Automated Process to assist comparative identification using Panoramic Radiographs in Dentistry
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Keywords

Identification, Forensic Dentistry, Data Science, Machine Learning.

How to Cite

Ortiz, A. G., Pereira, C. F. S., Faria, C. A. de, Azevedo, C. L., Biazevic, M. G. H., & Michel Crosato, E. (2024). Automated Process to assist comparative identification using Panoramic Radiographs in Dentistry: Processo automatizado para auxiliar na identificação comparativa. Brazilian Journal of Implantology and Health Sciences, 6(2), 1374–1378. https://doi.org/10.36557/2674-8169.2024v6n2p1374-1378

Abstract

Introduction: Comparative Identification through Panoramic Radiographs in Dentistry is a forensic investigation method that uses radiographic images to compare dental characteristics between different individuals. Objective: To propose a simplified automation process to assist in the comparative identification of patients in dentistry. Development: Image segmentation techniques, feature extraction, and machine learning were performed. The programming language used was Python 3.10, utilizing the Google Colab application, version 3.7.13. Additional language packages included Numpy, Matplotlib, and CV2. The project was submitted to the Ethics Committee and was approved with the protocol number 79354517000000075. The proposed program displays the main image and comparison images, showing the degree of similarity. The images are presented side by side, and identical colorations indicate similar locations. Final Considerations: The application demonstrates consistent potential for automation to optimize the workflow of the Forensic Dentist and enhance the quality of judicial expertise in Dentistry.

https://doi.org/10.36557/2674-8169.2024v6n2p1374-1378
PDF (Português (Brasil))

References

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Ortiz AG, Soares GH, da Rosa GC, Biazevic MG, Michel-Crosato E. A pilot study of an automated personal identification process: Applying machine learning to panoramic radiographs. Imaging Sci Dent. 2021 Jun;51(2):187-193. Doi: 10.5624/isd.20200324.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright (c) 2024 Adrielly Garcia Ortiz, Carlos Felipe S. Pereira, Camila Alves de Faria, Camila Lindoni Azevedo, Maria Gabriela Haye Biazevic, Edgard Michel Crosato