Examination of regressive features of third molars for the purpose of age assessment in the living by means of rescaled regression analyses

Timme, Maximilian; Timme, Walter; Olze, Andreas; Ottow, Christian; Gladitz, J.; Pfeiffer, Heidi; Dettmeyer, R.; Schmeling, Andreas

doi:10.1007/s00414-019-02144-8

Cited by 4 publications

(2 citation statements)

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“…As shown in Table 15 , the study of Chaudhary and Liversidge [ 71 ] pointed out an overall overestimate of 7.21 years for males and 6.87 years for females, being the mean absolute error of 7.91 and 7.74 years in the same two scenarios. On the other hand, Timme et al [ 72 ] did not report the error metrics, but a standard error of 3.55 years and the percentage of explained variance (69%).…”

Section: Evaluation Studiesmentioning

confidence: 99%

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A systematic overview of dental methods for age assessment in living individuals: from traditional to artificial intelligence-based approaches

et al. 2023

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Dental radiographies have been used for many decades for estimating the chronological age, with a view to forensic identification, migration flow control, or assessment of dental development, among others. This study aims to analyse the current application of chronological age estimation methods from dental X-ray images in the last 6 years, involving a search for works in the Scopus and PubMed databases. Exclusion criteria were applied to discard off-topic studies and experiments which are not compliant with a minimum quality standard. The studies were grouped according to the applied methodology, the estimation target, and the age cohort used to evaluate the estimation performance. A set of performance metrics was used to ensure good comparability between the different proposed methodologies. A total of 613 unique studies were retrieved, of which 286 were selected according to the inclusion criteria. Notable tendencies to overestimation and underestimation were observed in some manual approaches for numeric age estimation, being especially notable in the case of Demirjian (overestimation) and Cameriere (underestimation). On the other hand, the automatic approaches based on deep learning techniques are scarcer, with only 17 studies published in this regard, but they showed a more balanced behaviour, with no tendency to overestimation or underestimation. From the analysis of the results, it can be concluded that traditional methods have been evaluated in a wide variety of population samples, ensuring good applicability in different ethnicities. On the other hand, fully automated methods were a turning point in terms of performance, cost, and adaptability to new populations.

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Section: Evaluation Studiesmentioning

confidence: 99%

“… μ σ med. Chaudhary and Liversidge [ 71 ] PL British 163 16–45 + 7.21/ + 6.87 5.16/5.83 – 7.91/7.74 – – – – Timme et al [ 72 ] PL and RP German 1245 15–40 – – – – – – 3.55 0.69 …”

Section: Evaluation Studiesmentioning

confidence: 99%