Age estimation based on 3D pulp chamber segmentation of first molars from cone-beam–computed tomography by integrated deep learning and level set

Zheng, Qian; Ge, Zhi-pu; Du, Han; Li, Gang

doi:10.1007/s00414-020-02459-x

Cited by 25 publications

(28 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…While some authors report a low capacity for age estimation [ 11 , 28 , 34 , 36 , 39 , 40 ], others report equations with good R 2 and SEE values [ 19 , 20 , 37 , 38 ], showing that CBCT can be as useful as CT [ 26 , 41 , 42 , 43 , 44 ], or micro-CT [ 18 , 22 , 31 , 32 ]. Various methods involving ratios of dental linear measurements have been employed on CBCT for age estimation, e.g., Kwaal’s method [ 45 , 46 ], Cameriere’s pulp tooth area ratio [ 19 , 20 , 37 , 43 ], pulp to tooth volume ratio [ 31 , 47 , 48 , 49 , 50 ], pulp volume [ 11 , 38 ], and chamber volume [ 29 , 30 , 51 , 52 ]. The equations obtained in the present study using linear measurements and ratios yield acceptable R 2 and SEE values.…”

Section: Discussionmentioning

confidence: 99%

Age Estimation Using Maxillary Central Incisor Analysis on Cone Beam Computed Tomography Human Images

Santos

Muinelo-Lorenzo

Fernández-Alonso

et al. 2022

IJERPH

View full text Add to dashboard Cite

Forensic dentistry plays an important role in human identification, and dental age estimation is an important part of the process. Secondary dentin deposition throughout an individual’s lifetime and consequent modification in teeth anatomy is an important parameter for age estimation procedures. The aim of the present study was to develop regression equations to determine age in adults by means of linear measurements and ratios on sagittal, coronal and axial slices of maxillary central incisors using cone bean computed tomography (CBCT). Multiplanar measurements of upper central incisors were taken for a sample of 373 CBCTs. Subsequently, one-way analysis of variance (ANOVA) and multivariate linear regressions were performed for age estimation. The equations obtained from axial linear measurements and ratios presented a standard error of the estimate (SEE) of ±10.9 years (R2 = 0.49), and a SEE of ±10.8 years (R2 = 0.50), respectively. The equation obtained for multiplanar linear measurements presented a SEE of ±10.9 years (R2 = 0.52), while the equation for multiplanar ratios presented a SEE of ±10.7 years (R2 = 0.51). Thus, CBCT measurements on upper central incisors were found to be an acceptable method for age estimation. Horizontal measurements, especially pulp measurements, improve the accuracy of age estimate equations.

show abstract

Section: Discussionmentioning

confidence: 99%

Age Estimation Using Maxillary Central Incisor Analysis on Cone Beam Computed Tomography Human Images

Santos

Muinelo-Lorenzo

Fernández-Alonso

et al. 2022

IJERPH

View full text Add to dashboard Cite

show abstract

“…Most of the age estimation studies performed with machine learning use deep learning methods 13 – 20 . Deep learning-based age estimation has been reported to be more accurate than conventional methods.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, many studies have been conducted recently using the latest computer technology to minimize human error and efficiently process multi-dimensional data [8][9][10][11][12] . In particular, deep learning is widely used for medical image analysis, and various methods have been reported to estimate age by automatically analyzing radiographs to improve the accuracy and efficiency of age estimation [13][14][15][16][17][18][19][20] . For example, Halabi et al 13 developed machine…”

mentioning

confidence: 99%

“…A study by Guo et al 15 evaluated the classification performance of legal age thresholds by applying CNN technology based on dental orthopantomograms, and the result of the CNN model proved that humans outperformed its age classification performance. Most of the age estimation studies performed with machine learning use deep learning methods [13][14][15][16][17][18][19][20] . Deep learning-based age estimation has been reported to be more accurate than conventional methods.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Validation of data mining models by comparing with conventional methods for dental age estimation in Korean juveniles and young adults

Kumagai

Jeong

Kim³

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Teeth are known to be the most accurate age indicators of human body and are frequently applied in forensic age estimation. We aimed to validate data mining-based dental age estimation, by comparing the accuracy of the estimation and classification performance of 18-year thresholds with conventional methods and with data mining-based age estimation. A total of 2657 panoramic radiographs were collected from Koreans and Japanese populations aged 15 to 23 years. They were subdivided into a training and internal test set of 900 radiographs each from Koreans, and an external test set of 857 radiographs from Japanese. We compared the accuracy and classification performance of the test sets from conventional methods with those from the data mining models. The accuracy of the conventional method with the internal test set was slightly higher than that of the data mining models, with a slight difference (mean absolute error < 0.21 years, root mean square error < 0.24 years). The classification performance of the 18-year threshold was also similar between the conventional method and the data mining models. Thus, conventional methods can be replaced by data mining models in forensic age estimation using second and third molar maturity of Korean juveniles and young adults.

show abstract

“…Die forensische Altersdiagnstik lebender Personen kann als Instrument der Rechtspflege entscheidend zur Durchführung rechtsstaatlicher Verfahren beitragen und ist aktuell ein Schwerpunkt der rechts-medizinischen Forschung [1][2][3][4][5][6][7][8]. Aufgrund der zunehmenden Migrationsbewegungen kam es zuletzt zu einem Anstieg der Gutachtenaufträge zur forensischen Altersdiagnostik.…”

Section: Introductionunclassified

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

et al. 2021

View full text Add to dashboard Cite

ZusammenfassungEs 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.

show abstract

Age estimation based on 3D pulp chamber segmentation of first molars from cone-beam–computed tomography by integrated deep learning and level set

Cited by 25 publications

References 25 publications

Age Estimation Using Maxillary Central Incisor Analysis on Cone Beam Computed Tomography Human Images

Age Estimation Using Maxillary Central Incisor Analysis on Cone Beam Computed Tomography Human Images

Validation of data mining models by comparing with conventional methods for dental age estimation in Korean juveniles and young adults

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

Contact Info

Product

Resources

About