Comparison of mandibular morphometric parameters in digital panoramic radiography in gender determination using machine learning

Hanife Pertek, Mustafa Kamaşak, Soner Kotan, Fatma Pertek Hatipoğlu, Ömer Hatipoğlu*, Taha Emre Köse

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Objective: This study aimed to evaluate the usability of morphometric features obtained from mandibular panoramic radiographs in gender determination using machine learning algorithms. Materials and methods: High-resolution radiographs of 200 patients aged 20–77 (41.0 ± 12.7) were included in the study. Twelve different morphometric measurements were extracted from each digital panoramic radiography included in the study. These measurements were used as features in the machine learning phase in which six different machine learning algorithms were used (k-nearest neighbor, decision trees, support vector machines, naive Bayes, linear discrimination analysis, and neural networks). To evaluate the reliability, we have performed tenfold cross-validation and we repeated this 10 times for every classification process. This process enhances the reliability of the results for other datasets. Results: When all 12 features are used together, the accuracy rate is found to be 82.6 ± 0.5%. The classification accuracies are also compared using each feature alone. Three features that give the highest accuracy are coronoid height (80.9 ± 0.9%), condyle height (78.2 ± 0.5%), and ramus height (77.2 ± 0.4%), respectively. When compared to the classification algorithms, the highest accuracy was obtained with the naive Bayes algorithm with a rate of 84.0 ± 0.4%. Conclusion: Machine learning techniques can accurately determine gender by analyzing mandibular morphometric structures from digital panoramic radiographs. The most precise results are achieved by evaluating the structures in combination, using attributes obtained from applying the MRMR algorithm to all features.

Original languageEnglish
JournalOral Radiology
DOIs
Publication statusAccepted/In press - 2024

Bibliographical note

Publisher Copyright:
© The Author(s) under exclusive licence to Japanese Society for Oral and Maxillofacial Radiology 2024.

Keywords

  • Digital panoramic radiography
  • Forensic science
  • Gender determination
  • Machine learning
  • Mandibular morphometric parameters
  • İmage processing

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