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Related Concept Videos

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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
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Multi-Task Deep Learning for Sex and Age Estimation from Panoramic Radiographs in a Brazilian Young Population.

Matheus L Oliveira1, Su Yang2, Matheus Sampaio-Oliveira1

  • 1Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas, Piracicaba, São Paulo, Brazil.

International Dental Journal
|January 28, 2026
PubMed
Summary

This study introduces ForensicNet, a deep learning model for accurate age and sex estimation from dental radiographs in young Brazilians. The AI tool offers objective and rapid analysis for forensic and clinical use.

Keywords:
Age estimationForensic dentistryMulti-task deep learningPanoramic radiographySex estimation

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Area of Science:

  • Forensic dentistry
  • Artificial intelligence in medicine
  • Medical imaging analysis

Background:

  • Accurate age and sex estimation is vital in forensic and clinical settings.
  • Traditional methods for age and sex determination are often subjective and time-consuming.
  • Panoramic radiographs contain valuable data for automated analysis.

Purpose of the Study:

  • To develop and evaluate a multi-task deep learning framework, ForensicNet, for simultaneous age and sex estimation.
  • To utilize panoramic radiographs for automated analysis in young Brazilian individuals (5-15 years).
  • To address the limitations of conventional subjective methods.

Main Methods:

  • A dataset of 2200 panoramic radiographs was collected and split into training, validation, and test sets.
  • A multi-task deep learning model (ForensicNet) based on EfficientNet-B3 was implemented.
  • The model incorporated Convolutional Block Attention Modules (CBAM) and was trained end-to-end with weighted multi-task loss.

Main Results:

  • ForensicNet outperformed benchmark models in both age estimation (lowest mean absolute error, highest R-squared) and sex classification (highest accuracy, AUC).
  • Grad-CAM visualizations confirmed the model's focus on relevant anatomical areas.
  • Ablation studies indicated the importance of CBAM and task weights for optimal performance.

Conclusions:

  • The ForensicNet framework shows robust performance for age and sex estimation from panoramic radiographs in young Brazilians.
  • This AI tool has potential applications in forensic identification and pediatric clinical contexts.
  • It offers a fast, objective, and reproducible alternative to traditional methods.