ArticleBMC oral health2025
Automatic maxillary sinus segmentation and age estimation model for the northwestern Chinese Han population.
Article in BMC oral health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Maxillary sinus classification for sex and age using 23 artificial intelligence architectures.Scientific reports · 2026Article
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11 authors.
Funding
Abstract
backgroundAge 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.
methodsCone 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.
resultsThe 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.
conclusionThe 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.
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