Eye tracker-based differentiation of causes of acute Vertigo: A mobile approach for faster and more accurate triage and diagnosis
Bibliographic record
Abstract
Background Acute vertigo poses a significant detriment to the quality of life, stemming from a variety of causes that span from peripheral vestibular disorders to severe conditions like stroke. Swiftly identifying the root cause of vertigo is imperative for providing timely and appropriate treatment. Material and Methods Our study aimed to develop an innovative method facilitating the differentiation of vertigo causes through straightforward technical tools. We devised a protocol utilizing an eye-tracker and artificial intelligence, with the goal of accurately pinpointing the origin of vertigo, akin to the clinical neurological and ENT standard examination (HINTS exam). Using Microsoft HoloLens, voice-guided instructions dynamically instruct patients to perform specific eye movements while maintaining a steady head position. During other phases of the examination, patients are directed to move their heads while keeping their gaze fixed. While the analysis is currently executed using the HoloLens, there is future potential for application through a smartphone app. Results The HoloLens enables the identification of eye movement patterns, such as nystagmus and skew deviation. Through our meticulously designed test setup, patients experiencing vertigo can be distinguished from healthy individuals, and differentiation between central and peripheral vertigo is achievable. The development of the eye-tracker was a collaborative effort involving engineers, neurologists, and ENT specialists. Conclusion The eye-tracker exhibits promising potential for advancing the care of vertigo patients. By broadening access to a dependable and rapid analysis of vertigo causes, more individuals at risk of a stroke could be identified and treated within the critical lytic window. Publication History Article published online: 19 April 2024 © 2024. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".