Namatevs, IvarsNikulins, ArtursEdelmers, EdgarsNeimane, LauraSlaidiņa, AndaRadziņš, OskarsSudars, Kaspars2023-10-092023-10-092023-10Namatevs, 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/tomography90501412379-1381https://dspace.rsu.lv/jspui/handle/123456789/14912Publisher Copyright: © 2023 by the authors.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.155640463enginfo:eu-repo/semantics/openAccessartificial intelligenceCBCTconvolutional neural networkdentistryosteoporosisdeep learning3.2 Clinical medicine2.6 Medical engineering1.1. Scientific article indexed in Web of Science and/or Scopus database/dk/atira/pure/researchoutput/researchoutputtypes/contributiontojournal/article10.3390/tomography9050141https://www-webofscience-com.db.rsu.lv/wos/alldb/full-record/MEDLINE:37888733http://www.scopus.com/inward/record.url?scp=85175038751&partnerID=8YFLogxK