Automatic maxillary sinus segmentation and age estimation model for the northwestern Chinese Han population.
Background: Age estimation is vital in forensic science, with maxillary sinus development serving as a reliable indicator. This study developed an automatic segmentation model for maxillary sinus identification and parameter measurement, combined with regression and machine learning models for age e...
| Publicado en: | BMC Oral Health Vol. 25; no. 1; pp. 1 - 11 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
BioMed Central
2/26/2025
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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=183283771&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 183283771 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726831 1CIC jtl: BMC Oral Health issn: 14726831 maglogo: N pubinfo: dt: 2/26/2025 vid: 25 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 183283771 183283771 183283771 10.1186/s12903-025-05618-x 183283771 ppf: 1 ppct: 10 formats: tig: atl: Automatic maxillary sinus segmentation and age estimation model for the northwestern Chinese Han population. aug: au: Guo, Yu-Xin Lan, Jun-Long Bu, Wen-Qing Tang, Yu Wu, Di Yang, Hui Ren, Jia-Chen Song, Yu-Xuan Yue, Hong-Ying Guo, Yu-Cheng Meng, Hao-Tian affil: https://ror.org/017zhmm22 Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, 710004, Xi'an, Shaanxi, People's Republic of China sug: subj: Maxillary Sinus Radiography Age Determination by Teeth Methods Forensic Anthropology Tomography, X-Ray Computed Chinese Persons Image Interpretation, Computer Assisted Models, Statistical Forensic Dentistry Predictive Value of Tests Machine Learning Human Funding Source China Male Female Child Adolescence Adult Middle Age Maxillary Sinus Anatomy and Histology Descriptive Statistics Data Analysis Software Pearson's Correlation Coefficient Multiple Linear Regression T-Tests Prediction Models Reproducibility of Results Child: 6-12 years Adolescent: 13-18 years Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Background: Age estimation is vital in forensic science, with maxillary sinus development serving as a reliable indicator. This study developed an automatic segmentation model for maxillary sinus identification and parameter measurement, combined with regression and machine learning models for age estimation. Methods: Cone Beam Computed Tomography (CBCT) images from 292 Han individuals (ranging from 5 to 53 years) were used to train and validate the segmentation model. Measurements included sinus dimensions (length, width, height), inter-sinus distance, and volume. Age estimation models using multiple linear regression and random forest algorithms were built based on these variables. Results: The automatic segmentation model achieved high accuracy, which yielded a Dice similarity coefficient (DSC) of 0.873, an Intersection over Union (IoU) of 0.7753, a Hausdorff Distance 95% (HD95) of 9.8337, and an Average Surface Distance (ASD) of 2.4507. The regression model performed best, with mean absolute errors (MAE) of 1.45 years (under 18) and 3.51 years (aged 18 and above), providing relatively precise age predictions. Conclusion: The maxillary sinus-based model is a promising tool for age estimation, particularly in adults, and could be enhanced by incorporating additional variables like dental dimensions. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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