Kaspars Sudars, Ivars Namatevs, Arturs Nikulins, Edgars Edelmers, Laura Neimane, Anda Slaidina, Oskars Radzins. Artificial Intelligence-Powered System for Identifying Bone Deterioration in Radiological Imaging. International Workshop on Embedded Digital Intelligence (IWoEDI'2023), 2023.
Bibtex citation:
Bibtex citation:
@inproceedings{15672_2023,
author = {Kaspars Sudars and Ivars Namatevs and Arturs Nikulins and Edgars Edelmers and Laura Neimane and Anda Slaidina and Oskars Radzins},
title = {Artificial Intelligence-Powered System for Identifying Bone Deterioration in Radiological Imaging},
journal = {International Workshop on Embedded Digital Intelligence (IWoEDI'2023)},
year = {2023}
}
author = {Kaspars Sudars and Ivars Namatevs and Arturs Nikulins and Edgars Edelmers and Laura Neimane and Anda Slaidina and Oskars Radzins},
title = {Artificial Intelligence-Powered System for Identifying Bone Deterioration in Radiological Imaging},
journal = {International Workshop on Embedded Digital Intelligence (IWoEDI'2023)},
year = {2023}
}
Abstract: The aim of this research was to investigate the potential of deep convolutional neural networks (DCNN) for developing a reliable osteoporosis diagnostic tool using conebeam computed tomography (CBCT) scans of the mandible. The study utilized CBCT scans of patients' mandibular bone tissue and incorporated two pre-existing DCNN architectures derived from the ResNet-101 model. Findings from the study suggest that employing transfer learning methodologies can produce satisfactory outcomes in the creation of deep learning models for osteoporosis detection, even when the availability of mandibular CBCT image datasets is restricted.