Assessing the metabolism of the olfactory circuit by use of F-FDG PET-CT imaging in patients suspected of suffering from Alzheimer's disease or frontotemporal dementia.

Purpose: The loss of olfactory function is known to occur in patients suffering from (behavioral variant) frontotemporal dementia ((bv)FTD) and Alzheimer's disease (AD), although different pathophysiological mechanisms underpin this clinical symptom in both disorders. This study assessed whether brain metabolism of the olfactory circuit as assessed by positron emission tomography (PET) imaging with 2-[fluorine-18]fluoro-2-deoxy-d-glucose ([F]-FDG) can distinguish these entities in different subsets of patients.

Methods: Patients presenting with cognitive decline were included from a prospectively kept database: (1) bvFTD patients, (2) AD patients and (3) patients with logopenic primary progressive aphasia (PPA).

Performance of an artificial intelligence automated system for diabetic eye screening in a large English population.

Aims: A diabetic eye screening programme has huge value in reducing avoidable sight loss by identifying diabetic retinopathy at a stage when it can be treated. Artificial intelligence automated systems can be used for diabetic eye screening but are not employed in the national English Diabetic Eye Screening Programme. The aim was to report the performance of a commercially available deep-learning artificial intelligence software in a large English population.

Simultaneous screening and classification of diabetic retinopathy and age-related macular degeneration based on fundus photos-a prospective analysis of the RetCAD system.

Aim: To assess the accuracy of an artificial intelligence (AI) based software (RetCAD, Thirona, The Netherlands) to identify and grade age-related macular degeneration (AMD) and diabetic retinopathy (DR) simultaneously based on fundus photos.

Methods: This prospective study included 1245 eyes of 630 patients attending an ophthalmology day-care clinic. Fundus photos were acquired and parallel graded by the RetCAD AI software and by an expert reference examiner for image quality, and staging of AMD and DR.

An optimized toolbox for the optogenetic control of intracellular transport.

Cellular functioning relies on active transport of organelles by molecular motors. To explore how intracellular organelle distributions affect cellular functions, several optogenetic approaches enable organelle repositioning through light-inducible recruitment of motors to specific organelles. Nonetheless, robust application of these methods in cellular populations without side effects has remained challenging.

Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks.

We developed a fully automated system using a convolutional neural network (CNN) for total retina segmentation in optical coherence tomography (OCT) that is robust to the presence of severe retinal pathology. A generalized U-net network architecture was introduced to include the large context needed to account for large retinal changes. The proposed algorithm outperformed qualitative and quantitatively two available algorithms.