Browsing by Author "Namatevs, Ivars"

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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.
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    Research of computerization and implementation of the E-prescription for individual pharmacies
    (Rezekne Higher Education Institution, 2011) Ardava, Elita; Onzevs, Oskars; Viksne, Ilmars; Namatevs, Ivars
    The paper deals with establishment, implementation and development of electronic prescription or e-Prescription in context with e-Health solutions. It includes introduction of a numerous innovative solutions, which are to be committed for data information flow, data management and functionality as well as of establishment of a new feasible communication forms between doctors, patients and pharmacists. The aim of the study is to describe some technical aspects and functionality of implementation of e-Prescription system for medical institutions, patients and pharmacies; and, calculation of the total cost of implementation (TCI) for Latvian individual pharmacies. Several expecting financial aspects, which have encompassed evaluation of TCI, calculating payback time, ROI, NPV, and IRR are to be calculated. On the bases of these financial calculations, the primary investment of implementation of e-Prescription for individual pharmacies and initial costs are determined. Impact on individual parts of TCI with the scope to individual pharmacy size, location, existence or absence of formal information strategy has calculated. According to collected data, research paper shows how proposed electronic system is going to implement among Latvian individual pharmacies.
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    Three-Dimensional Imaging in Agriculture: Challenges and Advancements in the Phenotyping of Japanese Quinces in Latvia
    (2023-12-17) Kaufmane, Edīte; Edelmers, Edgars; Sudars, Kaspars; Namatevs, Ivars; Nikulins, Arturs; Strautiņa, Sarmīte; Kalniņa, Ieva; Peter, Astile; Medical Education Technology Centre
    This study presents an innovative approach to fruit measurement using 3D imaging, focusing on Japanese quince (Chaenomeles japonica) cultivated in Latvia. The research consisted of two phases: manual measurements of fruit parameters (length and width) using a calliper and 3D imaging using an algorithm based on k-nearest neighbors (k-NN), the ingeniously designed “Imaginary Square” method, and object projection analysis. Our results revealed discrepancies between manual measurements and 3D imaging data, highlighting challenges in the precision and accuracy of 3D imaging techniques. The study identified two primary constraints: variability in fruit positioning on the scanning platform and difficulties in distinguishing individual fruits in close proximity. These limitations underscore the need for improved algorithmic capabilities to handle diverse spatial orientations and proximities. Our findings emphasize the importance of refining 3D scanning techniques for better reliability and accuracy in agricultural applications. Enhancements in image processing, depth perception algorithms, and machine learning models are crucial for effective implementation in diverse agricultural scenarios. This research not only contributes to the scientific understanding of 3D imaging in horticulture but also underscores its potential and limitations in advancing sustainable and productive farming practices.