Clinical applications of personalising the neural components of visual image quality metrics for individual eyes

Utilising a visual image quality metric to optimise spectacle prescriptions for eyes with keratoconus

PURPOSE

To compare optical performance, visual performance, and patient-perceived quality of vision with: (1) spectacles determined using subjective refraction and (2) spectacles determined using an objective optimisation method based on wavefront aberration data for eyes with keratoconus.

METHODS

Thirty-seven eyes (20 subjects) with keratoconus underwent both subjective refraction and uncorrected wavefront aberration measurement. Wavefront aberration data were used to objectively identify a sphero-cylindrical refraction that optimised the visual image quality metric visual Strehl ratio (VSX). The two refractions were assembled in trial frames and worn by the subject in a random order. High-contrast visual acuity (VA), letter contrast sensitivity (CS), and the patient's short-term subjective preference were recorded for each prescription.

RESULTS

Median magnitude of the dioptric difference (a measure of similarity between the subjective and objective refractions) was 2.77 D (range = 0.21-20.44 D, first quartile = 1.02 D, third quartile = 4.36 D). Sixty-eight per cent of eyes had better VA with the objective refraction and 32% of eyes gained more than one line of VA. Monocularly, objective refraction was preferred 68% of the time when looking at a distant acuity chart and 76% of the time when viewing a real-world dynamic scene.

CONCLUSIONS

Objective refraction based on visual image quality derived from wavefront aberration data can be valuable in the determination of monocular spectacle refractions for individuals with keratoconus.

Dioptric differences between clinically determined and metric-optimised refractions for adults with Down syndrome

PURPOSE

Refractions based on the optimisation of single-value wavefront-derived metrics may help determine appropriate corrections for individuals with Down syndrome where clinical techniques fall short. This study compared dioptric differences between refractions obtained using standard clinical techniques and two metric-optimised methods: visual Strehl ratio (VSX) and pupil fraction tessellated (PFSt), and investigated characteristics that may contribute to the differences between refraction types.

METHODS

Thirty adults with Down syndrome (age = 29 ± 10 years) participated. Three refractive corrections (VSX, PFSt and clinical) were determined and converted to vector notation (M, J0 , J45 ) to calculate the dioptric difference between pairings of each type using a mixed model repeated measures approach. Linear correlations and multivariable regression were performed to examine the relationship between dioptric differences and the following participant characteristics: higher order root mean square (RMS) for a 4 mm pupil diameter, spherical equivalent refractive error and Vineland Adaptive Behavior Scales (a measure of developmental ability).

RESULTS

The least squares mean estimates (standard error) of the dioptric differences for each pairing were as follows: VSX versus PFSt = 0.51 D (0.11); VSX versus clinical = 1.19 D (0.11) and PFSt versus clinical = 1.04 D (0.11). There was a statistically significant difference in the dioptric differences between the clinical refraction and each of the metric-optimised refractions (p < 0.001). Increased dioptric differences in refraction were correlated with increased higher order RMS (R = 0.64, p < 0.001 [VSX vs. clinical] and R = 0.47, p < 0.001 [PFSt vs. clinical]) as well as increased myopic spherical equivalent refractive error (R = 0.37, p = 0.004 [VSX vs. clinical] and R = 0.51, p < 0.001 [PFSt vs. clinical]).

CONCLUSIONS

The observed differences in refraction demonstrate that a significant portion of the refractive uncertainty is related to increased higher order aberrations and myopic refractive error. Methodology surrounding clinical techniques and metric-optimisation based on wavefront aberrometry may explain the difference in refractive endpoints.

Art Painting Image Classification Based on Neural Network

Neural network (NN) is among the most important and vital form of artificial intelligence which are utilized for the classification of data, information, or images. Moreover, NN has been extensively utilized in various research domains throughout the world, and it is because of overwhelming properties. Painting is a form formed by China's long history and culture, and a large number of paintings reflect the living conditions of China in different periods, which is of great value to the development of China's culture. Image classification has become a key research content in the field of image in the stage of rapid development of information technology, and the content of art painting image classification has also developed rapidly. At present, most traditional image classification methods are formed on the basis of shallow structure learning algorithm, and there are many types of image features that can be extracted, but some features will be lost when extracting, and we need to master the basic painting knowledge. As a result, this extraction process is not general, which explains why traditional Chinese art picture classification is not ubiquitous. The fast development of big data technology and neural network algorithms in recent years has the potential to speed up the categorization of art painting images. As a result, this research investigates the use of neural networks to classify art painting images. The painting image classification method based on artistic style is used to determine the styles of distinct creative works, and the painting image classification algorithm based on saliency is then used to categorize the picture semantics. Finally, a dataset for testing the categorization impact of art painting pictures is developed. The results show that the neural network algorithm can significantly improve the classification effect of art painting images with higher accuracy.