High-speed widefield handheld swept-source OCT angiography with a VCSEL light source
Bibliographic record
Abstract
Abstract Optical coherence tomography (OCT) and OCT angiography (OCTA) enable noninvasive structural and angiographic imaging of the eye. Portable handheld OCT/OCTA systems are required for imaging patients in the supine position. Examples include infants in the neonatal intensive care unit (NICU) and operating room (OR). The speed of image acquisition plays a pivotal role in acquiring high quality OCT/OCTA images, particularly with the handheld system, since both the operator hand tremor and subject motion can cause significant motion artifacts. In addition, having a large field of view and the ability of real-time data visualization are critical elements in rapid disease screening, reducing imaging time, and detecting peripheral retinal pathologies. The arrangement of optical components is less flexible in the handheld system due to the limitation of size and weight. In this paper, we introduce a 400-kHz, 55-degree field of view handheld OCT/OCTA system that has overcome many technical challenges as a portable OCT system as well as a high-speed OCTA system. We demonstrate imaging premature infants with retinopathy of prematurity (ROP) in the NICU, and patients with incontinentia pigmenti (IP) in the OR using our handheld OCT system. Our design may have potential for improving the diagnosis of retinal diseases and help provide a practical guideline for designing a flexible and portable OCT system.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".