Effect of Light Intensity on the Relative Afferent Pupillary Defect in Unilateral Neuro-ophthalmic Pathology.

SIGNIFICANCE: Objective pupillometry with standardized light intensities allows a comprehensive assessment of the relative afferent pupillary defect in patients with unilateral neuro-ophthalmic pathology. PURPOSE: This study aimed to determine the impact of varying light intensities on the grade of relative afferent pupillary defect in unilateral neuro-ophthalmic pathology vis-à-vis healthy controls. METHODS: Monocular pupillary light reflexes of 20 controls (14 to 50 years) and 31 cases (12 to 72 years) with clinically diagnosed relative afferent pupillary defect were measured thrice using 1-second-long light pulses, followed by 3 seconds of darkness, at eight light intensities (6.4 to 1200 lux) using objective pupillometry. The relative afferent pupillary defect was quantified as the ratio of the percentage change in the direct light reflexes of the left and right eyes. Its change with light intensity was described using standard exponential fits. RESULTS: The median (25th to 75th interquartile range) defect score of 54.8% cases decreased from baseline values of 1.58 (1.25 to 1.87) for right eye pathology and 0.45 (0.39 to 0.55) for left eye pathology to saturation values of 1.18 (1.05 to 1.31) and 0.98 (0.95 to 1.06), respectively, at light intensities between 56.9 and 300.5 lux. Like controls (1.01 [1.00 to 1.06]), the defect scores of the remaining 45.2% cases were constant with light intensity at 1.23 (1.18 to 1.46) and 0.87 (0.86 to 0.89) for right and left eye pathologies, respectively. CONCLUSIONS: Relative afferent pupillary defects may decrease with test light intensity in a significant proportion of patients with unilateral neuro-ophthalmic pathology. This highlights the importance of objective pupillometry with standardization light intensities for clinical assessment of afferent pupillary defects.

Impact of induced pseudomyopia and refractive fluctuations of accommodative spasm on visual acuity.

CLINICAL RELEVANCE: High-contrast visual acuity is disproportionately poor in patients with accommodative spasm subtype of near reflex (SNR-A), relative to uncorrected refractive errors of equivalent magnitude. This exaggerated loss of performance in SNR-A may be explained by the combination of pseudomyopia and its fluctuations, vis-à-vis, each factor considered separately. BACKGROUND: To determine how combinations of pseudomyopic refraction and its temporal variations in SNR-A impact high-contrast visual acuity by inducing these patterns in healthy cyclopleged adults, relative to their baseline acuity. METHODS: Refractive profiles of 15 patients with SNR-A were obtained from a previous study, averaged, and induced before the right eye of 14 cyclopleged adults (mean ±1 SD age: 22.7 ± 2.6 yrs) by feeding the profile into a coaxially placed, motorised, Badal optometer. LogMAR acuity was measured using the method of constant stimuli: (1) before cycloplegia, (2) after cycloplegia and post-cycloplegia with (3) combination of pseudomyopia and its temporal fluctuations, (4) only pseudomyopia, (5) only temporal fluctuations in refraction about emmetropia, (6) condition 5 with double the amplitude of induced fluctuations and (7) condition 5 with half the amplitude of induced fluctuations. RESULTS: The induced refractive fluctuations ranged from -0.80 to -1.75D, around a mean pseudomyopia of -1.20D. Visual acuity deterioration was maximum for the combination of pseudomyopia and temporal fluctuations condition (0.51 ± 0.07logMAR), followed by only pseudomyopia (0.27 ± 0.05logMAR) and only refractive fluctuations conditions (0.17 ± 0.04logMAR), all relative to baseline post-cycloplegia (0.13 ± 0.04logMAR) (p < 0.001). Visual acuity loss increased with doubling of refractive fluctuations (0.20 ± 0.04logMAR), relative to native fluctuations or halving the amplitude (0.15 ± 0.03logMAR) (p < 0.01). Task precision, as adjudged from the slope of psychometric function, followed a similar pattern of loss as visual acuity. CONCLUSION: Combination of induced pseudomyopia and temporal fluctuations in refraction produces an additive loss of visual acuity and task precision, relative to baseline and each factor considered separately.

Development and evaluation of inexpensive automated deep learning-based imaging systems for embryology.

Embryo assessment and selection is a critical step in an in vitro fertilization (IVF) procedure. Current embryo assessment approaches such as manual microscopy analysis done by embryologists or semi-automated time-lapse imaging systems are highly subjective, time-consuming, or expensive. Availability of cost-effective and easy-to-use hardware and software for embryo image data acquisition and analysis can significantly empower embryologists towards more efficient clinical decisions both in resource-limited and resource-rich settings. Here, we report the development of two inexpensive (<$100 and <$5) and automated imaging platforms that utilize advances in artificial intelligence (AI) for rapid, reliable, and accurate evaluations of embryo morphological qualities. Using a layered learning approach, we have shown that network models pre-trained with high quality embryo image data can be re-trained using data recorded on such low-cost, portable optical systems for embryo assessment and classification when relatively low-resolution image data are used. Using two test sets of 272 and 319 embryo images recorded on the reported stand-alone and smartphone optical systems, we were able to classify embryos based on their cell morphology with >90% accuracy.

An inexpensive smartphone-based device for point-of-care ovulation testing.

The ability to accurately predict ovulation at-home using low-cost point-of-care diagnostics can be of significant help for couples who prefer natural family planning. Detecting ovulation-specific hormones in urine samples and monitoring basal body temperature are the current commonly home-based methods used for ovulation detection; however, these methods, relatively, are expensive for prolonged use and the results are difficult to comprehend. Here, we report a smartphone-based point-of-care device for automated ovulation testing using artificial intelligence (AI) by detecting fern patterns in a small volume (<100 µL) of saliva that is air-dried on a microfluidic device. We evaluated the performance of the device using artificial saliva and human saliva samples and observed that the device showed >99% accuracy in effectively predicting ovulation.