Semi-supervised automatic dental age and sex estimation using a hybrid transformer model

Fan, Fei; Ke, Wenchi; Dai, Xinhua; Shi, Lei; Liu, Yuanyuan; Lin, Yushan; Cheng, Ziqi; Zhang, Yi; Chen, Hu; Deng, Zhenhua

doi:10.1007/s00414-023-02956-9

Cited by 7 publications

(4 citation statements)

References 25 publications

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“…Looking ahead to the future of forensic age assessment, it becomes evident that the path leads toward fully automated procedures reliant on artificial intelligence [66][67][68][69][70][71][72][73][74][75]. This prompts a broader question about the relevance of the present research.…”

Section: Discussionmentioning

confidence: 96%

Third Molar Eruption in Dental Panoramic Radiographs as a Feature for Forensic Age Assessment—Presentation of a New Non-Staging Method Based on Measurements

Timme,

Bender,

Steffens

et al. 2023

Biology

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The evaluation of third molar eruption in dental panoramic radiographs (DPRs) constitutes an evidence-based approach for forensic age assessment in living individuals. Existing methodologies involve staging morphological radiographic findings and comparing them to reference populations. Conversely, the existing literature presents an alternative method where the distance between third molars and the occlusal plane is measured on dental plaster models. The aim of this study was to adapt this measurement principle for DPRs and to determine correlation between eruption and chronological age. A total of 423 DPRs, encompassing 220 females and 203 males aged 15 to 25 years, were examined, including teeth 38 [FDI] and 48. Two independent examiners conducted the measurements, with one examiner providing dual assessments. Ultimately, a quotient was derived by comparing orthogonal distances from the mesial cementoenamel junctions of the second and third molars to a simplified radiological occlusal plane. This quotient was subsequently correlated with the individual’s age. We estimated correlations between age and quotients, as well as inter- and intra-rater reliability. Correlation coefficients (Spearman’s rho) between measurements and individuals’ ages ranged from 0.555 to 0.597, conditional on sex and tooth. Intra-rater agreement (Krippendorf’s alpha) ranged from 0.932 to 0.991, varying according to the tooth and sex. Inter-rater agreement ranged from 0.984 to 0.992, with distinctions drawn for different teeth and sex. Notably, all observer agreement values fell within the “very good” range. In summary, assessing the distance of third molars from a simplified occlusal plane in DPRs emerges as a new and promising method for evaluating eruption status in forensic age assessment. Subsequent reference studies should validate these findings.

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Section: Discussionmentioning

confidence: 96%

Third Molar Eruption in Dental Panoramic Radiographs as a Feature for Forensic Age Assessment—Presentation of a New Non-Staging Method Based on Measurements

Timme,

Bender,

Steffens

et al. 2023

Biology

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“…These anatomical structures were assessed using panoramic radiographs commonly used in the dental field, which provide a broad view of the maxillofacial region [ 11 ]. Recently, deep learning has been widely used in forensic dentistry to estimate sex and chronological age from panoramic radiographs [ 28 , 29 ]. However, previous studies have used datasets with insufficient or non-uniform sex and age distributions, which could lead to inaccurate estimation for a particular sex or age owing to data bias.…”

Section: Discussionmentioning

confidence: 99%

“…They reported an ACC of 0.854 for sex estimation and an MAE of 2.84 ± 3.75 for chronological age estimation. Similarly, Fan et al [ 29 ] proposed a Transformer-based model for sex and chronological age estimation on 15,195 panoramic radiographs acquired from patients aged 16–50 and achieved an ACC of 0.955 for sex estimation and an MAE of 2.61 for chronological age estimation. Zhang et al [ 30 ] proposed a sex-prior guided Transformer-based model for chronological age estimation on 10,703 panoramic radiographs acquired from patients aged 5–25 and achieved an MAE of 0.80 for chronological age estimation.…”

Section: Discussionmentioning

confidence: 99%

“…The MAE value for age estimation was 2.84 ± 3.75 years, and the sex estimation accuracy was 85.4%. Fan et al [ 29 ] estimated sex and age using DASE-Net for 15,195 panoramic radiographs aged 16–50; the MAE for age estimation was 2.61 years and the accuracy of sex estimation was 95.54%. These studies used datasets with insufficient or non-uniform sex and age distributions.…”

Section: Introductionmentioning

confidence: 99%

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Automatic and robust estimation of sex and chronological age from panoramic radiographs using a multi-task deep learning network: a study on a South Korean population

Park,

Yang,

Kim

et al. 2024

Int J Legal Med

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Sex and chronological age estimation are crucial in forensic investigations and research on individual identification. Although manual methods for sex and age estimation have been proposed, these processes are labor-intensive, time-consuming, and error-prone. The purpose of this study was to estimate sex and chronological age from panoramic radiographs automatically and robustly using a multi-task deep learning network (ForensicNet). ForensicNet consists of a backbone and both sex and age attention branches to learn anatomical context features of sex and chronological age from panoramic radiographs and enables the multi-task estimation of sex and chronological age in an end-to-end manner. To mitigate bias in the data distribution, our dataset was built using 13,200 images with 100 images for each sex and age range of 15–80 years. The ForensicNet with EfficientNet-B3 exhibited superior estimation performance with mean absolute errors of 2.93 ± 2.61 years and a coefficient of determination of 0.957 for chronological age, and achieved accuracy, specificity, and sensitivity values of 0.992, 0.993, and 0.990, respectively, for sex prediction. The network demonstrated that the proposed sex and age attention branches with a convolutional block attention module significantly improved the estimation performance for both sex and chronological age from panoramic radiographs of elderly patients. Consequently, we expect that ForensicNet will contribute to the automatic and accurate estimation of both sex and chronological age from panoramic radiographs.

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Machine learning and deep learning enabled age estimation on medial clavicle CT images

Qiu,

Liu,

Dai

et al. 2023

Int J Legal Med

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Semi-supervised automatic dental age and sex estimation using a hybrid transformer model

Cited by 7 publications

References 25 publications

Third Molar Eruption in Dental Panoramic Radiographs as a Feature for Forensic Age Assessment—Presentation of a New Non-Staging Method Based on Measurements

Third Molar Eruption in Dental Panoramic Radiographs as a Feature for Forensic Age Assessment—Presentation of a New Non-Staging Method Based on Measurements

Automatic and robust estimation of sex and chronological age from panoramic radiographs using a multi-task deep learning network: a study on a South Korean population

Machine learning and deep learning enabled age estimation on medial clavicle CT images

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