Browsing by Author "Radziņš, Oskars"

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    Evaluation of condylar positional, structural, and volumetric status in class III orthognathic surgery patients
    (2020-12-06) Podčernina, Jevgenija; Urtāne, Ilga; Pirttiniemi, Pertti; Šalms, Ģirts; Radziņš, Oskars; Aleksejūnienė, Jolanta; Rīga Stradiņš University; Department of Orthodontics; Department of Oral and Maxillofacial Surgery and Oral Medicine
    Background and objectives: The need to evaluate the condylar remodeling after orthognathic surgery, using three-dimensional (3D) images and volume rendering techniques in skeletal Class III patients has been emphasized. The study examined condylar positional, structural, and volumetric changes after bimaxillary or single-jaw maxillary orthognathic surgeries in skeletal Class III patients using the cone-beam computed tomography. Materials and Methods: Presurgical, postsurgical, and one-year post-surgical full field of view (FOV) cone-beam computed tomography (CBCT) images of 44 patients with skeletal Class III deformities were obtained. Group 1 underwent a bimaxillary surgery (28 patients: 24 females and 4 males), with mean age at the time of surgery being 23.8 ± 6.0 years, and Group 2 underwent maxillary single-jaw surgery (16 patients: 8 females and 8 males), with mean age at the time of surgery being 23.7 ± 5.1 years. After the orthognathic surgery, the CBCT images of 88 condyles were evaluated to assess their displacement and radiological signs of bone degeneration. Three-dimensional (3D) condylar models were constructed and superimposed pre-and postoperatively to compare changes in condylar volume. Results: Condylar position was found to be immediately altered after surgery in the maxillary single-jaw surgery group, but at the one-year follow-up, the condyles returned to their pre-surgical position. There was no significant difference in condylar position when comparing between pre-surgery and one-year follow-up in any of the study groups. Condylar rotations in the axial and coronal planes were significant in the bimaxillary surgery group. No radiological signs of condylar bone degeneration were detected one year after the surgery. Changes in condylar volume after surgery were found to be insignificant in both study groups. Conclusions: At one year after orthognathic surgery, there were no significant changes in positional, structural, or volumetric statuses of condyles.
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    Long-Term Volumetric Stability of Maxillary Sinus Floor Augmentation Using a Xenograft Bone Substitute and Its Combination with Autologous Bone : A 6+ Year Retrospective Follow-Up Study Using Cone Beam Computed Tomography
    (2024-05) Zamure-Damberga, Liene; Radziņš, Oskars; Šalms, Ģirts; Zolovs, Maksims; Bokvalde, Zanda; Neimane, Laura; Department of Conservative Dentistry and Oral Health; Department of Oral and Maxillofacial Surgery and Oral Medicine; Statistics Unit
    Deproteinised bovine bone (DBB) is widely used as bone substitute in maxillary sinus floor augmentation (MSFA) surgery. No previous studies have shown the long-term volumetric changes in the augmented bone when using DBB. The selected patients had MFSA performed using a lateral window technique and a xenograft, alone or in combination with the patient’s autologous bone from the mandible. Cone beam computed tomography (CBCT) images were used to compare the volumetric changes in the augmented bone for patients over a period of 6 or more years. No significant bone reduction was seen in the augmented bone region when comparing MSFA after 7 months and 6 or more years after dental implantation.
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    Modular Neural Networks for Osteoporosis Detection in Mandibular Cone-Beam Computed Tomography Scans
    (2023-10) Namatevs, Ivars; Nikulins, Arturs; Edelmers, Edgars; Neimane, Laura; Slaidiņa, Anda; Radziņš, Oskars; Sudars, Kaspars; Department of Morphology; Institute of Anatomy and Anthropology; Department of Conservative Dentistry and Oral Health; Department of Prosthetic Dentistry; Department of Orthodontics
    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.