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Topological spatial data can be useful for the classification and analysis of biomedical data. Neural networks have been used previously to make diagnostic classifications of corneal disease using summary statistics as network inputs. This approach neglects global shape features (used by clinicians when they make their diagnosis) and produces results that are difficult to interpret clinically. In this study we propose the use of Zernike polynomials to model the global shape of the cornea and use the polynomial coefficients as features for a decision tree classifier. We use this model to classify a sample of normal patients and patients with corneal distortion caused by keratoconus. Extensive experimental results, including a detailed study on enhancing model performance via adaptive boosting and bootstrap aggregation leads us to conclude that the proposed method can be highly accurate and a useful tool for clinicians. Moreover, the resulting model is easy to interpret using visual cues.

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The authors measured the light-distribution patterns and the decay in light output of three mobility lights that visually impaired persons can use for night travel: the Wide-Angle Mobility Light (WAML), the Streamlight, and the Mag-Lite. The WAML had a wide beam with a medium-bright central region. The beam of the Streamlight had the brightest central region and a moderately wide surround of lower illumination. The Mag-Lite had the narrowest light distribution about a bright central spot. Both the Streamlight and the Mag-Lite maintained near-maximum brightness before undergoing rapid decay, while the WAML showed a gradual decay changing from near-maximum brightness to near extinction in almost a linear fashion 40 to 80 minutes after being turned on.