| Autor: |
Bertoncini, C. A., Hinders, M. K. |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2/22/2010, Vol. 1211 Issue 1, p1566-1573, 8p, 1 Color Photograph, 1 Diagram, 1 Chart, 3 Graphs |
| Abstrakt: |
Periodontal disease, commonly known as gum disease, affects millions of people. The current method of detecting periodontal pocket depth is painful, invasive, and inaccurate. As an alternative to manual probing, an ultrasonographic periodontal probe is being developed to use ultrasound echo waveforms to measure periodontal pocket depth, which is the main measure of periodontal disease. Wavelet transforms and pattern classification techniques are implemented in artificial intelligence routines that can automatically detect pocket depth. The main pattern classification technique used here, called a binary classification algorithm, compares test objects with only two possible pocket depth measurements at a time and relies on dimensionality reduction for the final determination. This method correctly identifies up to 90% of the ultrasonographic probe measurements within the manual probe’s tolerance. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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