ArticleImaging science in dentistry2020
Comparison of data mining algorithms for sex determination based on mastoid process measurements using cone-beam computed tomography.
Article in Imaging science in dentistry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Sex estimation techniques based on skulls in forensic anthropology: A scoping review.PloS one · 2024Article
- Sex determination from lateral cephalometric radiographs using an automated deep learning convolutional neural network.Imaging science in dentistry · 2022Article
- Cone-Beam Computed Tomography: A New Tool on the Horizon for Forensic Dentistry.International journal of environmental research and public health · 2022Review
- Radiographic Evaluation of Mastoid Parameters for Sexual Differentiation in North Indian Population.Cureus · 2021Article
- [Use of diagnostic modalities for dentofacial imaging in forensic dentistry. Literature review].Revista cientifica odontologica (Universidad Cientifica del Sur)Review
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5 authors.
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Abstract
purposeThe mastoid region is ideal for studying sexual dimorphism due to its anatomical position at the base of the skull. This study aimed to determine sex in the Iranian population based on measurements of the mastoid process using different data mining algorithms. MATERIALS AND
methodsThis retrospective study was conducted on 190 3-dimensional cone-beam computed tomographic (CBCT) images of 105 women and 85 men between the ages of 18 and 70 years. On each CBCT scan, the following 9 landmarks were measured: the distance between the porion and the mastoidale; the mastoid length, height, and width; the distance between the mastoidale and the mastoid incision; the intermastoid distance (IMD); the distance between the lowest point of the mastoid triangle and the most prominent convex surface of the mastoid (MF); the distance between the most prominent convex mastoid point (IMSLD); and the intersecting angle drawn from the most prominent right and left mastoid point (MMCA). Several predictive models were constructed and their accuracy was compared using cross-validation.
resultsThe results of the t-test revealed a statistically significant difference between the sexes in all variables except MF and MMCA. The random forest model, with an accuracy of 97.0%, had the best performance in predicting sex. The IMSLD and IMD made the largest contributions to predicting sex, while the MMCA variable had the least significant role.
conclusionThese results show the possibility of developing an accurate tool using data mining algorithms for sex determination in the forensic framework.
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