Scholarlink

Background <p>Forensic odontology involves identification and evaluation of dental evidence to determine the sex of unknown skeletal remains. This is crucial in times of mass disasters and natural calamities. Anatomical features of the skull, particularly the mandible, help in sex determination, and lateral cephalograms provide valuable insights. Artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), has proven effective in medical and dental diagnostics. However, AI integration in forensic odontology is still in early stages. </p> Objective <p>This study aims to assess the accuracy of sex determination using linear and angular measurements of the mandible from lateral cephalograms, by comparing machine learning algorithms.</p> Materials and methods <p>712 lateral cephalometric radiographs were collected from our College. Cephalometric analysis was performed using Webceph software. Key measurements (ramus height, mandibular body length, articular angle, gonion angle, mandibular plane angle) were evaluated using Random Forest and XGBoost machine learning models. </p> Results <p>Both Random Forest and XGBoost demonstrated high performance in sex determination. The Random Forest model achieved an accuracy of 97.20% and precision of 97.65%, slightly outperforming XGBoost.</p> Conclusion <p>Machine learning models are reliable and accurate for sex determination based on mandibular features. These models hold significant potential for practical applications in forensic and clinical settings. </p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Sex determination through analysis of mandibular indices using lateral cephalogram: An Artificial intelligence diagnostics

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月17日

查看全文
  • P. Shalini Prabha,
  • Anuradha Ganesan,
  • Krithika Chandrasekar Lakshmi,
  • Arul Jothi Murugan

摘要

Background

Forensic odontology involves identification and evaluation of dental evidence to determine the sex of unknown skeletal remains. This is crucial in times of mass disasters and natural calamities. Anatomical features of the skull, particularly the mandible, help in sex determination, and lateral cephalograms provide valuable insights. Artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), has proven effective in medical and dental diagnostics. However, AI integration in forensic odontology is still in early stages.

Objective

This study aims to assess the accuracy of sex determination using linear and angular measurements of the mandible from lateral cephalograms, by comparing machine learning algorithms.

Materials and methods

712 lateral cephalometric radiographs were collected from our College. Cephalometric analysis was performed using Webceph software. Key measurements (ramus height, mandibular body length, articular angle, gonion angle, mandibular plane angle) were evaluated using Random Forest and XGBoost machine learning models.

Results

Both Random Forest and XGBoost demonstrated high performance in sex determination. The Random Forest model achieved an accuracy of 97.20% and precision of 97.65%, slightly outperforming XGBoost.

Conclusion

Machine learning models are reliable and accurate for sex determination based on mandibular features. These models hold significant potential for practical applications in forensic and clinical settings.