Artificial intelligence extension of the OSCAR-IB criteria

Artificial intelligence (AI)-based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological disorders, this includes neurodegeneration and inflammation. Scalable imaging technology for big data in neurology is optical coherence tomography (OCT). We highlight that OCT changes observed in the retina, as a window to the brain, are small, requiring rigorous quality control pipelines. There are existing tools for this purpose. Firstly, there are human-led validated consensus quality control criteria (OSCAR-IB) for OCT. Secondly, these criteria are embedded into OCT reporting guidelines (APOSTEL). The use of the described annotation of failed OCT scans advances machine learning. This is illustrated through the present review of the advantages and disadvantages of AI-based applications to OCT data. The neurological conditions reviewed here for the use of big data include Alzheimer disease, stroke, multiple sclerosis (MS), Parkinson disease, and epilepsy. It is noted that while big data is relevant for AI, ownership is complex. For this reason, we also reached out to involve representatives from patient organizations and the public domain in addition to clinical and research centers. The evidence reviewed can be grouped in a five-point expansion of the OSCAR-IB criteria to embrace AI (OSCAR-AI). The review concludes by specific recommendations on how this can be achieved practically and in compliance with existing guidelines

Development of a broadband dielectric beam splitter with reduced spectral wavefront error

A broadband beam splitter coating with the reflecting range from 400 to 900 nm and the transmitting range from 920 to 2300 nm was developed. To avoid absorptive losses at low wavelengths, Ta₂O₅ was chosen as the high index material. A new setup based on a Hartmann-Shack sensor was built to measure the wavefront error and its spectral dependence. An area of 120 mm diameter was measured. While in a standard coating, large resonant-like wavefront errors occur, an optimized coating with significantly reduced spectral wavefront error was designed and produced

Für den weitesten Weg. Schichten im All: Mittels Galvanik und Sputtern hergestellte Schichten erfüllen extreme Anforderungen

Thin films for space application have to operate under extreme conditions, reworks or replacement of components in general is not possible. The main challenges are extreme temperatures, bombardment by energetic particles (ionizing radiation) and sometimes mechanical stress. The „toolbox“ of deposition processes ranges from Atomic Layer Deposition to low pressure plasma processes to electroplating. Due to its low specific weight carbon fibre reinforced plastic (CFRP) is an often used construction material for space applications. The uniform deposition of copper on CFRP waveguides for the RADAR antennas of the Sentinel satellites has been an impressive demonstration for the generation of functional surfaces on complex shaped 3D substrates by electrochemical processes. Sometimes optimal solutions require hybrid processes. A heat reflector on components for the BepiColombo mission investigating the Hermean environment has been realized by a combination of sputtering and electroplating. Systems in space often have to observe with high precision and therefore need „eagle eyes“. The continuous development of magnetron sputter technology during the past 10 years has enabled the deposition of optical interference systems with hundreds of individual layers and thickness precision in the ± 0,15 % range. As examples a beam splitter and a band pass filter for space missions are presented

Uniformity and wavefront control of optical filters

The present paper addresses uniformity effects in demanding dielectric optical coatings. The origins of spectral resonant wavefront errors (WFE) induced by non-uniformities in complex dielectric filters are investigated in detail. The coating is a broad-band beamsplitter with a high reflectance between 400 and 900 nm and a high transmittance between 920 and 2300nm. The WFE can significantly be reduced with an optimized design. A new setup based on Hartmann-Shack sensors measures the spectrally dependent WFE in the visual spectral range. The paper presents a method for referencing the measured data. The experimental WFE maps obtained by spectral Hartmann-Shack measurements agree well with the expected spectra taken from spectral photometric measurements and the coating design. The paper also addresses coatings on curved surfaces. A band pass filter centered at 670 nm on the convex side of a lens was developed. Using a combination of a sub-rotation and special uniformity masks, a very low spectral shift of the passband position overall the lens surface could be demonstrated. The deposition concept and mask design, respectively, are developed via simulation studies based on a simulation approach shown in [10]. Extension of the model framework by plasma simulation and a concept for computing deposition profiles on moving 3D substrates was required for solving the problem

Migraine induced by hypoxia: an MRI spectroscopy and angiography study

Neuro Imaging Researc

Artificial intelligence extension of the OSCAR-IB criteria

Artificial intelligence (AI)-based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological disorders, this includes neurodegeneration and inflammation. Scalable imaging technology for big data in neurology is optical coherence tomography (OCT). We highlight that OCT changes observed in the retina, as a window to the brain, are small, requiring rigorous quality control pipelines. There are existing tools for this purpose. Firstly, there are human-led validated consensus quality control criteria (OSCAR-IB) for OCT. Secondly, these criteria are embedded into OCT reporting guidelines (APOSTEL). The use of the described annotation of failed OCT scans advances machine learning. This is illustrated through the present review of the advantages and disadvantages of AI-based applications to OCT data. The neurological conditions reviewed here for the use of big data include Alzheimer disease, stroke, multiple sclerosis (MS), Parkinson disease, and epilepsy. It is noted that while big data is relevant for AI, ownership is complex. For this reason, we also reached out to involve representatives from patient organizations and the public domain in addition to clinical and research centers. The evidence reviewed can be grouped in a five-point expansion of the OSCAR-IB criteria to embrace AI (OSCAR-AI). The review concludes by specific recommendations on how this can be achieved practically and in compliance with existing guidelines