Diabetic Retinopathy (DR)1, 2 is a leading cause of blindness worldwide and is estimated to threaten the vision of nearly 200 million by 2030.3 To work with the ever-increasing population, the use of image processing algorithms to screen for those at risk has been on the rise. Research-oriented solutions have proven effective in classifying images with or without DR, but often fail to address the true need of the clinic - referring only those who need to be seen by a specialist, and reading every single case. In this work, we leverage an array of image pre-preprocessing techniques, as well as Transfer Learning to re-purpose an existing deep network for our tasks in DR. We train, test, and validate our system on 979 clinical cases, achieving a 95% Area Under the Curve (AUC) for referring Severe DR with an equal error Sensitivity and Specificity of 90%. Our system does not reject any images based on their quality, and is agnostic in terms of eye side and field. These results show that general purpose classifiers can, with the right type of input, have a major impact in clinical environments or for teams lacking access to large volumes of data or high-throughput supercomputers.
Development of multi-mode, high-power, large-aperture two-dimensional VCSEL arrays, operating at a nominal wavelength of 940nm, with highly stable beam profile will be presented. They are designed and fabricated using Trilumina’s proprietary flip-chip-bondable back-side-emitting VCSEL array chip technology. We have demonstrated that a 150-element VCSEL chip array with the improved design shows divergence angle (FWHM) of less than 15°. Additionally, we have integrated this design into drive circuitry that allows us to achieve peak optical powers in excess of 400W.
Flip-chip bonding enables a unique architecture for two-dimensional arrays of VCSELs. Such arrays feature scalable power outputs and the capability to separately address sub-array regions while maintaining fast turn-on and turn-off response times. These substrate-emitting VCSEL arrays can also make use of integrated micro-lenses for beam shaping and directional control. Advances in the performance of these laser arrays will be reviewed and emerging applications are discussed.
Recent investigations by our group have demonstrated that near-infrared spectra collected from lysed blood solutions can be used to create clinically useful partial least squares (PLS) models for pH with standard errors of prediction below 0.05 pH units for a pH range of 1 (6.8 to 7.8). Further work was performed in order to discern the primary source of pH information in the spectra. Results from these experiments are presented using spectral data acquired over the spectral range of 1300 nm to 2500 nm from plasma, lysed blood and amino acids solutions. Data were analyzed by principal component analysis (PCA) and loading vectors were compared. Experiments were designed to eliminate possible correlation between pH and other components in the system in order to ensure variations in the spectral data were due to hydrogen ion changes only. Results indicate that variations in the spectral characteristics of histidine mimic those seen in lysed blood, but not those seen in plasma, suggesting that histidine residues from hemoglobin are providing the necessary variation for pH modeling in the lysed blood solutions.
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