Telomere length is not a useful tool for chronological age estimation in animals.

Telomeres are short repetitive DNA sequences capping the ends of chromosomes. Telomere shortening occurs during cell division and may be accelerated by oxidative damage or ameliorated by telomere maintenance mechanisms. Consequently, telomere length changes with age, which was recently confirmed in a large meta-analysis across vertebrates. However, based on the correlation between telomere length and age, it was concluded that telomere length can be used as a tool for chronological age estimation in animals. Correlation should not be confused with predictability, and the current data and studies suggest that telomeres cannot be used to reliably predict individual chronological age. There are biological reasons for why there is large individual variation in telomere dynamics, which is mainly due to high susceptibility to a wide range of environmental, but also genetic factors, rendering telomeres unfeasible as a tool for age estimation. The use of telomeres for chronological age estimation is largely a misguided effort, but its occasional reappearance in the literature raises concerns that it will mislead resources in wildlife conservation.

Microanatomy of incremental growth lines in dental tissues in reindeer Rangifer tarandus.

Counting growth layers in dentine and/or secondary cementum is widely used for age determination in wild mammals but the underlying seasonal changes in the structure and degree of mineralisation of dental tissue have not been well characterised. We embedded first (m1) and second (m2) mandibular permanent molar teeth from a 12-year-old female Svalbard reindeer (Rangifer tarandus platyrhynchus) in PolyMethylMethAcrylate (PMMA), prepared cut and polished surfaces coated with evaporated carbon and used 20 kV back-scattered electron imaging in a scanning electron microscope (BSE-SEM) to study aspects of dental tissue structure which depend on the degree of mineralisation at the micron and sub-micron scale. BSE-SEM revealed differences between the mineral content of growth layers (annulations) in the secondary cementum and the primary and secondary dentine, the latter, incidentally, still forming at death in m1. Wide bands of less well mineralised tissue formed in the cementum during active appositional phases. Thin, denser bands formed by maturation-mineralisation of existing tissue when growth slowed in winter. This maturation mimics the processes seen in lamellar bone and articular cartilage. Counter to previous suggestions, there was evidence of substantial resorption and repair of the secondary cementum and of formation of dentine throughout life. Secondary dentine is layered by mineral content like cementum. In the crown, this was mainly tubular dentine with well-marked interglobular dentine layers. In the lower pulp chamber and root, it was largely without tubules. Substantial non-mineralised spaces found at the cement-dentine junction in the root apical regions in m2 represent inclusions of the Hertwig's Epithelial Root Sheath (HERS) or the Epithelial Rests of Malassez (ERM) between the two tissues, a phenomenon which has previously only been identified in Muridae. The anatomical changes which result in the formation of the incremental lines (annulations) in dental tissues of reindeer, identified here for the first time at the micrometre level, are likely to be common across most if not all long-lived species of mammals living in seasonal environments.

Olecranon bone age assessment in puberty using a lateral elbow radiograph and a deep-learning model.

OBJECTIVES: To improve pubertal bone age (BA) evaluation by developing a precise and practical elbow BA classification using the olecranon, and a deep-learning AI model. MATERIALS AND METHODS: Lateral elbow radiographs taken for BA evaluation in children under 18 years were collected from January 2020 to June 2022, retrospectively. A novel classification and the olecranon BA were established based on the morphological changes in the olecranon ossification process during puberty. The olecranon BA was compared with other elbow and hand BA methods, using intraclass correlation coefficients (ICCs), and a deep-learning AI model was developed. RESULTS: A total of 3508 lateral elbow radiographs (mean age 9.8 ± 1.8 years) were collected. The olecranon BA showed the highest applicability (100%) and interobserver agreement (ICC 0.993) among elbow BA methods. It showed excellent reliability with Sauvegrain (0.967 in girls, 0.969 in boys) and Dimeglio (0.978 in girls, 0.978 in boys) elbow BA methods, as well as Korean standard (KS) hand BA in boys (0.917), and good reliability with KS in girls (0.896) and Greulich-Pyle (GP)/Tanner-Whitehouse (TW)3 (0.835 in girls, 0.895 in boys) hand BA methods. The AI model for olecranon BA showed an accuracy of 0.96 and a specificity of 0.98 with EfficientDet-b4. External validation showed an accuracy of 0.86 and a specificity of 0.91. CONCLUSION: The olecranon BA evaluation for puberty, requiring only a lateral elbow radiograph, showed the highest applicability and interobserver agreement, and excellent reliability with other BA evaluation methods, along with a high performance of the AI model. CLINICAL RELEVANCE STATEMENT: This AI model uses a single lateral elbow radiograph to determine bone age for puberty from the olecranon ossification center and can improve pubertal bone age assessment with the highest applicability and excellent reliability compared to previous methods. KEY POINTS: Elbow bone age is valuable for pubertal bone age assessment, but conventional methods have limitations. Olecranon bone age and its AI model showed high performances for pubertal bone age assessment. Olecranon bone age system is practical and accurate while requiring only a single lateral elbow radiograph.

Immunohistochemical analysis of PDGFR-α for wound age determination.

Immunohistochemical analysis of platelet-derived growth factor receptor-α (PDGFR-α) was performed on human skin wounds obtained from forensic autopsy cases. Thirty human skin wounds were collected at different post-infliction intervals as follows: Group I, 4 h to 3 days (n = 16); Group II, 4 to 7 days (n = 7); Group III, 9 to 10 days (n = 3); and Group IV, 14 to 20 days (n = 4). Immunopositive reactions for PDGFR-α were not observed in the uninjured human skin specimens. In a semi-quantitative morphometrical analysis, the number of PDGFR-α-positive cells was observed increased in Group II, with the average number of PDGFR-α-positive cells being the highest in Group II. Additionally, in Group II, all specimens showed PDGFR-α-positive cells, with an average number of > 200 cells in five fields of view, suggesting a wound age of 4 to 7 days. Taken together, the immunohistochemical detection of PDGFR-α in human skin wounds can be a useful tool for wound age determination.

MRI segmentation of tooth tissue in age prediction of sub-adults - a new method for combining data from the 1st, 2nd, and 3rd molars.

PURPOSE: We aimed to establish a model combining MRI volume measurements from the 1st, 2nd and 3rd molars for age prediction in sub-adults and compare the age prediction performance of different combinations of all three molars, internally in the study cohort. MATERIAL AND METHOD: We examined 99 volunteers using a 1.5 T MR scanner with a customized high-resolution single T2 sequence. Segmentation was performed using SliceOmatic (Tomovision©). Age prediction was based on the tooth tissue ratio (high signal soft tissue + low signal soft tissue)/total. The model included three correlation parameters to account for statistical dependence between the molars. Age prediction performance of different combinations of teeth for the three molars was assessed using interquartile range (IQR). RESULTS: We included data from the 1st molars from 87 participants (F/M 59/28), 2nd molars from 93 (F/M 60/33) and 3rd molars from 67 (F/M 45/22). The age range was 14-24 years with a median age of 18 years. The model with the best age prediction performance (smallest IQR) was 46-47-18 (lower right 1st and 2nd and upper right 3rd molar) in males. The estimated correlation between the different molars was 0.620 (46 vs. 47), 0.430 (46 vs. 18), and 0.598 (47 vs. 18). IQR was the smallest in tooth combinations including a 3rd molar. CONCLUSION: We have established a model for combining tissue volume measurements from the 1st, 2nd and 3rd molars for age prediction in sub-adults. The prediction performance was mostly driven by the 3rd molars. All combinations involving the 3rd molar performed well.

Age estimation in adults by canine teeth: a systematic review of the Cameriere method with meta-analysis on the reliability of the pulp/tooth area ratio.

The age estimation of an adult using methods accessible to the forensic routine is a goal pursued by forensic experts. Cameriere, Ferrante and Cingolani (2004) proposed the use of the pulp/tooth area ratio of canine teeth as a promising variable, but its reliability has shown conflicting results in the scientific literature. This article aimed to carry out a systematic review with meta-analysis to verify whether the pulp/tooth area ratio of canine teeth includes a variable that can be used alone to estimate dental age in adults. A systematic search was carried out in six databases using keywords related to the theme in Portuguese, English, and Spanish. The study selection process followed pre-established eligibility criteria. Assessments were carried out regarding risk of bias and publication bias of selected studies, and meta-analysis was carried out considering Pearson's correlation coefficient between pulp/tooth area ratio and chronological age as effect measure. Most selected studies showed low risk of bias; no publication bias was found when all studies were considered, and potential publication bias was found when outliers were removed. Despite the high heterogeneity among studies and the need for more research, it could be observed that the pulp/tooth area ratio has strong negative correlation with chronological age, and the pulp/tooth area ratio could be derived from both periapical radiographs and orthopantomographs. Therefore, it is suggested that there is scientific evidence that the pulp/tooth area ratio obtained from canine teeth is reliable for dental age estimation in adults.

Machine learning and deep learning enabled age estimation on medial clavicle CT images.

The medial clavicle epiphysis is a crucial indicator for bone age estimation (BAE) after hand maturation. This study aimed to develop machine learning (ML) and deep learning (DL) models for BAE based on medial clavicle CT images and evaluate the performance on normal and variant clavicles. This study retrospectively collected 1049 patients (mean± SD: 22.50±4.34 years) and split them into normal training and test sets, and variant training and test sets. An additional 53 variant clavicles were incorporated into the variant test set. The development stages of normal MCE were used to build a linear model and support vector machine (SVM) for BAE. The CT slices of MCE were automatically segmented and used to train DL models for automated BAE. Comparisons were performed by linear versus ML versus DL, and normal versus variant clavicles. Mean absolute error (MAE) and classification accuracy was the primary parameter of comparison. For BAE, the SVM had the best MAE of 1.73 years, followed by the commonly-used CNNs (1.77-1.93 years), the linear model (1.94 years), and the hybrid neural network CoAt Net (2.01 years). In DL models, SE Net 18 was the best-performing DL model with similar results to SVM in the normal test set and achieved an MAE of 2.08 years in the external variant test. For age classification, all the models exhibit superior performance in the classification of 18-, 20-, 21-, and 22-year thresholds with limited value in the 16-year threshold. Both ML and DL models produce desirable performance in BAE based on medial clavicle CT.

Radiological age assessment based on clavicle ossification in CT: enhanced accuracy through deep learning.

BACKGROUND: Radiological age assessment using reference studies is inherently limited in accuracy due to a finite number of assignable skeletal maturation stages. To overcome this limitation, we present a deep learning approach for continuous age assessment based on clavicle ossification in computed tomography (CT). METHODS: Thoracic CT scans were retrospectively collected from the picture archiving and communication system. Individuals aged 15.0 to 30.0 years examined in routine clinical practice were included. All scans were automatically cropped around the medial clavicular epiphyseal cartilages. A deep learning model was trained to predict a person's chronological age based on these scans. Performance was evaluated using mean absolute error (MAE). Model performance was compared to an optimistic human reader performance estimate for an established reference study method. RESULTS: The deep learning model was trained on 4,400 scans of 1,935 patients (training set: mean age = 24.2 years ± 4.0, 1132 female) and evaluated on 300 scans of 300 patients with a balanced age and sex distribution (test set: mean age = 22.5 years ± 4.4, 150 female). Model MAE was 1.65 years, and the highest absolute error was 6.40 years for females and 7.32 years for males. However, performance could be attributed to norm-variants or pathologic disorders. Human reader estimate MAE was 1.84 years and the highest absolute error was 3.40 years for females and 3.78 years for males. CONCLUSIONS: We present a deep learning approach for continuous age predictions using CT volumes highlighting the medial clavicular epiphyseal cartilage with performance comparable to the human reader estimate.

Bone age assessment based on different MRI modalities of the proximal humerus epiphysis: the comparisons of T1WI, T2WI, and PDWI.

Bone age assessment (BAA) is crucial in various fields, including legal proceedings, athletic competitions, and clinical medicine. However, the use of X-ray methods for age estimation without medical indication is subject to ethical debate, especially in forensic and athletic fields. The application of magnetic resonance imaging (MRI) with non-ionizing radiation can overcome this limitation in BAA. This study aimed to compare the application value of several MRI modalities of proximal humeral in BAA. A total of 468 patients with shoulder MRIs were retrospectively collected from a Chinese Han population aged 12-30 years (259 males and 209 females) for training and testing, including T1 weighted MRI (T1WI), T2 weighted MRI (T2WI), and Proton density weighted MRI (PDWI). Optimal regression models were established for age estimation, yielding mean absolute error (MAE) values below 2.0 years. The MAE values of T1WI were the lowest, with 1.700 years in males and 1.798 years in females. The area under the curve (AUC) and accuracy values of different MRI modalities of 16-year and 18-year thresholds were all around 0.9. For the 18-year threshold, T1WI outperformed T2WI and PDWI. In conclusion, the three MRI modalities of the proximal humerus can serve as reliable indicators for age assessment, while the T1WI performed better in age assessment and classification.

Efficacy of the methods of age determination using artificial intelligence in panoramic radiographs - a systematic review.

The aim of this systematic review is to analyze the literature to determine whether the methods of artificial intelligence are effective in determining age in panoramic radiographs. Searches without language and year limits were conducted in PubMed/Medline, Embase, Web of Science, and Scopus databases. Hand searches were also performed, and unpublished manuscripts were searched in specialized journals. Thirty-six articles were included in the analysis. Significant differences in terms of root mean square error and mean absolute error were found between manual methods and artificial intelligence techniques, favoring the use of artificial intelligence (p < 0.00001). Few articles compared deep learning methods with machine learning models or manual models. Although there are advantages of machine learning in data processing and deep learning in data collection and analysis, non-comparable data was a limitation of this study. More information is needed on the comparison of these techniques, with particular emphasis on time as a variable.

Automated bone age assessment from knee joint by integrating deep learning and MRI-based radiomics.

Bone age assessment (BAA) is a crucial task in clinical, forensic, and athletic fields. Since traditional age estimation methods are suffered from potential radiation damage, this study aimed to develop and evaluate a deep learning radiomics method based on multiparametric knee MRI for noninvasive and automatic BAA. This retrospective study enrolled 598 patients (age range,10.00-29.99 years) who underwent MR examinations of the knee joint (T1/T2*/PD-weighted imaging). Three-dimensional convolutional neural networks (3D CNNs) were trained to extract and fuse multimodal and multiscale MRI radiomic features for age estimation and compared to traditional machine learning models based on hand-crafted features. The age estimation error was greater in individuals aged 25-30 years; thus, this method may not be suitable for individuals over 25 years old. In the test set aged 10-25 years (n = 95), the 3D CNN (a fusion of T1WI, T2*WI, and PDWI) demonstrated the lowest mean absolute error of 1.32 ± 1.01 years, which is higher than that of other MRI modalities and the hand-crafted models. In the classification for 12-, 14-, 16-, and 18- year thresholds, accuracies and the areas under the ROC curves were all over 0.91 and 0.96, which is similar to the manual methods. Visualization of important features showed that 3D CNN estimated age by focusing on the epiphyseal plates. The deep learning radiomics method enables non-invasive and automated BAA from multimodal knee MR images. The use of 3D CNN and MRI-based radiomics has the potential to assist radiologists or medicolegists in age estimation.

Adult age estimation from the sternum using maximum intensity projection images of CT and data mining in a Chinese population.

This study aimed to explore and develop data mining models for adult age estimation based on CT reconstruction images from the sternum. Maximum intensity projection (MIP) images of chest CT were retrospectively collected from a modern Chinese population, and data from 2700 patients (1349 males and 1351 females) aged 20 to 70 years were obtained. A staging technique within four indicators was applied. Several data mining models were established, and mean absolute error (MAE) was the primary comparison parameter. The intraobserver and interobserver agreement levels were good. Within internal validation, the optimal data mining model obtained the lowest MAE of 9.08 in males and 10.41 in females. For the external validation (N = 200), MAEs were 7.09 in males and 7.15 in females. In conclusion, the accuracy of our model for adult age estimation was among similar studies. MIP images of the sternum could be a potential age indicator. However, it should be combined with other indicators since the accuracy level is still unsatisfactory.

Predicting chronological age of 14 or 18 in adolescents: integrating dental assessments with machine learning.

AIM: Age estimation plays a critical role in personal identification, especially when determining compliance with the age of consent for adolescents. The age of consent refers to the minimum age at which an individual is legally considered capable of providing informed consent for sexual activities. The purpose of this study is to determine whether adolescents meet the age of 14 or 18 by using dental development combined with machine learning. METHODS: This study combines dental assessment and machine learning techniques to predict whether adolescents have reached the consent age of 14 or 18. Factors such as the staging of the third molar, the third molar index, and the visibility of the periodontal ligament of the second molar are evaluated. RESULTS: Differences in performance metrics indicate that the posterior probabilities achieved by machine learning exceed 93% for the age of 14 and slightly lower for the age of 18. CONCLUSION: This study provides valuable insights for forensic identification for adolescents in personal identification, emphasizing the potential to improve the accuracy of age determination within this population by combining traditional methods with machine learning. It underscores the importance of protecting and respecting the dignity of all individuals involved.

Association between dental age and malocclusions: a systematic review.

BACKGROUND: The evidence in the literature suggests that some skeletal or dental malocclusions are involved with dental development, resulting in advanced or delayed dental age (DA). The purpose of this systematic review was to investigate the association between DA and different types of malocclusions. METHODS: The search was carried out on PubMed, Scopus, Web of Science, Virtual Health Library, and in the gray literature. Observational studies that evaluated the association between DA and sagittal, vertical, or transversal malocclusions were included. The quality assessment was performed using the Newcastle-Ottawa Scale (NOS). The data from primary studies were narratively synthesized. The certainty of evidence was evaluated using the GRADE approach. The study was conducted from August 2023 to October 2023. RESULTS: One Thousand Nine Hundred Ninety-One records were identified in the initial search. Twenty (n = 20) studies were included. Most of the studies (n=15) presented a moderate quality according to NOS. Twelve studies evaluated the association between DA and sagittal discrepancies; eight studies evaluated vertical discrepancies, and only one study analyzed a transversal discrepancy. Demirjian's method for DA assessment was the most used among the studies. The primary studies observed that patients of both sexes presenting a vertical growth pattern and males with skeletal Class III malocclusion tend to have advanced DA. The study that investigated transversal malocclusion found that unilateral posterior cross-bite is associated with delayed DA. The certainty of evidence was very low for all outcomes evaluated. CONCLUSION: DA may be associated with the type of malocclusion. It is suggested that DA can be used as an initial diagnostic tool in orthodontics. Future well-designed studies should be performed in order to investigate the association between DA and different types of malocclusions in more detail. TRIAL REGISTRATION: This study was registered in the PROSPERO database (CRD42023454207).

A comparison of estimated age based on pulp volume from cone beam computed tomography (CT) images and panoramic radiography data with chronological age.

This retrospective study was conducted to evaluate different methods for dental age estimation in children and to examine the feasibility of using cone beam computed tomography (CBCT) data for age estimation. A total of 200 radiographic records (both digital panoramic radiographs and CBCTs) were acquired from 100 children aged 9 to 16 years, all taken on the same dates. Radiographic data was acquired from archived records and included both panoramic radiography and CBCT data belonging to the same individual. CBCT was used when panoramic radiographic data was insufficient. The pulp volume and pulp/tooth volume ratio of the left first molar teeth in the mandible were calculated from the CBCT data using MIMICS software. In addition, age was estimated by the Demirjian and Willems methods from data obtained from panoramic radiography images. Statistical analyses and linear regression analysis were performed as necessary. There was a statistically significant difference between the mean difference between the Demirjian method and chronological age, and between the Willems method and chronological age (p < 0.001). Statistically significance was achieved in a linear regression model created from pulp volume (R2 = 0.098) and pulp/tooth volume ratio (R2 = 0.395) data for the estimated dental age analysis (p < 0.001) and a negative correlation was observed with chronological age. When compared estimated dental age from CBCT data with chronological age, the pulp/tooth volume ratio method yielded results closer to chronological age than using only pulp volume data. When considering both panoramic radiographic age estimation methods and age estimation methods using CBCT data, we found that the results obtained with the Willems method, a panoramic radiographic age estimation technique, provided the closest results to the chronological age. More contributions should be made to the literature regarding the feasibility of age estimation using pulp and tooth volume as an alternative method.

[Forensic age diagnosis by assessment of sternum ossification processes]. / Sudebno-meditsinskaya diagnostika vozrasta posredstvom otsenki protsessov ossifikatsii grudiny.

OBJECTIVE: To study the sternum ossification processes that take place over time as well as the possibilities of sternum X-rays application to evaluate the age on the modern Russian population in forensic terms. MATERIAL AND METHODS: Sternum X-rays with fragments of ribs' chondral parts were obtained from 369 male and female corpses aged 12-96 years without pathological and traumatic changes. The X-rays were done in frontal projection. The set of examined parameters included: complete and partial synostosis of manubrium and body, induration along the synostosis (or on the articulated edges of manubrium and body), complete and partial synostosis of body segments, traces of body segments union, complete and partial synostosis of body and xiphoid process, induration along the synostosis (or on the articulated edges of manubrium and body), form of costal notches, induration of articular edges of body, presence of calcification foci in sternal edge of II-VII ribs' cartilages, depth of sternum notches, overgrowths on articular edges of body, presence of union between body's costal notches and calcification centers of ribs' cartilages. Mean values, standard deviation and correlation coefficient were determined. RESULTS: The maximal correlation with age was found in such parameters such: overgrowths on articular edges of body (r=0.6), presence of calcification foci in sternal edge of II-VII ribs (r=0.6), induration of body's articular edges (r=0.5), presence of union between costal notches and calcification centers of ribs cartilages (r=0.5), synostosis of body and xiphoid process (r=0.4). CONCLUSION: A subsequent construction of linear model for age diagnosis has its details when comparing the predicted age with the real.

[Validation of method for estimation age of majority by the third molar maturity index on the sample of Ufa population]. / Validatsiya metoda opredeleniya vozrasta sovershennoletiya po indeksu zrelosti tret'ego molyara na vyborke naseleniya g. Ufy.

The assessment of majority age is important for determining legal responsibility. The definition of the 3rd molar maturity index (Im3) have proven to be a simple and effective method of majority age establishment, the accuracy of which has been tested in different populations. There is a clear lack of studies in this scientific area in Russia. OBJECTIVE: To test diagnostic accuracy of majority age assessment method by 3rd molar (Cameriere index) in the Ufa sample. MATERIAL AND METHODS: The number of orthopantomograms equal 120 from males and females aged from 14 to 23 years without apparent pathological changes of the pulp of teeth 38 and 48 was examined. The measures were conducted using the standard R. Cameriere method. The method of logistic regression, determination of predictive values of sensitivity and specificity were used. RESULTS: The Im3 cut-off point, that was equal 0.08, had a sensitivity of 89% and specificity of 95% in the male sample, accuracy of age group determination was 93%. In the female sample the method sensitivity was 93%, specificity - 97%, accuracy - 97%. CONCLUSION: The study results confirmed the absence of population variability of the Cameriere index value, that makes it possible to use the majority age determination method by 3rd molar for practical application in the examination of a living person at the territory of the Russian Federation.

[Morphology of sternum for formation of the biological profile of unidentified individual]. / Morfologiya grudiny dlya formirovaniya biologicheskogo profilya neopoznannogo individa.

Identification of a person by general group characteristics does not lose its relevance over a long period. An analysis of publications (2000-2023) devoted to the possibilities of using the sternum to determine gender and age showed a fairly large amount of work on this topic, with very promising results. The trend in the development of this area is the use of modern methods of medical imaging. This becomes the starting point for conducting such studies on the territory of the Russian Federation and developing a methodology that includes the Russian population, taking into account their population characteristics.

Deep learning enables automatic adult age estimation based on CT reconstruction images of the costal cartilage.

OBJECTIVE: Adult age estimation (AAE) is a challenging task. Deep learning (DL) could be a supportive tool. This study aimed to develop DL models for AAE based on CT images and compare their performance to the manual visual scoring method. METHODS: Chest CT were reconstructed using volume rendering (VR) and maximum intensity projection (MIP) separately. Retrospective data of 2500 patients aged 20.00-69.99 years were obtained. The cohort was split into training (80%) and validation (20%) sets. Additional independent data from 200 patients were used as the test set and external validation set. Different modality DL models were developed accordingly. Comparisons were hierarchically performed by VR versus MIP, single-modality versus multi-modality, and DL versus manual method. Mean absolute error (MAE) was the primary parameter of comparison. RESULTS: A total of 2700 patients (mean age = 45.24 years ± 14.03 [SD]) were evaluated. Of single-modality models, MAEs yielded by VR were lower than MIP. Multi-modality models generally yielded lower MAEs than the optimal single-modality model. The best-performing multi-modality model obtained the lowest MAEs of 3.78 in males and 3.40 in females. On the test set, DL achieved MAEs of 3.78 in males and 3.92 in females, which were far better than the MAEs of 8.90 and 6.42 respectively, for the manual method. For the external validation, MAEs were 6.05 in males and 6.68 in females for DL, and 6.93 and 8.28 for the manual method. CONCLUSIONS: DL demonstrated better performance than the manual method in AAE based on CT reconstruction of the costal cartilage. CLINICAL RELEVANCE STATEMENT: Aging leads to diseases, functional performance deterioration, and both physical and physiological damage over time. Accurate AAE may aid in diagnosing the personalization of aging processes. KEY POINTS: • VR-based DL models outperformed MIP-based models with lower MAEs and higher R2 values. • All multi-modality DL models showed better performance than single-modality models in adult age estimation. • DL models achieved a better performance than expert assessments.

Magnetic resonance imaging of the knee for chronological age estimation-a systematic review.

INTRODUCTION: Radiographs of the hand and teeth are frequently used for medical age assessment, as skeletal and dental maturation correlates with chronological age. These methods have been criticized for their lack of precision, and magnetic resonance imaging (MRI) of the knee has been proposed as a more accurate method. The aim of this systematic review is to explore the scientific and statistical evidence for medical age estimation based on skeletal maturation as assessed by MRI of the knee. MATERIALS AND METHODS: A systematic review was conducted that included studies published before April 2021 on living individuals between 8 and 30 years old, with presumptively healthy knees for whom the ossification stages had been evaluated using MRI. The correlation between "mature knee" and chronological age and the risk of misclassifying a child as an adult and vice versa was calculated. RESULTS: We found a considerable heterogeneity in the published studies -in terms of study population, MRI protocols, and grading systems used. There is a wide variation in the correlation between maturation stage and chronological age. CONCLUSION: Data from published literature is deemed too heterogenous to support the use of MRI of the knee for chronological age determination. Further, it is not possible to assess the sensitivity, specificity, negative predictive value, or positive predictive value for the ability of MRI to determine whether a person is over or under 18 years old. KEY POINTS: • There is an insufficient scientific basis for the use of magnetic resonance imaging of the knee in age determination by skeleton. • It is not possible to assess the predictive value of MRI of the knee to determine whether a person is over or under 18 years of age.