30 years of the ABFO study: Reproduction in a Brazilian sample

Pinheiro, Adriele Alves; Makeeva, Irina; Bueno, Juliano Martins; Miamoto, Paulo; Franco, Ademir

doi:10.1016/j.morpho.2023.04.001

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…On radiographs, forensic odontologists may have two main pathways for age estimation: (I) qualitative analyses of tooth developmental stages and their classification into ordinal data 9 – 11 , and (II) metric analyses of the teeth based on linear measurements and ratios between tooth parts 12 . Stages allocated to the teeth can be used in regressive formulae 13 or can be converted into tabulated scores 14 to lead to an estimated age. Differently, tooth ratios are compared to reference cut-offs to indicate if a person is below or above a certain age threshold of legal interest 15 .…”

Section: Introductionmentioning

confidence: 99%

Binary decisions of artificial intelligence to classify third molar development around the legal age thresholds of 14, 16 and 18 years

Franco,

Murray,

Heng

et al. 2024

Sci Rep

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Third molar development is used for dental age estimation when all the other teeth are fully mature. In most medicolegal facilities, dental age estimation is an operator-dependent procedure. During the examination of unaccompanied and undocumented minors, this procedure may lead to binary decisions around age thresholds of legal interest, namely the ages of 14, 16 and 18 years. This study aimed to test the performance of artificial intelligence to classify individuals below and above the legal age thresholds of 14, 16 and 18 years using third molar development. The sample consisted of 11,640 panoramic radiographs (9680 used for training and 1960 used for validation) of males (n = 5400) and females (n = 6240) between 6 and 22.9 years. Computer-based image annotation was performed with V7 software (V7labs, London, UK). The region of interest was the mandibular left third molar (T38) outlined with a semi-automated contour. DenseNet121 was the Convolutional Neural Network (CNN) of choice and was used with Transfer Learning. After Receiver-operating characteristic curves, the area under the curve (AUC) was 0.87 and 0.86 to classify males and females below and above the age of 14, respectively. For the age threshold of 16, the AUC values were 0.88 (males) and 0.83 (females), while for the age of 18, AUC were 0.94 (males) and 0.83 (females). Specificity rates were always between 0.80 and 0.92. Artificial intelligence was able to classify male and females below and above the legal age thresholds of 14, 16 and 18 years with high accuracy.

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Section: Introductionmentioning

confidence: 99%

Binary decisions of artificial intelligence to classify third molar development around the legal age thresholds of 14, 16 and 18 years

Franco,

Murray,

Heng

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…On radiographs, forensic odontologists may have two main pathways for age estimation: I) qualitative analyses of tooth developmental stages and their classi cation into ordinal data [9][10][11] , and II) metric analyses of the teeth based on linear measurements and ratios between tooth parts 12 . Stages allocated to the teeth can be used in regressive formulae 13 or can be converted into tabulated scores 14 to lead to an estimated age. Differently, tooth ratios are compared to reference cut-offs to indicate if a person is below or above a certain age threshold of legal interest 15 .…”

Section: Introductionmentioning

confidence: 99%

Binary decisions of artificial intelligence to classify third molar development around the legal age thresholds of 14, 16 and 18 years

Franco,

Murray,

Heng

et al. 2023

Preprint

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Third molar development is used for dental age estimation when all the other teeth are fully mature. In most medicolegal facilities, dental age estimation is an operator-dependent procedure. During the examination of unaccompanied and undocumented minors, this procedure may lead to binary decisions around age thresholds of legal interest, namely the ages of 14, 16 and 18 years. This study aimed to test the performance of artificial intelligence to classify individuals below and above the legal age thresholds of 14, 16 and 18 years using third molar development. The sample consisted of 11.640 (9.680 used for training and 1.960 used for validation) panoramic radiographs of males (n = 5.400) and females (n = 6.240) between 6 and 22.9 years. Computer-based image annotation was performed with V7 software (V7labs, London, UK). The region of interest was the semi-automated contour of the mandibular left third molar (T38). DenseNet 121 was the Convolutional Neural Network (CNN) of choice. Transfer Learning architecture was used. After Receiver-operating characteristic curves, the area under the curve (AUC) was 0.87 and 0.86 to classify males and females below and above the age of 14, respectively. For the age threshold of 16, the AUC values were 0.88 (males) and 0.83 (females), while for the age of 18, AUC were 0.94 (males) and 0.83 (females). Specificity rates were always between 0.80 and 0.92. Artificial intelligence was able to classify male and females below and above the legal age thresholds of 14, 16 and 18 years with high accuracy.

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“…In recent years it has become increasingly important to determine the age of living people for a variety of reasons, including identifying criminal and legal responsibility and for many other social events [6]. Forensic odontology plays an important role in the identification of the age of the human.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Dental Age by Mandibular Third Molar Through Digital Orthopantomogram Using Modified Demirjian Method In South Indian Population Visiting A Dental College- A Retrospective Study

Veluchamy,

Achuthan,

Pancharethinam

2023

IJForenO

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Aim: The aim of this study was to evaluate the applicability of Demirjian’s method for dental age assessment in a group of South Indian population and to develop an age-predictive equation suitable for the studied group. Subjects and methods: In this retrospective, blind, cross-sectional study, 120 Digital Orthopantomograms were selected from the archived medical files of patients attending Dental College and evaluated to estimate dental age. Results: Age was over estimated for almost all of the studied subjects with an accuracy range from 0.18 to 1.19 years for males and from 0.08 to 0.87 years for females, with the exception of two age subgroups Conclusion: Demirjian’s method is very useful and suitable for the South Indian population. The development of a prediction equation and the introduction of adaptable conversion tables to transform the maturity score into a dental age for the South Indian population with population-specific samples may be suitable alternatives.

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30 years of the ABFO study: Reproduction in a Brazilian sample

Cited by 4 publications

References 24 publications

Binary decisions of artificial intelligence to classify third molar development around the legal age thresholds of 14, 16 and 18 years

Binary decisions of artificial intelligence to classify third molar development around the legal age thresholds of 14, 16 and 18 years

Binary decisions of artificial intelligence to classify third molar development around the legal age thresholds of 14, 16 and 18 years

Estimation of Dental Age by Mandibular Third Molar Through Digital Orthopantomogram Using Modified Demirjian Method In South Indian Population Visiting A Dental College- A Retrospective Study

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