetitions of a five-fold cross-validation, the mean absolute error (MAE) was computed between the calendar and estimated age to assess the accuracy of each method. The results show that the lowest error for age estimation was achieved using SVR with a MAE of 5.49 years. GPR performed comparably well to SVR with a MAE of 5.55 years, while NN led to a MAE of 5.72 years. RVR and LR revealed MAE of more than six years (6.21 and 6.34 years). The error of age estimation could be further reduced by applying outlier correction to the input data leading to a minimum MAE of 4.53 years with GPR. In conclusion, our results suggest that machine learning can be used to quantify the effects of healthy aging on cardio-vascular function.

Analysis of maturation features in fetal brain ultrasound via artificial intelligence for the estimation of gestational age

Optimal prenatal care relies on accurate gestational age dating. After the first trimester, the accuracy of current gestational age estimation methods diminishes with increasing gestational age. Considering that, in many countries, access to first trimester crown rump length is still difficult owing to late booking, infrequent access to prenatal care, and unavailability of early ultrasound examination, the development of accurate methods for gestational age estimation in the second and third trimester of pregnancy remains an unsolved challenge in fetal medicine. This study aimed to evaluate the performance of an artificial intelligence method based on automated analysis of fetal brain morphology on standard cranial ultrasound sections to estimate the gestational age in second and third trimester fetuses compared with the current formulas using standard fetal biometry. Standard transthalamic axial plane images from a total of 1394 patients undergoing routine fetal ultrasound were used to develop an artificial intelligence method to automatically estimate gestational age from the analysis of fetal brain information. We compared its performance-as stand alone or in combination with fetal biometric parameters-against 4 currently used fetal biometry formulas on a series of 3065 scans from 1992 patients undergoing second (n=1761) or third trimester (n=1298) routine ultrasound, with known gestational age estimated from crown rump length in the first trimester. Overall, 95% confidence interval of the error in gestational age estimation was 14.2 days for the artificial intelligence method alone and 11.0 when used in combination with fetal biometric parameters, compared with 12.9 days of the best method using standard biometrics alone. In the third trimester, the lower 95% confidence interval errors were 14.3 days for artificial intelligence in combination with biometric parameters and 17 days for fetal biometrics, whereas in the second trimester, the 95% confidence interval error was 6.7 and 7, respectively. The performance differences were even larger in the small-for-gestational-age fetuses group (14.8 and 18.5, respectively). An automated artificial intelligence method using standard sonographic fetal planes yielded similar or lower error in gestational age estimation compared with fetal biometric parameters, especially in the third trimester. These results support further research to improve the performance of these methods in larger studies.

Validity of Willems age estimation method in children’s & adolescents’ of Dakshina Kannada Region, India

Willems gender-specific and non-specific maturity scores derive the age of the individual with acceptable error margins in the Belgian Population. This needs to be validated in various subpopulations to enable its use in age estimation. Our aim was to test the validity of both Willems’s I & II maturity scores to estimate dental age in children and adolescents (aged 5–20 yrs) in Dakshina Kannada population in India. Seven teeth of mandibular left quadrant from 800 Orthopantomograms (Male:Female::372:428) of individuals aged 5–20 years were staged using Demirjian’s method and substituted by the Willems maturity scores to derive the age. The chronological and the predicted age of the individuals were correlated to derive the mean absolute error of age estimation in male, female and total. The mean absolute error of estimation of age in males, females, and total of Willems I and II techniques were 1.11, 1.04, 1.07, 1.15, 1.04 and 1.09 years respectively. The gender nonspecific scores predominantly underestimated the age. The Willems I technique showed an underestimation of age in females and over-estimation of age in males (p < 0.001). The present study validates Willems method of age estimation in Dakshina Kannada population, showing a low error rate of ∼1 year.

Quantitative analysis of DNA methylation by pyrosequencing and MassARRAY technique for age estimation: a comparative study

To study the difference in age estimation based on quantitative analysis of DNA methylation by MassARRAY and pyrosequencing techniques. The methylation levels of 9 CpG sites from two independent whole blood sample sets (containing 65 and 62 samples) were detected using MassARRAY and pyrosequencing techniques. Z-score transformation was used to remove the batch effects of different techniques, and a linear regression model was used for age prediction. For age prediction using the MassARRAY system, the 65 samples showed a mean absolute difference (MAD) of 2.49 years before Z-score transformation of the data and 2.44 years after the transformation, similar to the results in the 62 samples (MAD of 3.36 years before and 3.42 years after Z-score transformation). For data typed from pyrosequencing, the 65 samples showed a MAD of 4.20 years before and 2.76 years after data Z-score transformation, also similar to the results in the 62 samples (MAD of 3.92 years before and 3.63 years after data transformation). Z-score transformation can effectively reduce the system batch effect between MassARRAY and pyrosequencing. Data from the MassARRAY system allows direct age estimation without further data processing, while the pyrosequencing data may increase the error in age estimation, which can be corrected by Z-score transformation of the data.