Modified method of dental age estimation of Malay juveniles

Bunyarit, Safar Sumit; Jayaraman, Jayakumar; Naidu, Murali; Poh, R.; Danaee, Mahmoud; Nambiar, Phrabhakaran

doi:10.1016/j.legalmed.2017.07.009

Cited by 11 publications

(5 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Comparing the conducted study with other works [46][47][48][49], it should be emphasized that the works performed were dedicated to children of a certain age. The teaching set included a representative research group, which probably influenced the neural modeling process.…”

Section: Discussionmentioning

confidence: 98%

See 1 more Smart Citation

Tooth and Bone Parameters in the Assessment of the Chronological Age of Children and Adolescents Using Neural Modelling Methods

Zaborowicz

Biedziak

Olszewska

et al. 2021

Sensors

View full text Add to dashboard Cite

The analog methods used in the clinical assessment of the patient’s chronological age are subjective and characterized by low accuracy. When using those methods, there is a noticeable discrepancy between the chronological age and the age estimated based on relevant scientific studies. Innovations in the field of information technology are increasingly used in medicine, with particular emphasis on artificial intelligence methods. The paper presents research aimed at developing a new, effective methodology for the assessment of the chronological age using modern IT methods. In this paper, a study was conducted to determine the features of pantomographic images that support the determination of metric age, and neural models were produced to support the process of identifying the age of children and adolescents. The whole conducted work was a new methodology of metric age assessment. The result of the conducted study is a set of 21 original indicators necessary for the assessment of the chronological age with the use of computer image analysis and neural modelling, as well as three non-linear models of radial basis function networks (RBF), whose accuracy ranges from 96 to 99%. The result of the research are three neural models that determine the chronological age.

show abstract

Section: Discussionmentioning

confidence: 98%

“…Neural models are also used in prosthetic [ 47 ] and conservative treatment [ 48 ]. In 2017, the Bunyarit team used the Demirijan method indicators in neural modelling to more accurately estimate the dental age of Malaysian children [ 49 ].…”

Section: Introductionmentioning

confidence: 99%

Tooth and Bone Parameters in the Assessment of the Chronological Age of Children and Adolescents Using Neural Modelling Methods

Zaborowicz

Biedziak

Olszewska

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Continued work has been done in this area to get population-specific data for age estimation of Malaysians with promising results. 19,20…”

Section: Discussionmentioning

confidence: 99%

Age estimation from developing third molars

et al. 2021

Self Cite

View full text Add to dashboard Cite

We investigated the development of third molars among Malaysians (including variations between jaws and genders) using Demirjian’s method. Dental panoramic radiographs of 1224 subjects aged 8 to 24 years were examined, and the molars were assigned Demirjian et al.’s development grades (A–H). Results indicated that 18.8% had congenitally missing or extracted third molars. Development of molars begins earlier in females (also in the mandible), but by age 9, male children's molar development speeds up with more advanced grades in their middle teens than females. Grade C indicates the subject is a juvenile, while initiation of root development (Grade E), was observed from 13 years on. Grade H can occur in a child aged 18 years who technically is still a juvenile. We compared the development and growth patterns of the third molar from both the maxilla and the mandible.

show abstract

“…Innovations in the field of computer science, including methods of artificial intelligence, are increasingly used in medicine. They support diagnosis and improve treatment efficiency [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone Parameters

Zaborowicz

Biedziak

et al. 2022

Sensors

View full text Add to dashboard Cite

Dental age is one of the most reliable methods for determining a patient’s age. The timing of teething, the period of tooth replacement, or the degree of tooth attrition is an important diagnostic factor in the assessment of an individual’s developmental age. It is used in orthodontics, pediatric dentistry, endocrinology, forensic medicine, and pathomorphology, but also in scenarios regarding international adoptions and illegal immigrants. The methods used to date are time-consuming and not very precise. For this reason, artificial intelligence methods are increasingly used to estimate the age of a patient. The present work is a continuation of the work of Zaborowicz et al. In the presented research, a set of 21 original indicators was used to create deep neural network models. The aim of this study was to verify the ability to generate a more accurate deep neural network model compared to models produced previously. The quality parameters of the produced models were as follows. The MAE error of the produced models, depending on the learning set used, was between 2.34 and 4.61 months, while the RMSE error was between 5.58 and 7.49 months. The correlation coefficient R2 ranged from 0.92 to 0.96.

show abstract

Modified method of dental age estimation of Malay juveniles

Cited by 11 publications

References 31 publications

Tooth and Bone Parameters in the Assessment of the Chronological Age of Children and Adolescents Using Neural Modelling Methods

Tooth and Bone Parameters in the Assessment of the Chronological Age of Children and Adolescents Using Neural Modelling Methods

Age estimation from developing third molars

Deep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone Parameters

Contact Info

Product

Resources

About