Chuchalina, M.; Sisojevs, A. and Tatarinov, A.. Determination of Factors of Interest in Bone Models Based on Ultrasonic Data. Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods – ICPRAM, 1(), 281-287 pp. SciTePress, 2024.
Bibtex citāts:
Bibtex citāts:
@inproceedings{16910_2024,
author = {Chuchalina and M.; Sisojevs and A. and Tatarinov and A.},
title = {Determination of Factors of Interest in Bone Models Based on Ultrasonic Data},
journal = {Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods – ICPRAM},
volume = {1},
pages = {281-287},
publisher = {SciTePress},
year = {2024}
}
author = {Chuchalina and M.; Sisojevs and A. and Tatarinov and A.},
title = {Determination of Factors of Interest in Bone Models Based on Ultrasonic Data},
journal = {Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods – ICPRAM},
volume = {1},
pages = {281-287},
publisher = {SciTePress},
year = {2024}
}
Anotācija: Osteoporosis is characterized by increased bone fragility due to a decrease in thickness of the cortical layer CTh and the development of internal porosity in it. The assessment of bone models that simulate the state of osteoporosis causes difficulties due to their complex and multi-layered structure. In the present work, the possibility of using machine learning approaches to determine internal porosity using the ultrasonic data obtained by scanning bone models was researched. The bone models were represented as sets of PMMA plates with gradually varying CTh from 2 to 6 mm. A stepwise progression of porosity from 0 to 100% of CTh was set by increasing the thickness of the porous layer PTh in steps of 1 mm. The evaluation method was based on the results of the supervised multi-class classification of the raw ultrasonic signals and their magnitude of the DFT spectrum with PTh used for labeling. Ultrasonic data was split into training and testing datasets while preserving the percentag e of samples for each class. The results of the experiments demonstrated the potential effectiveness of the PTh classification, while optimization of the datasets and additional signal processing may contribute to the improvement of the results.