A5051278764- Orthodontics and Dentofacial Orthopedics
- Dental Radiography and Imaging
- Temporomandibular Joint Disorders
- Bone health and osteoporosis research
- Dental Trauma and Treatments
- Injection Molding Process and Properties
- Neural Networks and Applications
- Drug Solubulity and Delivery Systems
- Cleft Lip and Palate Research
- Advanced Neuroimaging Techniques and Applications
- dental development and anomalies
- Craniofacial Disorders and Treatments
- Bone fractures and treatments
- Hip disorders and treatments
- Medical Imaging and Analysis
- Advanced Image Processing Techniques
- 3D Printing in Biomedical Research
- Explainable Artificial Intelligence (XAI)
- Dental Implant Techniques and Outcomes
- Powder Metallurgy Techniques and Materials
- Innovative Microfluidic and Catalytic Techniques Innovation
- Additive Manufacturing and 3D Printing Technologies
- Image and Signal Denoising Methods
- Generative Adversarial Networks and Image Synthesis
Riga Stradiņš University
2020-2025
Riga Technical University
2023-2025
Heriot-Watt University
2022
University of Glasgow
2022
Using microcrystalline cellulose (MCC) with plastic behaviour and calcium phosphate anhydrous (CaHPO4) brittle under compaction is very popular in the pharmaceutical industry for achieving desirable structural–mechanical properties of tablet formulations. Thus, mixtures specific grades MCC CaHPO4 were tested volume proportions 100-0, 75-25, 50-50, 25-75, 0-100 at a constant weight-by-weight concentration sodium stearyl fumarate lubricant, utilizing state-of-the-art benchtop simulator...
Abstract Background/Aim This study aimed to develop a protocol that combines cone‐beam computed tomography (CBCT), software, and 3D printing design replicas for tooth autotransplantation. The goal was evaluate the impact of this approach on extraoral time donor teeth total surgical time, thereby enhancing efficiency outcomes. Materials Methods A non‐randomized trial (protocol 10.1186/ISRCTN13563091 ) conducted at Riga Stradins University, enrolling 46 patients (13–22 years old) who required...
Background and objectives: The need to evaluate the condylar remodeling after orthognathic surgery, using three-dimensional (3D) images volume rendering techniques in skeletal Class III patients has been emphasized. study examined positional, structural, volumetric changes bimaxillary or single-jaw maxillary surgeries cone-beam computed tomography. Materials Methods: Presurgical, postsurgical, one-year post-surgical full field of view (FOV) tomography (CBCT) 44 with deformities were...
Deproteinised bovine bone (DBB) is widely used as substitute in maxillary sinus floor augmentation (MSFA) surgery. No previous studies have shown the long-term volumetric changes augmented when using DBB. The selected patients had MFSA performed a lateral window technique and xenograft, alone or combination with patient’s autologous from mandible. Cone beam computed tomography (CBCT) images were to compare for over period of 6 more years. significant reduction was seen region comparing MSFA...
In this technical note, we examine the capabilities of deep convolutional neural networks (DCNNs) for diagnosing osteoporosis through cone-beam computed tomography (CBCT) scans mandible. The evaluation was conducted using 188 patients’ mandibular CBCT images utilizing DCNN models built on ResNet-101 framework. We adopted a segmented three-phase method to assess osteoporosis. Stage 1 focused bone slice identification, 2 pinpointed coordinates cross-sectional views, and 3 bone’s thickness,...
Deep Learning models are currently the cornerstone of artificial intelligence in medical imaging. The performance imaging is significantly influenced by amount and quality training data. Diffusion have recently attracted attention computer vision community as they enable photorealistic synthetic image-to-image translation. Previous attempts to use diffusion for super-resolution produced satisfactory high-resolution images from low-resolution inputs. However, drawback slow speed inference,...
Deep neural networks are widely used in computer vision for image classification, segmentation and generation. They also often criticised as “black boxes” because their decision-making process is not interpretable by humans. However, learning explainable representations that explicitly disentangle the underlying mechanisms structure observational data still a challenge. To further explore latent space achieve generic processing, we propose pipeline discovering directions of generative...