Assessment of pupillary light reflex using a smartphone application

Effects of brightness variations on a smartphone-based version of Radner reading charts

OBJECTIVE

The purpose of this study was to evaluate the equivalence of smartphone-based measurements of near visual acuity under different screen brightness conditions with a standard near visual acuity test.

METHODS

On a sample of 85 participants, we have evaluated near visual acuity with a smartphone-based version of the Radner reading chart at three distinct screen brightness levels. Results have been compared with those obtained with classical Radner paper charts.

RESULTS

We have found that, when a sufficient screen brightness is employed, the smartphone-based version of the Radner reading chart produces results that are in line with the paper Radner charts while low brightness levels lead to a significant underestimation of reading acuities. This result was consistent across different refractive conditions.

CONCLUSIONS

In conclusion, we have shown that handheld devices, such as smartphones, can be potentially exploited for remote measurements of near visual acuity provided a correct control of brightness screen is employed.

Interdevice Agreement between a Smartphone and a Commercial Pupillometer

Background

The reliability of dynamic pupillometry parameters varies from one pupillometer to another, making it difficult to standardize the values for any particular device. Hence, further studies are required to evaluate the agreement of various pupillometer devices and explore their utility in routine clinical settings.

Aim

This study sought to evaluate the agreement between smartphone and commercial pupillometer measurements in routine clinical settings.

Methods

The study included pupillary measurements obtained by a single investigator from 100 healthy participants (200 eyes) with each pupillometer. Pupillary measurements taken by a smartphone pupillometry application (reflex pupillary light reflex analyzer by Brightlamp [Indianapolis, IN, USA]) were compared with a commercial pupillometer (neurological pupil index-200, NeurOptics Inc., Irvine, USA).

Results

The comparison of descriptive statistics revealed a statistically significant difference between the smartphone and commercial pupillometers for various parameters, including maximum diameter, minimum diameter, constriction velocity (CV), maximum CV, and dilatation velocity (P < 0.05), except for latency (P = 0.36). The intraclass correlation coefficient revealed poor agreement between the two devices (<0.50).

Conclusion

The measurements by smartphone pupillometry application were found to be unreliable, indicating that they may not be an ideal substitute for commercial pupillometers in their present form in the Indian population. Further studies with larger sample size as well as improvements in the processing and interpretation of the measurements by the software, are needed to determine its utility in routine clinical settings.

The Internet, Apps, and the Anesthesiologist

Modern anesthesia continues to be impacted in new and unforeseen ways by digital technology. Combining portability and versatility, mobile applications or "apps" provide a multitude of ways to enhance anesthetic and peri-operative care. Research suggests that the uptake of apps into anesthetic practice is becoming increasingly routine, especially amongst younger anesthetists brought up in the digital age. Despite this enthusiasm, there remains no consensus on how apps are safely and efficiently integrated into anesthetic practice. This review summarizes the most popular forms of app usage in anesthesia currently and explores the challenges and opportunities inherent in implementing app use in anesthesia, with an emphasis on a practical approach for the modern anesthetist.

Validation of a Smartphone Pupillometry Application in Diagnosing Severe Traumatic Brain Injury

The pupillary light reflex (PLR) is an important biomarker for the detection and management of traumatic brain injury (TBI). We investigated the performance of PupilScreen, a smartphone-based pupillometry app, in classifying healthy control subjects and subjects with severe TBI in comparison to the current gold standard NeurOptics pupillometer (NPi-200 model with proprietary Neurological Pupil Index [NPi] TBI severity score). A total of 230 PLR video recordings taken using both the PupilScreen smartphone pupillometer and NeurOptics handheld device (NPi-200) pupillometer were collected from 33 subjects with severe TBI (sTBI) and 132 subjects who were healthy without self-reported neurological disease. Severe TBI status was determined by Glasgow Coma Scale (GCS) at the time of recording. The proprietary NPi score was collected from the NPi-200 pupillometer for each subject. Seven PLR curve morphological parameters were collected from the PupilScreen app for each subject. A comparison via t-test and via binary classification algorithm performance using NPi scores from the NPi-200 and PLR parameter data from the PupilScreen app was completed. This was used to determine how the frequently used NPi-200 proprietary NPi TBI severity score compares to the PupilScreen app in ability to distinguish between healthy and sTBI subjects. Binary classification models for this task were trained for the diagnosis of healthy or severe TBI using logistic regression, k-nearest neighbors, support vector machine, and random forest machine learning classification models. Overall classification accuracy, sensitivity, specificity, area under the curve, and F1 score values were calculated. Median GCS was 15 for the healthy cohort and 6 (interquartile range 2) for the severe TBI cohort. Smartphone app PLR parameters as well as NPi from the digital infrared pupillometer were significantly different between healthy and severe TBI cohorts; 33% of the study cohort had dark eye colors defined as brown eyes of varying shades. Across all classification models, the top performing PLR parameter combination for classifying subjects as healthy or sTBI for PupilScreen was maximum diameter, constriction velocity, maximum constriction velocity, and dilation velocity with accuracy, sensitivity, specificity, area under the curve (AUC), and F1 score of 87%, 85.9%, 88%, 0.869, and 0.85, respectively, in a random forest model. The proprietary NPi TBI severity score demonstrated greatest AUC value, F1 score, and sensitivity of 0.648, 0.567, and 50.9% respectively using a random forest classifier and greatest overall accuracy and specificity of 67.4% and 92.4% using a logistic regression model in the same classification task on the same dataset. The PupilScreen smartphone pupillometry app demonstrated binary healthy versus severe TBI classification ability greater than that of the NPi-200 proprietary NPi TBI severity score. These results may indicate the potential benefit of future study of this PupilScreen smartphone pupillometry application in comparison to the NPi-200 digital infrared pupillometer across the broader TBI spectrum, as well as in other neurological diseases.

A haploscope based binocular pupillometer system to quantify the dynamics of direct and consensual Pupillary Light Reflex

This study described the development of a haploscope-based pupillometer for the parametrization of the Pupillary Light Reflex (PLR), and its feasibility in a set of 30 healthy subjects (light or dark-colored irides) and five patients diagnosed with Relative Afferent Pupillary Defect (RAPD). Our supplementary aim focused on evaluating the influence of iris colour on the PLR to decide whether a difference in PLR parameters should be anticipated when this system is used across ethnicities. All the participants underwent a customized pupillometry protocol and the generated pupil traces, captured by an eye tracker, were analyzed using exponential fits to derive PLR parameters. A Pupil Response Symmetry (PRS) coefficient was calculated to predict the presence of RAPD. The mean (SD) Initial PD during dilation (3.2 (0.5) mm) and the minimum PD during constriction (2.9 (0.4) mm) in the light iris group had a statistically significant (p < 0.001) higher magnitude compared to the dark iris group. The normal limits of the PRS coefficient ranged from - 0.20 to + 1.07 and all RAPD patients were outside the calculated normal limits. This proposed system, analysis strategies, and the tested metrics showed good short-term repeatability and the potential in detecting pupil abnormalities in neuro-ophthalmic diseases.