@inbook{a435fd1d739948738ab880eabe7e2000,
title = "Estimation of Cortical Bone Strength Using CNN-based Regression Model",
abstract = "Cortical bone is a compact layer that acts as a protective surface and forms an external layer of all bones. With osteoporosis, imbalance between bone formation and bone loss occurs, and this leads to a deterioration of bone microstructure including cortical bone thinning. Therefore, there is a clinical need to estimate and assess bone strength and quality. The detection of bone cortical thickness is still challenging due to the high variance in the speed of sound in the cortical bone. The main aim of this study is to develop an accurate ultrasound method to estimate cortical bone thickness that could be used as a proxy of bone quality by using CNN-based regression models. To achieve this, pulse-echo measurements are performed at multiple ultrasound frequencies and the continuous wavelet transformations (CWT) of the acquired data was used as an input to the CNN. The maximum observed percentages were in 1 mm and 2 mm with an average error of 5.57%, and the minimum error was in group 7 (7mm) with a percentage of 1.6%. The preliminary results showed that combination of multi-frequency RF signals has potential to be used for cortical thickness estimation.",
keywords = "Bone characterization, Chirp signal, Continuous wavelet transformation, Deep learning, Regression models",
author = "Sultan, {Hossam H.} and Enrico Grisan and Laura Peralta and Sevan Harput",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Ultrasonics Symposium, IUS 2022 ; Conference date: 10-10-2022 Through 13-10-2022",
year = "2022",
doi = "10.1109/IUS54386.2022.9957568",
language = "English",
series = "IEEE International Ultrasonics Symposium, IUS",
publisher = "IEEE Computer Society Press",
booktitle = "IUS 2022 - IEEE International Ultrasonics Symposium",
}