Age estimation in Turkish children and young adolescents using fourth cervical vertebra

Accuracy of Artificial Intelligence for Cervical Vertebral Maturation Assessment-A Systematic Review

Background/Objectives: To systematically review and summarize the existing scientific evidence on the diagnostic performance of artificial intelligence (AI) in assessing cervical vertebral maturation (CVM). This review aimed to evaluate the accuracy and reliability of AI algorithms in comparison to those of experienced clinicians. Methods: Comprehensive searches were conducted across multiple databases, including PubMed, Scopus, Web of Science, and Embase, using a combination of Boolean operators and MeSH terms. The inclusion criteria were cross-sectional studies with neural network research, reporting diagnostic accuracy, and involving human subjects. Data extraction and quality assessment were performed independently by two reviewers, with a third reviewer resolving any disagreements. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 tool was used for bias assessment. Results: Eighteen studies met the inclusion criteria, predominantly employing supervised learning techniques, especially convolutional neural networks (CNNs). The diagnostic accuracy of AI models for CVM assessment varied widely, ranging from 57% to 95%. The factors influencing accuracy included the type of AI model, training data, and study methods. Geographic concentration and variability in the experience of radiograph readers also impacted the results. Conclusions: AI has considerable potential for enhancing the accuracy and reliability of CVM assessments in orthodontics. However, the variability in AI performance and the limited number of high-quality studies suggest the need for further research.

Forensic Age Estimation: A Multifactorial Approach in a Retrospective Population Study

Objectives: The objective of this study was to evaluate the accuracy of forensic age estimation in a German population by combining clavicle and wisdom teeth assessments based on cone beam computed tomography (CT) data. The study aimed to determine the reliability of this approach in predicting biological age. Material and Methods: A total of 161 CT data sets from 120 males and 41 females with known exact ages were evaluated by three raters. The clavicle was assessed according to stages 1-5 (including substages 2a-c and 3a-c), and the Demirjian stage's classification method was used for the wisdom teeth. Inter-class correlation (ICC) was calculated to assess the agreement among the three raters. Additionally, ordinary least square regressions were performed to predict chronological age using the clavicle or one of the four teeth. Finally, age prediction models using multiple indicators were developed. Results: The ICCs ranged from 0.82 for the clavicle to 0.86 and 0.88 for the wisdom teeth. Linear estimation tended to overestimate chronological age, especially in subjects over 18 years old. The clavicle showed the strongest overestimation. Combining age estimation from the clavicle with the upper and lower wisdom teeth improved the predictive power, resulting in a 14% and 15% increase in R² for the upper and lower wisdom teeth, respectively. Adding more than one tooth to the prediction did not improve the predictive power (all ΔR² < 1%). Conclusions: Age estimation using CT can be significantly improved by combining information from the analysis of wisdom teeth with age estimation based on the clavicle.

Deep focus approach for accurate bone age estimation from lateral cephalogram

Background/purpose

Bone age is a useful indicator of children's growth and development. Recently, the rapid development of deep-learning technique has shown promising results in estimating bone age. This study aimed to devise a deep-learning approach for accurate bone-age estimation by focusing on the cervical vertebrae on lateral cephalograms of growing children using image segmentation.

Materials and methods

We included 900 participants, aged 4-18 years, who underwent lateral cephalogram and hand-wrist radiograph on the same day. First, cervical vertebrae segmentation was performed from the lateral cephalogram using DeepLabv3+ architecture. Second, after extracting the region of interest from the segmented image for preprocessing, bone age was estimated through transfer learning using a regression model based on Inception-ResNet-v2 architecture. The dataset was divided into train:test sets in a ratio of 4:1; five-fold cross-validation was performed at each step.

Results

The segmentation model possessed average accuracy, intersection over union, and mean boundary F1 scores of 0.956, 0.913, and 0.895, respectively, for the segmentation of cervical vertebrae from lateral cephalogram. The regression model for estimating bone age from segmented cervical vertebrae images yielded average mean absolute error and root mean squared error values of 0.300 and 0.390 years, respectively. The coefficient of determination of the proposed method for the actual and estimated bone age was 0.983. Our method visualized important regions on cervical vertebral images to make a prediction using the gradient-weighted regression activation map technique.

Conclusion

Results showed that our proposed method can estimate bone age by lateral cephalogram with sufficiently high accuracy.

Evaluation of the compatibility of C2, C3, and C4 fractal dimension values with hand-wrist and cervical vertebra maturation methods in determining skeletal maturation

OBJECTIVES

To examine the correlation between the fractal dimension (FD) values of cervical vertebrae (C2, C3, and C4) and hand-wrist maturation (HWM) and cervical vertebra maturation (CVM) methods. In addition, the correlation between the pubertal growth spurt (PGS) and FD values was examined.

METHODS

The lateral cephalometric and hand-wrist radiographs of 120 subjects (61 females and 59 males) aged 8-18 years with a mean age of 13 years, were evaluated retrospectively. The CVM stages were determined in accordance with Hassel-Farman's modification of Lamparski criteria. The HWM stages were determined in accordance with Björk and Grave-Brown criteria. Both HWM and CVM stages were divided into two according to PGS. Fractal analysis of cervical vertebrae was performed according to the White-Rudolph method. The relationships between HWM stages, CVM stages and other variables were evaluated by Spearman's rank-order correlation coefficient. The relationships between FD values, chronological age, and divided stages were evaluated by the Pearson correlation coefficient. The Mann-Whitney U test was used to compare the FD values of two divided stages.

RESULTS

There were positive and statistically significant positive correlations between chronological age and both HWM and CVM stages. There was a positive and statistically significant correlation between CVM and HWM stages. In females and total, there were negative and statistically significant correlations between C4 FD values and divided HWM stages, and the differences between C4 FD values of divided HWM stages were also statistically significant. In total, there was a negative and statistically significant correlation between C4 FD values and divided CVM stages, and the difference between C4 FD values of divided CVM stages was also statistically significant.

CONCLUSION

The negative correlations found between C4 FD values and divided HWM and CVM stages suggest the fractal analysis of cervical vertebrae, especially of C4, may be used as an objective tool for evaluating the PGS.

Zur Anwendbarkeit der dentalen Methode von Roberts et al. aus dem Jahr 2016 zum Nachweis der Vollendung des 18. Lebensjahres lebender Personen

Es sollte die Methode von Roberts et al. aus dem Jahr 2016 zum Nachweis der Vollendung des 18. Lebensjahres überprüft werden.

Dazu wurden 603 Orthopantomogramme (OPG) von 300 Frauen und 303 Männern im Alter von 16,01 bis 25,99 Jahren von 3 Untersuchern ausgewertet, wobei ein Hauptuntersucher alle OPGs zweimal begutachtet hat. Durch die 3 Untersucher wurde eine konsensuale Bestimmung für die Fälle vorgenommen, bei denen mindestens ein Untersucher ein Stadium zugeordnet hatte.

In 31 Fällen (11 Frauen, 20 Männer) konnte konsensuell ein Stadium bestimmt werden. Hauptursache für die Nichtauswertbarkeit war die nicht abgeschlossene Entwicklung des Zahnes 38 [FDI] (30,18 %), gefolgt von Karies, Restaurationen oder anderen Pathologien (20,56 %) und dem Fehlen des Zahnes 38 (19,57 %). Das Stadium „RCW‑C“ konnte bei den Frauen konsensual nicht, die Stadien „RCW‑B“ und „RCW‑C“ konnten bei den Männern nur 4‑mal (3x „RCW-C“, 1x „RCW-B“) bestimmt werden. Der Cohen’s-Kappa-Wert für die Binnenbeobachterübereinstimmung für die 47 Fälle, in denen der Hauptuntersucher in mindestens einem Durchgang ein Stadium zugeordnet hatte, lag im moderaten bzw. guten Bereich (Frauen: 0,44; Männer: 0,62). Der Fleiss’-Kappa-Wert für die Zwischenbeobachterübereinstimmung der 3 Untersucher für die 69 Fälle, bei denen mindestens ein Untersucher ein Stadium zugeordnet hatte, lag im mangelhaften Bereich (Frauen: 0,07; Männer: 0,11), wobei die 95%-Konfidenzintervalle für den Kappa-Wert auch die „0“ einschlossen.

Alle Personen, bei denen ein Stadium zugeordnet wurde, waren über 18 Jahre alt. Aktuell kann eine Anwendung der Methode nicht empfohlen werden. Es wird die Frage aufgeworfen, ob den Stadien eine allgemeingültige Entwicklungsabfolge zu Grunde zu liegt, da bei 30 Fällen der Befund nicht mit den Stadien in Deckung zu bringen war.