Estimating the Porosity of Nickel-Titanium (NiTi) Implants Using Optical Coherence Tomography and Machine Learning
Yury V. Kistenev
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Tomsk State University, Russian Federation
Paper #9177 received 10 Oct 2024; revised manuscript received 27 Oct 2024; accepted for publication 28 Oct 2024; published online 2 Dec 2024.
DOI: 10.18287/JBPE24.10.040310
Abstract
Nickel-titanium (NiTi) implants take root well in the body and have high biocompatibility with respect to surrounding tissues. Porosity is an important parameter responsible for the biocompatibility of the material, and as a result, it is important to be able to control the material manufacturing process to achieve the required values. Destructive and non-destructive methods are currently used to analyse porosity, of which non-destructive ones are the most preferable. A method of estimating the porosity of NiTi materials using optical coherence tomography and machine learning was developed. The trained support vector machine with a radial basis function kernel classifier using the first and the second order statistics of optical coherence tomography images as a feature vector provided the classification accuracy of 95 ± 6% for two classes of NiTi materials.
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