Pupillary Assessment for the Detection of Ischemic Stroke: Analysis of Multiform Pupil Shapes Using Bexel Irina Softwaredaprof
Ischemic strokes, which occur when blood flow to the brain is obstructed, account for approximately 87% of all strokes and potential long-term disability. Prompt diagnosis and treatment of ischemic stroke are crucial for patient outcomes, and accurate and accessible diagnostic methods are needed for effective management of this condition.
Ischemic Stroke is ranked as the second leading cause of death worldwide with an annual mortality rate of about 5.5 million. Three types of ischemic strokes include Ischemic stroke, hemorrhagic stroke and Transient ischemic attack (TIA).
The World Stroke Organization reports that there are more than 12.2 million new strokes annually worldwide, and one in four individuals over the age of 25 will experience a stroke during their lifetime.
Pupil assessment has been proposed as a potential diagnostic tool for ischemic stroke, as changes in pupil size, shape, and response to light can indicate neurological impairment.
The aim of this study is to investigate the relationship between cerebral ischemic stroke and oval ellipse forms of the human pupil, and to assess the accuracy and reliability of pupillary assessment as a possible diagnostic tool for this condition.
The study concludes that pupillary abnormalities could potentially be used as an early indicator of ischemic cerebral strokes and that further research in this area is needed. The researcher also suggests that the development of new pupillary assessment software with machine learning techniques could lead to higher accuracy in detecting pupillary abnormalities and potentially identifying new unknown pupillary signs of diagnostic significance.
Overall, this study highlights the potential value of pupillary assessment in the diagnosis and management of neurological conditions, particularly in resource-limited settings where advanced imaging techniques may not be readily available in underdeveloped countries.
Author: Bryan K. Marcia, Ph.D.
Published in:: https://www.researchgate.net/publication