System Identification Position Error in Panoramic Radiography: a Review

Authors

  • Nur Nafiiyah Universitas Islam Lamongan
  • Eha Renwi Astuti Universitas Airlangga
  • Ramadhan Hardani Putra Universitas Airlangga
  • Alhidayati Asymal Universitas Airlangga

DOI:

https://doi.org/10.26555/jiteki.v11i1.30598

Keywords:

Position error, Panoramic radiography, Automatic, Identification, Artificial intelligence

Abstract

The professionalism of the radiologist greatly influences the results of radiological images. The quality of panoramic radiography greatly influences accurate clinical diagnosis. The correct patient position is one of the many factors that affect high-quality and accurate panoramic radiography. The process of taking radiographic images causes radiation exposure to the patient, so that when taking radiographic images repeatedly it is very bad for the patient. A review research is needed to reduce radiation exposure by improving the quality of panoramic radiography. This research conducted a literature review by proposing the questions (1) What types of position errors in panoramic radiography have been researched? (2) How is the process of identifying position errors in panoramic radiography that have been researched? The results of the review research showed that the types of position errors in panoramic radiography that have been researched are the head turning, the tongue not sticking to the palate, the chin down, the chin not resting on the support. The process of evaluating position errors in panoramic radiography is mostly done manually, there is only one research that identifies position errors in panoramic radiography automatically using SVM. That there is a great opportunity to create an automatic system for identifying position errors in panoramic radiography to be more precise and time efficient.

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Published

2025-03-14

How to Cite

[1]
N. Nafiiyah, E. R. Astuti, R. H. Putra, and A. Asymal, “System Identification Position Error in Panoramic Radiography: a Review”, J. Ilm. Tek. Elektro Komput. Dan Inform, vol. 11, no. 1, pp. 68–78, Mar. 2025.

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