Comparison of mandibular morphometric parameters in digital panoramic radiography in gender determination using machine learning.
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 st...
| Publicado en: | Oral Radiology Vol. 40; no. 3; pp. 415 - 424 |
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| Autores principales: | , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jul2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=178995313&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 178995313 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09116028 1P9 jtl: Oral Radiology issn: 09116028 maglogo: N pubinfo: dt: Jul2024 vid: 40 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 178995313 176624994 178995313 178995313 10.1007/s11282-024-00751-9 178995313 ppf: 415 ppct: 9 formats: tig: atl: Comparison of mandibular morphometric parameters in digital panoramic radiography in gender determination using machine learning. aug: au: Pertek, Hanife Kamaşak, Mustafa Kotan, Soner Hatipoğlu, Fatma Pertek Hatipoğlu, Ömer Köse, Taha Emre affil: https://ror.org/02kswqa67 Center for Nanotechnology & Biomaterials Application and Research (NBUAM), Marmara University, Istanbul, Turkey sug: subj: Radiography, Panoramic Mandible Anatomy and Histology Mandible Radiography Sex Determination Machine Learning Algorithms Image Processing, Computer Assisted Forensic Dentistry Predictive Value of Tests Human Male Female Adult Middle Age Aged Support Vector Machine Decision Trees Discriminant Analysis Neural Networks (Computer) Test-Retest Reliability Mandibular Condyle Anatomy and Histology Prediction Models Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Male Female ab: 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. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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