Smartphone-Based, Rapid, Wide-Field Fundus Photography for Diagnosis of Pediatric Retinal Diseases
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: An important, unmet clinical need is for cost-effective, reliable, easy-to-use, and portable retinal photography to evaluate preventable causes of vision loss in children. This study presents the feasibility of a novel smartphone-based retinal imaging device tailored to imaging the pediatric fundus. METHODS: Several modifications for children were made to our previous device, including a child-friendly 3D printed housing of animals, attention-grabbing targets, enhanced image stitching, and video-recording capabilities. Retinal photographs were obtained in children undergoing routine dilated eye examination. Experienced masked retina-specialist graders determined photograph quality and made diagnoses based on the images, which were compared to the treating clinician's diagnosis. RESULTS: Dilated fundus photographs were acquired in 43 patients with a mean age of 6.7 years. The diagnoses included retinoblastoma, Coats' disease, commotio retinae, and optic nerve hypoplasia, among others. Mean time to acquire five standard photographs totaling 90-degree field of vision was 2.3 ± 1.1 minutes. Patients rated their experience of image acquisition favorably, with a Likert score of 4.6 ± 0.8 out of 5. There was 96% agreement between image-based diagnosis and the treating clinician's diagnosis. CONCLUSIONS: We report a handheld smartphone-based device with modifications tailored for wide-field fundus photography in pediatric patients that can rapidly acquire fundus photos while being well-tolerated. TRANSLATIONAL RELEVANCE: Advances in handheld smartphone-based fundus photography devices decrease the technical barrier for image acquisition in children and may potentially increase access to ophthalmic care in communities with limited resources.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it