Please use this identifier to cite or link to this item:
10.3390/tomography9050141
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Namatevs, Ivars | - |
dc.contributor.author | Nikulins, Arturs | - |
dc.contributor.author | Edelmers, Edgars | - |
dc.contributor.author | Neimane, Laura | - |
dc.contributor.author | Slaidiņa, Anda | - |
dc.contributor.author | Radziņš, Oskars | - |
dc.contributor.author | Sudars, Kaspars | - |
dc.date.accessioned | 2023-10-09T12:25:04Z | - |
dc.date.available | 2023-10-09T12:25:04Z | - |
dc.date.issued | 2023-10 | - |
dc.identifier.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 | - |
dc.identifier.issn | 2379-1381 | - |
dc.identifier.uri | https://dspace.rsu.lv/jspui/handle/123456789/14912 | - |
dc.description | Publisher Copyright: © 2023 by the authors. | - |
dc.description.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. | en |
dc.format.extent | 15 | - |
dc.format.extent | 5640463 | - |
dc.language.iso | eng | - |
dc.relation.ispartof | Tomography (Ann Arbor, Mich.) | - |
dc.rights | info:eu-repo/semantics/openAccess | - |
dc.subject | artificial intelligence | - |
dc.subject | CBCT | - |
dc.subject | convolutional neural network | - |
dc.subject | dentistry | - |
dc.subject | osteoporosis | - |
dc.subject | deep learning | - |
dc.subject | 3.2 Clinical medicine | - |
dc.subject | 2.6 Medical engineering | - |
dc.subject | 1.1. Scientific article indexed in Web of Science and/or Scopus database | - |
dc.title | Modular Neural Networks for Osteoporosis Detection in Mandibular Cone-Beam Computed Tomography Scans | en |
dc.type | /dk/atira/pure/researchoutput/researchoutputtypes/contributiontojournal/article | - |
dc.identifier.doi | 10.3390/tomography9050141 | - |
dc.contributor.institution | Department of Morphology | - |
dc.contributor.institution | Institute of Anatomy and Anthropology | - |
dc.contributor.institution | Department of Conservative Dentistry and Oral Health | - |
dc.contributor.institution | Department of Prosthetic Dentistry | - |
dc.contributor.institution | Department of Orthodontics | - |
dc.identifier.url | https://www-webofscience-com.db.rsu.lv/wos/alldb/full-record/MEDLINE:37888733 | - |
dc.identifier.url | http://www.scopus.com/inward/record.url?scp=85175038751&partnerID=8YFLogxK | - |
dc.description.status | Peer reviewed | - |
Appears in Collections: | Research outputs from Pure / Zinātniskās darbības rezultāti no ZDIS Pure |
Files in This Item:
File | Size | Format | |
---|---|---|---|
Modular_Neural_Networks_for_Osteoporosis_Detection.pdf | 5.51 MB | Adobe PDF | View/Open![]() |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.