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Aim: Diabetic retinopathy (DR) represents the main cause of vision loss among working age people. A prompt screening of this condition may prevent its worst complications. This study aims to validate the in-built artificial intelligence (AI) algorithm Selena+ of a handheld fundus camera (Optomed Aurora, Optomed, Oulu, Finland) in a first line screening of a real-world clinical setting. Methods: It was an observational cross-sectional study including 256 eyes of 256 consecutive patients. The sample included both diabetic and non-diabetic patients. Each patient received a 50°, macula centered, non-mydriatic fundus photography and, after pupil dilation, a complete fundus examination by an experienced retina specialist. All images were after analyzed by a skilled operator and by the AI algorithm. The results of the three procedures were then compared. Results: The agreement between the operator-based fundus analysis in bio-microscopy and the fundus photographs was of 100%. Among the DR patients the AI algorithm revealed signs of DR in 121 out of 125 subjects (96.8%) and no signs of DR 122 of the 126 non-diabetic patients (96.8%). The sensitivity of the AI algorithm was 96.8% and the specificity 96.8%. The overall concordance coefficient k (95% CI) between AI-based assessment and fundus biomicroscopy was 0.935 (0.891–0.979). Conclusions: The Aurora fundus camera is effective in a first line screening of DR. Its in-built AI software can be considered a reliable tool to automatically identify the presence of signs of DR and therefore employed as a promising resource in large screening campaigns. [ABSTRACT FROM AUTHOR]

The disorganization of retinal inner layers (DRIL) is an optical coherence tomography (OCT) biomarker strictly associated with visual outcomes in patients with diabetic macular edema (DME) whose pathophysiology is still unclear. The aim of this study was to characterize in vivo, using retinal imaging and liquid biopsy, DRIL in eyes with DME. This was an observational cross-sectional study. Patients affected by center-involved DME were enrolled. All patients underwent spectral domain optical coherence tomography (SD-OCT) and proteomic analysis of aqueous humor (AH). The presence of DRIL at OCT was analyzed by two masked retinal experts. Fifty-seven biochemical biomarkers were analyzed from AH samples. Nineteen eyes of nineteen DME patients were enrolled. DRIL was present in 10 patients (52.63%). No statistically significant difference was found between DME eyes with and without DRIL, considering the AH concentration of all the analyzed biomarkers except for glial fibrillary acidic protein (GFAP), a biomarker of Müller cells dysfunction (p = 0.02). In conclusion, DRIL, in DME eyes, seems to strictly depend on a major dysfunction of Müller cells, explaining its role not only as imaging biomarker, but also as visual function Müller cells-related parameter. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate the qualitative and quantitative differences between 20 and 85 kHz A-scan rate optical coherence tomography (OCT) images acquired by spectral domain OCT. The study included 60 healthy subjects analyzed with horizontal linear scans with a variable A-scan rate (SHIFT technology, Heidelberg Engineering, Heidelberg, Germany). The retinal thickness measurement of each retinal layer was performed in three different positions (subfoveal, nasal, and temporal). The qualitative assessment was performed by two independent observers who rated every image with a score ranging from 1 ("sufficient") to 3 ("excellent") on the basis of three parameters: visualization of the vitreo-retinal interface, characterization of the retinal layers, and visualization of the sclero-choroidal interface. No statistically significant differences in terms of retinal layer thickness between the two A-scan rate scans were observed (p > 0.05). The coefficient of variation of the retinal thickness values was lower in the 20 kHz group (25.8% versus 30.1% with the 85 kHz). The 20 kHz images showed a higher quality index for both observers. An inner plexiform layer (IPL) multilaminarity was detected in 78.3% of patients from the 20 kHz group and in 40% of patients from the 85 kHz group (p < 0.05). [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and deep learning (DL)-based systems have gained wide interest in macular disorders, including diabetic macular edema (DME). This paper aims to validate an AI algorithm for identifying and quantifying different major optical coherence tomography (OCT) biomarkers in DME eyes by comparing the algorithm to human expert manual examination. Intraretinal (IRF) and subretinal fluid (SRF) detection and volumes, external limiting-membrane (ELM) and ellipsoid zone (EZ) integrity, and hyperreflective retina foci (HRF) quantification were analyzed. Three-hundred three DME eyes were included. The mean central subfield thickness was 386.5 ± 130.2 µm. IRF was present in all eyes and confirmed by AI software. The agreement (kappa value) (95% confidence interval) for SRF presence and ELM and EZ interruption were 0.831 (0.738–0.924), 0.934 (0.886–0.982), and 0.936 (0.894–0.977), respectively. The accuracy of the automatic quantification of IRF, SRF, ELM, and EZ ranged between 94.7% and 95.7%, while accuracy of quality parameters ranged between 99.0% (OCT layer segmentation) and 100.0% (fovea centering). The Intraclass Correlation Coefficient between clinical and automated HRF count was excellent (0.97). This AI algorithm provides a reliable and reproducible assessment of the most relevant OCT biomarkers in DME. It may allow clinicians to routinely identify and quantify these parameters, offering an objective way of diagnosing and following DME eyes. [ABSTRACT FROM AUTHOR]

In this article, we develop a hypothesis on the role of spectral entropy in conveying a sensation of termination for short musical fragments. We tested the hypothesis in an experiment presenting spectral sequences involving sounds categorised in three families (pitch, bell, or noise) and asked 64 participants to rate their levels of closure in a Likert scale with five degrees. The results highlight an agreement by all participants to consider stimuli transiting from organised (e.g. pitch) to disorganised spectra (e.g. bells, noise) as more complete than transitions from the opposite direction. [ABSTRACT FROM AUTHOR]

This paper reports on an exploratory study in the field of electroacoustic music aimed at understanding whether a sensation similar to that associated with the concept of "cadence" in relation to tonal music can be identified when listening to sounds diffused in space. Using a variety of patterned stimuli in a perceptual experiment, we asked listeners to evaluate the completeness of multiple trajectories on the horizontal plane. The results show differences across multiple categories of listeners, and suggest that listeners acquainted with spatial music consider trajectories more complete when presenting the last two impulses at opposite directions from the centre. [ABSTRACT FROM AUTHOR]

In order to emulate a composer-based approach to musical writing, the paper aims to to create of a new musical synthesis model based on gestalt matrices. The approach is described in three different levels: - a sound model: all instruments have their own interface and have specific roles within a musical composition. Every instrument typology is unique, and composers write for specific instrumental characteristics; - an action model: every performer has different ways to interact with the instrument itself. Each instrument is a powerful sound provider. The action performed on the instrument has the function of "choosing" which particular sound model to play; - a score model: every musical staff has specific functions in the score and represents a certain contribution to the outcome which is related to macro-structure analysis. A self-generating matrix, described by use of patterns, is applied to these basic concepts, thus giving them personal stochastic behaviors and creating a comparison system to reduce data processing. [ABSTRACT FROM AUTHOR]