Abstract: Systems and methods are provided for predicting clinical parameters relating to glaucoma from central visual field patterns. A system includes a processor, an output device, and a computer readable medium that stores executable instructions. The instructions provide a pattern decomposition component that receives a set of visual field data for a patient representing, for each of a plurality of locations in the central region of an eye of the patient, a deviation in sensitivity to a visual stimulus from an age-adjusted normal value and decomposes the set of visual field data into a linear combination of a set of patterns defined via archetypal analysis over a corpus of visual field data to provide a set of decomposition coefficients. A machine learning model determines a clinical parameter for the patient from at least the set of decomposition coefficients, and a user interface provides the determined clinical parameter to a user.

Abstract: Methods and systems providing adaptive assessment of a physical subject to efficiently collect assessment data and modify an assessment schedule based on the analyses. The methods and systems can control the timing of each assessment in order to collect data at times and under conditions that are most informative about the physical subject. Such adaptive methods and systems significantly minimize the frequency of data collection without loss in accuracy or precision and can increase test reliability through reduction in redundancy. The ability to estimate an unknown, underlying function using a small number of free parameters that remain constant regardless of the number of data points being estimated substantially reduces the error of the function estimate. Because estimates of the measurement error are achieved with a minimum of sampled assessments, and with great accuracy, the statistical power of clinical trials, for example, can be greatly increased.

Abstract: Methods and systems providing adaptive assessment of a physical subject to efficiently collect assessment data and modify an assessment schedule based on the analyses. The methods and systems can control the timing of each assessment in order to collect data at times and under conditions that are most informative about the physical subject. Such adaptive methods and systems significantly minimize the frequency of data collection without loss in accuracy or precision and can increase test reliability through reduction in redundancy. The ability to estimate an unknown, underlying function using a small number of free parameters that remain constant regardless of the number of data points being estimated substantially reduces the error of the function estimate. Because estimates of the measurement error are achieved with a minimum of sampled assessments, and with great accuracy, the statistical power of clinical trials, for example, can be greatly increased.

Abstract: In some example implementations, there is provided a method. The method may include determining a probabilistic model representing threshold sensitivities across the visual field, the probabilistic model determined based on the data, the connection strengths, and the noise values. Related systems, methods, and articles of manufacture are also disclosed. In some implementations, the probabilistic model may include a continuous probability distribution.

Abstract: In some example implementations, there is provided a method. The method may include determining a probabilistic model representing threshold sensitivities across the visual field, the probabilistic model determined based on the data, the connection strengths, and the noise values. Related systems, methods, and articles of manufacture are also disclosed. In some implementations, the probabilistic model may include a continuous probability distribution.