Modular Neural Networks for Osteoporosis Detection in Mandibular Cone-Beam Computed Tomography Scans
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Date
2023-10
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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.
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Publisher Copyright: © 2023 by the authors.
Keywords
artificial intelligence, CBCT, convolutional neural network, dentistry, osteoporosis, deep learning, 3.2 Clinical medicine, 2.6 Medical engineering, 1.1. Scientific article indexed in Web of Science and/or Scopus database
Citation
Namatevs, I, Nikulins, A, Edelmers, E, Neimane, L, Slaidiņa, A, Radziņš, O & Sudars, K 2023, 'Modular Neural Networks for Osteoporosis Detection in Mandibular Cone-Beam Computed Tomography Scans', Tomography (Ann Arbor, Mich.), vol. 9, no. 5, pp. 1772-1786. https://doi.org/10.3390/tomography9050141