A novel AI‐powered IR‐Visible dual camera system for measuring and tracking ocular surface temperature
Bibliographic record
Abstract
Aims/Purpose: To report the development of a novel artificial intelligence (AI) powered, dual camera (infrared (IR)/visible) system capable of measuring and tracking ocular surface temperature (OST) over any time period. Methods: The system consists of an IR camera (Teledyne FLIR IR A655sc) and a visible (V) camera (FLIR BFS 51S5C‐C), co‐mounted on a slit‐lamp for simultaneous and overlapping fields of view (FOV); designed control algorithms; computer hardware and connecting cables. Novel algorithms are leveraged to synchronize IR and V video streams of exposed ocular surface and adnexa for image and video registration. Localisation of the cornea in V video stream obtained by deep learning (DL) AI algorithms designed for semantic segmentation of the cornea. Coordinates of V video stream segmented cornea are then used to extract corresponding OST data from the IR video stream in each video stream frame. Data analysis algorithms are used to extract OST data from IR video stream. Further image segmentation algorithms isolated specific areas of interest (AOI) for OST analysis. Results: The DL algorithm was trained using V images captured using the V camera. Image registration and segmentation errors were calculated to determine accuracy of system. Mean square error for registration was 5.03 ± 1.82 (c.0.45 mm). Mean Intersection over Union (IoU) was 97.6%, representing accuracy in identifying corneal and scleral pixels in tracked eye segmentation. Analysis software extracted rate of OST change and relative OST change compared to baseline across cornea and selected AOI. Conclusions: A novel AI‐powered system for measuring and tracking OST over time was developed. The system synchronously records IR and V video streams of the eye surface and automatically extracts OST over time. The system can track eye movements and remove artefact eyelid blink frames from the data. A consistent AOI, i.e., pupil, whole cornea, inferior half, superior half, or selected corneal region, can be selected for OST extraction and analysis over time. Experimental results show that the system can track and analyse OST change over time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".