Sagittal pelvic orientation feature analysis for predicting degenerative diseases

Analiza biomehanskih značilnosti ortopedskih pacientov za napovedovanje degenerativnih obolenj je raziskovalno delo na področju medicine oziroma bolj natančno ortopedije. Sagitalno ravnovesje pomeni harmonično obliko hrbtenice in pravilna sagitalna usmeritev medenice je pri zagotavljanju le-tega ključnega pomena. Sagitalno usmeritev medenice določimo na podlagi merjenja biomehanskih oziroma geometrijskih parametrov, s katerimi se nato ugotavlja nepravilnosti, ki lahko povzročijo degenerativna obolenja, kot so na primer hernia diska in spondilolisteza. Ugotavljanje nepravilnosti usmeritve medenice je običajno ročno opravilo in je posledično rezultat relativno subjektivna odločitev. S sodobnimi metodami strojnega učenja je mogoče paciente klasificirati in s tem omogočiti lažjo in bolj natančno odločitev. V delu je najprej predstavljena analiza geometrijskih parametrov, s katerimi se diagnosticira paciente z degenerativnimi obolenji ledvene hrbtenice, nato pa so parametri uporabljeni tudi v različnih napovednih modelih strojnega učenja, s katerimi izvedemo klasifikacijo tovrstnih pacientov. Ugotovili smo, da je najuspešnejši napovedni model strojnega učenja za naše podatke logistična regresija. Z njo dosežemo uspešnost diagnosticiranja hernijskih pacientov – občutljivost: 83.3% (95%CI 72.1 - 96.8%) in specifičnost: 75.0% (95%CI 64.7 - 85.2%), ki je primerljiva uspešnosti kliničnega diagnosticiranja. Uspešnost diagnosticiranja pacientov s spondilolistezo z našim napovednim modelom pa znaša – občutljivost: 96,4% (95%CI 93,6 - 99,1%) in specifičnost: 97.5% (95%CI 94,3 - 1.0%), kar izboljša občutljivost kliničnega diagnosticiranja pacientov s spondilolistezo od 8% do 36%, specifičnost pa do približno 10%.Sagittal pelvic orientation feature analysis for predicting degenerative diseases is a research in the field of medicine or more specifically orthopedics. Sagittal balance means a harmonious shape of the spine and proper sagittal orientation of the pelvis is crucial in ensuring this. Sagittal orientation of the pelvis is determined based on biomechanical or geometric parameters, which are then used to identify abnormalities that can cause degenerative diseases, such as disc herniation and spondylolisthesis. Determining pelvic orientation irregularities is usually a manual task and the result is a relatively subjective decision. With modern machine learning techniques, patients can be classified and thus enable an easier and more accurate decision. The paper first presents an analysis of geometric parameters used to diagnose patients with degenerative diseases of the lumbar spine, and then the parameters are also used in various predictive models, which are used to classify such patients. We found that the most successful predictive machine learning model for our data is logistic regression. It achieves the accurateness of diagnosing hernia – sensitivity: 83.3% (95%CI 72.1 - 96.8%) and specificity: 75.0% (95%CI 64.7 - 85.2%), which is comparable to the accuracy of clinical diagnosis. The accurateness of diagnosing spondylolisthesis with our model is – sensitivity: 96,4% (95% CI 93,6 - 99,1%) and specificity: 97.5% (95%CI 94.3 - 1.0%), which improves the sensitivity of the clinical diagnosis of spondylolisthesis from 8% to 36%, and the specificity to about 10%

Real-time video super-resolution

V magistrskem delu predstavimo globoki model za super-ločljivost videoposnetkov, ki omogoča izboljšanje ločljivosti slike v realnem času. Predlagana arhitektura vključuje tri glavne komponente: 2D konvolucijski modul in transformerski modul za izluščanje prostorskih značilk ter prilagojeno arhitekturo modela BasicVSR za izluščanje časovnih odvisnosti med okvirji videoposnetka. Ključni prispevek dela je vpeljava transformerskega modula v arhitekturo modelov za super-ločljivost videoposnetkov. Uporabili smo tehniko razvijanja za pretvorbo vhodne slike v 1D sekvenco, ki služi kot vhod v transformer. To nam omogoča zajem dolgoročnih odvisnosti znotraj slike, ki so lahko ključne za samo rekonstrukcijo. Rezultati so pokazali, da naš model dosega zadovoljive rezultate v primerjavi s trenutno uveljavljenimi modeli za super-ločljivost videoposnetkov, pri čemer je bil dosežen boljši čas izvajanja. Kljub višji zahtevi po pomnilniku je naš model uspešno izboljšal vizualno kakovost slik v realnem času. Poudarili smo tudi, da visoke vrednosti PSNR in SSIM niso vedno najboljši pokazatelji kakovosti slike, saj je pri oceni rezultatov pomembna tudi vizualna ocena.In this work, we present a deep learning model for video super-resolution that allows real-time video quality enhancement. The proposed architecture includes three main components: 2D convolutional module and transformer module for spatial feature extraction, and customized architecture of the BasicVSR model for extracting temporal dependencies between video frames. The key contribution of this work is the introduction of the transformer module into the architecture of video super-resolution models. We used unfolding technique to convert the input image into a 1D sequence, which serves as input to the transformer. This enables us to capture long-term dependencies within the image, which can be crucial for the reconstruction itself. The results have shown that our model achieves satisfactory results compared to currently established models for video super-resolution, with improved execution time. Despite the higher memory requirement, our model successfully enhances the visual quality of videos in real-time. We also emphasized that high PSNR and SSIM values are not always the best indicators of image quality, as visual evaluation is also important for assessing the results

OligoPrime

With the increasing number of molecular biology techniques, large numbers of oligonucleotides are frequently involved in individual research projects. Thus, a dedicated electronic oligonucleotide management system is expected to provide several benefits such as increased oligonucleotide traceability, facilitated sharing of oligonucleotides between laboratories, and simplified (bulk) ordering of oligonucleotides. Herein, we describe OligoPrime, an information system for oligonucleotide management, which presents a computational support for all steps in an oligonucleotide lifecycle, namely, from its ordering and storage to its application, and disposal. OligoPrime is easy to use since it is accessible via a web browser and does not require any installation from the end user’s perspective. It allows filtering and search of oligonucleotides by various parameters, which include the exact location of an oligonucleotide, its sequence, and availability. The oligonucleotide database behind the system is shared among the researchers working in the same laboratory or research group. Users might have different roles which define the access permissions and range from students to researchers and primary investigators. Furthermore, OligoPrime is easy to manage and install and is based on open-source software solutions. Its code is freely available at https://github.com/OligoPrime. Moreover, an implementation of OligoPrime, which can be used for testing is available at http://oligoprime.xyz/. To our knowledge, OligoPrime is the only software solution dedicated specifically to oligonucleotide management. We strongly believe that it has a large potential to enhance the transparency of use and to simplify the management of oligonucleotides in academic laboratories and research groups