Ivars Namatevs, Arturs Nikulins, Edgars Edelmers, Laura Neimane, Anda Slaidina, Oskars Radzins, Kaspars Sudars. Modular Neural Networks for Osteoporosis Detection in Mandibular Cone-Beam Computed Tomography Scans. Tomography, 9(5), 14 pp. MDPI, 2023.
Bibtex citation:
Bibtex citation:
@article{15674_2023,
author = {Ivars Namatevs and Arturs Nikulins and Edgars Edelmers and Laura Neimane and Anda Slaidina and Oskars Radzins and Kaspars Sudars},
title = {Modular Neural Networks for Osteoporosis Detection in Mandibular Cone-Beam Computed Tomography Scans},
journal = {Tomography},
volume = {9},
issue = {5},
pages = {14},
publisher = {MDPI},
year = {2023}
}
author = {Ivars Namatevs and Arturs Nikulins and Edgars Edelmers and Laura Neimane and Anda Slaidina and Oskars Radzins and Kaspars Sudars},
title = {Modular Neural Networks for Osteoporosis Detection in Mandibular Cone-Beam Computed Tomography Scans},
journal = {Tomography},
volume = {9},
issue = {5},
pages = {14},
publisher = {MDPI},
year = {2023}
}
Abstract: In this technical note, we examine the capabilities of deep convolutional neural networks (DCNNs) for diagnosing osteoporosis through cone-beam computed tomography (CBCT) scans of the mandible. The evaluation was conducted using 188 patients’ mandibular CBCT images utilizing DCNN models built on the ResNet-101 framework. We adopted a segmented three-phase method to assess osteoporosis. Stage 1 focused on mandibular bone slice identification, Stage 2 pinpointed the coordinates for mandibular bone cross-sectional views, and Stage 3 computed the mandibular bone’s thickness, highlighting osteoporotic variances. The procedure, built using ResNet-101 networks, showcased efficacy in osteoporosis detection using CBCT scans: Stage 1 achieved a remarkable 98.85% training accuracy, Stage 2 minimized L1 loss to a mere 1.02 pixels, and the last stage’s bone thickness computation algorithm reported a mean squared error of 0.8377. These findings underline the significant potential of AI in osteoporosis identification and its promise for enhanced medical care. The compartmentalized method endorses a sturdier DCNN training and heightened model transparency. Moreover, the outcomes illustrate the efficacy of a modular transfer learning method for osteoporosis detection, even when relying on limited mandibular CBCT datasets. The methodology given is accompanied by the source code available on GitLab. © 2023 by the authors.
Quartile: Q2