Purpose: To investigate the association between retinal microstructure and cone and rod function in geographic atrophy (GA) secondary to age-related macular degeneration (AMD) by using artificial intelligence (AI) algorithms.
Design: Prospective, observational case series.
Methods: A total of 41 eyes of 41 patients (75.8 ± 8.4 years old; 22 females) from a tertiary referral hospital were included. Mesopic, dark-adapted (DA) cyan and red sensitivities were assessed by using fundus-controlled perimetry ("microperimetry"); and retinal microstructure was assessed by using spectral-domain optical-coherence-tomography (SD-OCT), fundus autofluorescence (FAF), and near-infrared-reflectance (IR) imaging. Layer thicknesses and intensities and FAF and IR intensities were extracted for each test point. The cross-validated mean absolute error (MAE) was evaluated for random forest-based predictions of retinal sensitivity with and without patient-specific training data and percentage of increased mean-squared error (%IncMSE) as measurement of feature importance.
Results: Retinal sensitivity was predicted with a MAE of 4.64 dB for mesopic, 4.89 dB for DA cyan, and 4.40 dB for DA red testing in the absence of patient-specific data. Partial addition of patient-specific sensitivity data to the training sets decreased the MAE to 2.89 dB, 2.86 dB, and 2.77 dB. For all 3 types of testing, the outer nuclear layer thickness constituted the most important predictive feature (35.0, 42.22, and 53.74 %IncMSE). Spatially resolved mapping of "inferred sensitivity" revealed regions with differential degrees of mesopic and DA cyan sensitivity loss outside of the GA lesions.
Conclusions: "Inferred sensitivity" accurately reflected retinal function in patients with GA. Mapping of "inferred sensitivity" could facilitate monitoring of disease progression and serve as "quasi functional" surrogate outcome in clinical trials, especially in consideration of retinal regions beyond areas of GA.
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http://dx.doi.org/10.1016/j.ajo.2020.04.003 | DOI Listing |
We present a novel four-channel optically pumped magnetometer (OPM) for magnetoencephalography that utilizes a two-color pump/probe scheme on a single optical axis. We characterize its performance across 18 built sensor modules. The new sensor implements several improvements over our previously developed sensor including lower vapor-cell operating temperature, improved probe-light detection optics, and reduced optical power requirements.
View Article and Find Full Text PDFWe present a novel four-channel OPM sensor for magnetoencephalography that utilizes a two-color pump/probe scheme on a single optical axis. We characterize its performance across 18 built sensor modules. The new sensor implements several improvements over our previously developed sensor including lower vapor-cell operating temperature, improved probe-light detection optics, and reduced optical power requirements.
View Article and Find Full Text PDFSci Total Environ
June 2022
Department of Civil Engineering, University of Manitoba, Winnipeg, Canada. Electronic address:
Although numerous studies have detected SARS-CoV-2 RNA in wastewater and attempted to find correlations between the concentration of SARS-CoV-2 RNA and the number of cases, no consensus has been reached on sample collection and processing, and data analysis. Moreover, the fate of SARS-CoV-2 in wastewater treatment plants is another issue, specifically regarding the discharge of the virus into environmental settings and the water cycle. The current study monitored SARS-CoV-2 RNA in influent and effluent wastewater samples with three different concentration methods and sludge samples over six months (July to December 2020) to compare different virus concentration methods, assess the fate of SARS-CoV-2 RNA in wastewater treatment plants, and describe the potential relationship between SARS-CoV-2 RNA concentrations in influent and infection dynamics.
View Article and Find Full Text PDFEye (Lond)
August 2021
Department of Ophthalmology, University of Bonn, Bonn, Germany.
Sensitive and robust outcome measures of retinal function are pivotal for clinical trials in age-related macular degeneration (AMD). A recent development is the implementation of artificial intelligence (AI) to infer results of psychophysical examinations based on findings derived from multimodal imaging. We conducted a review of the current literature referenced in PubMed and Web of Science among others with the keywords 'artificial intelligence' and 'machine learning' in combination with 'perimetry', 'best-corrected visual acuity (BCVA)', 'retinal function' and 'age-related macular degeneration'.
View Article and Find Full Text PDFSci Rep
January 2021
Department of Ophthalmology, University of Bonn, Ernst-Abbe-Str. 2, 53127, Bonn, Germany.
Spatially-resolved retinal function can be measured by psychophysical testing like fundus-controlled perimetry (FCP or 'microperimetry'). It may serve as a performance outcome measure in emerging interventional clinical trials for macular diseases as requested by regulatory agencies. As FCP constitute laborious examinations, we have evaluated a machine-learning-based approach to predict spatially-resolved retinal function ('inferred sensitivity') based on microstructural imaging (obtained by spectral domain optical coherence tomography) and patient data in recessive Stargardt disease.
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