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Institute
Purpose: The purpose of this thesis is to provide a comprehensive literature review about albinism as an inherited metabolic disorder of melanin synthesis along with those related conditions impacting the visual system. As such, it addresses eye care emphasizing the visual consequences of albinism along with diagnostic and treatment options.
Methods: Background knowledge about ocular development is given as well as information about etiological biochemical and genetic processes. The current classification, clinical findings and their assessment and management options are presented based on recent results of research. In conclusion, two case reports are described as examples of visual care options.
Results: Melanin plays a big role in the retinal and chiasmal development. Melanin biosynthesis can be disrupted by different genes in various ways which leads to the current classification of albinism. Clinical findings include fundus hypopigmenta-tion, nystagmus, iris transillumination, photophobia, foveal hypoplasia, excessive chiasmal decussation, reduced visual acuity, high astigmatism (with-the-rule), strabismus and decreased stereopsis. Treatment options to improve visual acuity, fixation and binocularity are (tinted) prescription lenses and contact lenses, low vision aids, surgical procedures and vision therapy. Medication and supplementa-tion for increased pigmentation are currently being tested on mice.
Conclusions: Albinism is caused by genetic mutations resulting in ocular and cutaneous hypopigmentation. It establishes various phenotypes that require different therapy approaches in order to improve vision and therefore quality of life.
Purpose: Recent studies found a reduction of myopia progression with multifocal contact lenses, however, with yet unclear mechanism. This raises the hypothesis that the addition zones of the multifocal contact lenses induce myopic defocus on the retina, which consequentially leads to choroidal thickening and therefore inhibited eye growth. We tested the effect of the optical design of multifocal contact lenses on choroidal thickness.
Methods: 18 myopic students wore four different contact lenses ((1) single-vision lens corrected for distance, (2) single-vision lens with +2.50 D full-field defocus, (3) “Multifocal center-distance” design, addition +2.50 D, (4) “Multifocal center-near” design, addition +2.50 D) for each 30 minutes on their right eye. Automated analysis of the macular choroidal thickness, vitreous chamber depth and eccentric photorefraction were performed before and after each contact lens.
Results: Choroidal thickness and vitreous chamber depth showed no significant differences to baseline with none of the contact lenses. Choroidal thickness increased the most with the “Multifocal center-distance” and the full-field defocus lens, followed by the “Multifocal center-near” and the single-vision contact lens (+2.1 ± 11.1 μm, +2.0 ± 11.1 μm, +1.6 ± 11.3 μm, +0.9 ± 11.2 μm, respectively). The “Multifocal center-distance” design showed an overall more myopic refractive profile than the other lenses. Changes of vitreous chamber depth occurred in anti-phase to these of choroidal thickness.
Conclusion: Multifocal contact lenses have no significant influence on choroidal thickness and after short-term wear. Therefore, it is assumed that it is not the main contributor to the protective effect of multifocal contact lenses in myopia control.
Based on a data-driven approach, a computer-assisted workflow for the quantitative analysis of optical Kerr microscopy images of sintered FeNdB-type permanent magnets was developed. By analyzing the domain patterns visible in the Kerr image with data-driven approaches such as traditional machine learning and advanced deep learning, we can quantify grain orientation and size with a better trade-off between accuracy and higher throughput than electron backscatter diffraction (EBSD). The key distinction between traditional machine learning and advanced deep learning lies in feature extraction. Traditional methods require manual, user-dependent feature extraction from input data, while advanced deep learning achieves this automatically. The predictions from the trained models were compared to the measurements from EBSD for performance evaluation. The proposed data-driven model is trained on the dataset created from the correlative microscopy technique, which requires the images of grains extracted from the Kerr microscopy and corresponding EBSD grain orientation data (Euler angles). The fine-tuned deep learning model shows better generalization ability than the traditional machine learning models trained on the manually extracted features and resulted in a mean absolute error of less than 5° for grain orientation of the anisotropic magnet samples when evaluated against the measured EBSD values. The developed approach has reduced the measurement effort for grain orientation by 5 times and have sufficient accuracy when compared to the EBSD.
In this study, we investigate the use of artificial neural networks as a potentially efficient method to determine the rate capability of electrodes for lithium-ion batteries with different porosities. The performance of a lithium-ion battery is, to a large extent, determined by the microstructure (i.e., layer thickness and porosity) of its electrodes. Tailoring the microstructure to a specific application is a crucial process in battery development. However, unravelling the complex correlations between microstructure and rate performance using either experiments or simulations is time-consuming and costly. Our approach provides a swift method for predicting the rate capability of battery electrodes by using machine learning on microstructural images of electrode cross-sections. We train multiple models in order to predict the specific capacity based on the batteries’ microstructure and investigate the decisive parts of the microstructure through the use of explainable artificial intelligence (XAI) methods. Our study shows that even comparably small neural network architectures are capable of providing state-of-the-art prediction results. In addition to this, our XAI studies demonstrate that the models are using understandable human features while ignoring present artefacts.