A DEEP LEARNING-BASED TECHNIQUE FOR MEASURING THE SUCCESS OF ORTHODONTIC TREATMENT

Authors

  • Qabas Abdal Zahraa Jabbar Computer Science Department, Collage of Science, Mustansiriyah University, Baghdad, Iraq
  • Firas Ali Hashim Computer Science Department, Collage of Science, Mustansiriyah University, Baghdad, Iraq
  • Nada Thanoon Ahmed Computer Science Department, Collage of Science, Mustansiriyah University, Baghdad, Iraq
  • Nadia Mahmood Hussien Computer Science Department, Collage of Science, Mustansiriyah University, Baghdad,Iraq
  • Yasmin Makki Mohialden Computer Science Department, Collage of Science, Mustansiriyah University, Baghdad,Iraq
  • Itidal Saad Mohammed Computer Science Department, Collage of Science, Mustansiriyah University, Baghdad , Iraq

DOI:

https://doi.org/10.53555/nncse.v9i1.1494

Keywords:

Artificial intelligence deep learning, diagnostics, dental care

Abstract

Orthodontics is one of the most advanced procedures for achieving long-term stability with functional and aesthetically pleasing results. This study aims to evaluate the effectiveness of orthodontics by measuring the distances between each pair of teeth using k-means algorithm utilities from deep learning. The system creates Python-based tools, such as Numpy and OpenCV, from an integrated package. This instrument can assist the dentist in making decisions regarding gum disease, dental impaction, excessive teeth, tooth loss, and orthodontics. Because making an informed decision on an extraction pattern is crucial to the success of orthodontic treatment and the stability of long-term outcomes.

 

References

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Published

2023-01-08

Issue

Vol. 9 No. 1 (2023): Journal of Advance Research in Computer Science & Engineering (ISSN: 2456-3552)

Section

Articles

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How to Cite

Zahraa Jabbar , Q. A. ., Ali Hashim, F. ., Thanoon Ahmed, N. ., Mahmood Hussien, N. ., Makki Mohialden, Y. ., & Saad Mohammed, I. . (2023). A DEEP LEARNING-BASED TECHNIQUE FOR MEASURING THE SUCCESS OF ORTHODONTIC TREATMENT. Journal of Advance Research in Computer Science & Engineering (ISSN 2456-3552), 9(1), 1-4. https://doi.org/10.53555/nncse.v9i1.1494