Machine Learning Supported the Modified Gustafson's Criteria for Dental Age Estimation in Southwest China.
Adult age estimation is one of the most challenging problems in forensic science and physical anthropology. In this study, we aimed to develop and evaluate machine learning (ML) methods based on the modified Gustafson's criteria for dental age estimation. In this retrospective study, a total of 851...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 2; pp. 611 - 620 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=177626006&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 177626006 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2024 vid: 37 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 177626006 177626006 177626006 10.1007/s10278-023-00956-0 177626006 ppf: 611 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Machine Learning Supported the Modified Gustafson's Criteria for Dental Age Estimation in Southwest China. aug: au: Dai, Xinhua Liu, Anjie Liu, Junhong Zhan, Mengjun Liu, Yuanyuan Ke, Wenchi Shi, Lei Huang, Xinyu Chen, Hu Deng, Zhenhua Fan, Fei affil: https://ror.org/011ashp19 West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, 610041, Chengdu, People's Republic of China sug: subj: Machine Learning Utilization Age Determination by Teeth China Human Forensic Dentistry China Retrospective Design Male Female Radiography, Panoramic Adolescence Young Adult Adult Funding Source Adolescent: 13-18 years Adult: 19-44 years Male Female ab: Adult age estimation is one of the most challenging problems in forensic science and physical anthropology. In this study, we aimed to develop and evaluate machine learning (ML) methods based on the modified Gustafson's criteria for dental age estimation. In this retrospective study, a total of 851 orthopantomograms were collected from patients aged 15 to 40 years old. The secondary dentin formation (SE), periodontal recession (PE), and attrition (AT) of four mandibular premolars were analyzed according to the modified Gustafson's criteria. Ten ML models were generated and compared for age estimation. The partial least squares regressor outperformed other models in males with a mean absolute error (MAE) of 4.151 years. The support vector regressor (MAE = 3.806 years) showed good performance in females. The accuracy of ML models is better than the single-tooth model provided in the previous studies (MAE = 4.747 years in males and MAE = 4.957 years in females). The Shapley additive explanations method was used to reveal the importance of the 12 features in ML models and found that AT and PE are the most influential in age estimation. The findings suggest that the modified Gustafson method can be effectively employed for adult age estimation in the southwest Chinese population. Furthermore, this study highlights the potential of machine learning models to assist experts in achieving accurate and interpretable age estimation. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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