Search Results (4)

Background: Quantitative pupillometry has been proposed as an objective means to diagnose acute sports-related concussion (SRC). Objective: To assess the diagnostic accuracy of a smartphone-based quantitative pupillometer in the acute diagnosis of SRC. Methods: Division I college football players had baseline pupillometry including pupillary light reflex (PLR) parameters of maximum resting diameter, minimum diameter after light stimulus, percent change in pupil diameter, latency of pupil constriction onset, mean constriction velocity, maximum constriction velocity, and mean dilation velocity using a smartphone-based app. When an SRC occurred, athletes had the smartphone pupillometry repeated as part of their concussion testing. All combinations of the seven PLR parameters were tested in machine learning binary classification models to determine the optimal combination for differentiating between non-concussed and concussed athletes. Results: 93 football athletes underwent baseline pupillometry testing. Among these athletes, 11 suffered future SRC and had pupillometry recordings repeated at the time of diagnosis. In the machine learning pupillometry analysis that used the synthetic minority oversampling technique to account for the significant class imbalance in our dataset, the best-performing model was a random forest algorithm with the combination of latency, maximum diameter, minimum diameter, mean constriction velocity, and maximum constriction velocity PLR parameters as feature inputs. This model produced 91% overall accuracy, 98% sensitivity, 84.2% specificity, area under the curve (AUC) of 0.91, and an F1 score of 91.6% in differentiating between baseline and SRC recordings. In the machine learning analysis prior to oversampling of our imbalanced dataset, the best-performing model was k-nearest neighbors using latency, maximum diameter, maximum constriction velocity, and mean dilation velocity to produce 82% accuracy, 40% sensitivity, 87% specificity, AUC of 0.64, and F1 score of 24%. Conclusions: Smartphone pupillometry in combination with machine learning may provide fast and objective SRC diagnosis in football athletes. Full article

Chromatic Pupillometry, used to assess Pupil Light Reflex (PLR) to a coloured light stimulus, has regained interest since the discovery of melanopsin in the intrinsically photosensitive Retinal Ganglion Cells (ipRGCs). This technique has shown the potential to be used as a screening tool for neuro-ophthalmological diseases; however, most of the pupillometers available are expensive and not portable, making it harder for them to be used as a widespread screening tool. In this study, we developed a smartphone-based system for chromatic pupillometry that allows targeted stimulation of the ipRGCs. Using a smartphone, this system is portable and accessible and takes advantage of the location of the ipRGCs in the perifovea. The system incorporates a 3D-printed support for the smartphone and an illumination system. Preliminary tests were carried out on a single individual and then validated on eleven healthy individuals with two different LED intensities. The average Post-Illumination Pupil Light Response 6 s after the stimuli offsets (PIPR-6s) showed a difference between the blue and the red stimuli of 9.5% for both intensities, which aligns with the studies using full-field stimulators. The results validated this system for a targeted stimulation of the ipRGCs for chromatic pupillometry, with the potential to be a portable and accessible screening tool for neuro-ophthalmological diseases. Full article

Pupillometry, an integral component of neurological examination, serves to evaluate both pupil size and reactivity. The conventional manual assessment exhibits inherent limitations, thereby necessitating the development of portable automated infrared pupillometers (PAIPs). Leveraging infrared technology, these devices provide an objective assessment, proving valuable in the context of brain injury for the detection of neuro-worsening and the facilitation of patient monitoring. In cases of mild brain trauma particularly, traditional methods face constraints. Conversely, in severe brain trauma scenarios, PAIPs contribute to neuro-prognostication and non-invasive neuromonitoring. Parameters derived from PAIPs exhibit correlations with changes in intracranial pressure. It is important to acknowledge, however, that PAIPs cannot replace invasive intracranial pressure monitoring while their widespread adoption awaits robust support from clinical studies. Ongoing research endeavors delve into the role of PAIPs in managing critical neuro-worsening in brain trauma patients, underscoring the non-invasive monitoring advantages while emphasizing the imperative for further clinical validation. Future advancements in this domain encompass sophisticated pupillary assessment tools and the integration of smartphone applications, emblematic of a continually evolving landscape. Full article

Pupillary light reflex (PLR) assessment is a crucial examination for evaluating brainstem function, particularly in patients with acute brain injury and neurosurgical conditions. The PLR is controlled by neural pathways modulated by both the sympathetic and parasympathetic nervous systems. Altered PLR is a strong predictor of adverse outcomes after traumatic and ischemic brain injuries. However, the assessment of PLR needs to take many factors into account since it can be modulated by various medications, alcohol consumption, and neurodegenerative diseases. The development of devices capable of measuring pupil size and assessing PLR quantitatively has revolutionized the non-invasive neurological examination. Automated pupillometry, which is more accurate and precise, is widely used in diverse clinical situations. This review presents our current understanding of the anatomical and physiological basis of the PLR and the application of automated pupillometry in managing neurocritical patients. We also discuss new technologies that are being developed, such as smartphone-based pupillometry devices, which are particularly beneficial in low-resource settings. Full article