Early identification of conditions that can lead to blindness is critical for saving vision. The optical red-reflex test (RRT), which assesses the light reflections from the back of the eye, is a key exam for identifying adverse eye conditions in very young children. However, healthcare workers generally learn the RRT using peer practice and do not have the opportunity to observe abnormal reflexes, especially for rarer conditions, during training. The light reflections also differ in appearance between populations due to different pigmentation levels, so effective training requires practice with a diverse population. We have developed a set of 3D model eyes that aim to accurately mimic the response of eyes with varying pigmentation levels in the RRT, both for healthy eyes and pathologies that can be identified using the RRT. We characterized the optical properties of a set of full-color 3D printing materials (a white scattering material and four transparent colors - cyan, magenta, yellow and black). These properties were used to determine the number of layers, layer thicknesses, and color and scattering material combinations needed to match the reflectance of different fundi, given the constraints of the 3Dprinter. The model eyes can be used as an inexpensive tool for training a wide variety of health professionals to recognize abnormal reflections from the eye and as a reference standard for developing or calibrating eye screening instruments and tools.
KEYWORDS: Eye models, Monte Carlo methods, Eye, Image segmentation, 3D modeling, Optical simulations, Geometrical optics, Data modeling, Cancer, Sclera
There are two gaps in the present approach to breast cancer (BC) screening. First, access to mammography is often linked to socio-economic status, either of the individual or the country providing BC screening. Second, the BC incidence rate among women less than 40 years of age, commonly considered having high risk-benefit ratio for mammographic screening, is currently increasing the fastest of all age groups. Hence, both groups commonly access mammographic screening once they become symptomatic and thus are typically diagnosed with late-stage breast cancer, severely impacting long-term survival and often resulting in increased treatment costs. A safe and inexpensive pre-screening technology, which can identify women at risk of harboring early-stage BC or having very high mammographic breast density, and thus being at an elevated risk to develop BC in the future, can personalize a woman’s entry age into mammographic screening thus optimizing all women’s risk-benefit ratio related to their breast cancer screening. The Optical Breast Spectroscopy (OBS) device developed in our group is a portable device which quantifies the optical density of breast tissue employing up to 13 red/NIR wavelengths. Principal components analysis and tissue chromophore quantification allow identification of women with high mammographic density and hence elevated risk when combined with other risk factors such as BMI and menopausal status. Loss of left-right symmetry in the principal component scores or the tissue chromophores shows potential as an indicator of the presence of BC, although larger population studies are needed to validate the metrics. Longitudinal measurements improve the risk prediction.
Background: In excess of 60% of all cancers are detected in low and middle-income countries, with breast cancer (BC) the dominant malignancy for women. Incidence rates continue to climb, most noticeably in the less than 50-year-old population. Expansion of mammography infrastructure and resources is lacking, resulting in over 60% of women diagnosed with stage III/IV BC in the majority of these countries. Optical Breast Spectroscopy (OBS) was shown to correlate well with mammographic breast density (MBD). OBS could aid breast screening programs in low- and middle-income countries by lowering the number of mammographs required for complete population coverage. However, its performance needs to be tested in large population trails to ensure high sensitivity and acceptable specificity. Methods: For the planned studies in low- and middle-income countries in different continents, online methods need to be implemented to monitor the performance and data collection by these devices, operated by trained nurses. Based on existing datasets, procedures were developed to validate an individual woman's data integrity and to identify operator errors versus system malfunctions. Results: Using a dataset comprising spectra from 360 women collected by 2 instruments in different locations and with 3 different trained operators, automated methods were developed to identify 100% of the source or photodetector malfunctions as well as incorrect calibrations and 96% of instances of insufficient tissue contact. Conclusions: Implementing the dataset validation locally in each instrument and tethered to a cloud database will allow the planned clinical trials to proceed.
A transillumination breast spectroscopy (TiBS) system used for breast cancer risk assessment is being modified to
facilitate large-scale trials and to simply use. A proposed change involves switching from a broadband light source to
several laser sources cycled through in sequence, which will allow for a wavelength-independent detection system. The
effect of the reduction of the spectral content of the system on the ability to predict mammographic density (a known
breast cancer risk factor) from the spectra was assessed. Wavelengths for the laser sources were chosen based on their
contribution to the loading vectors obtained from a principal components analysis of spectra from a study correlating
TiBS spectra with mammographic density. 12 wavelengths were selected based on the principal component loads.
Principal component scores were obtained using both full-spectrum and 12-wavelength-spectrum data. No significant
loss of predictive ability was found when the broadband spectra were reduced to only 12 wavelengths-for both data sets,
3 principal component scores were significantly able to distinguish between high- and low-mammographic density
groups.
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