Smartphone Ocular Fundal Photography in the Diagnosis of Raised Intracranial Pressure: A Novel Adaptation to Neurosurgical Practice
Bibliographic record
Abstract
Background Consistently raised intracranial pressure (ICP) is a common final pathway to morbidity/mortality in many neurosurgical conditions. This underscores the need for early diagnosis and prompt management of raised ICP. This study aims to determine whether smartphone fundal photography features of raised ICP can accurately predict the computed tomography (CT) findings suggestive of elevated ICP in neurosurgery patients. Methods Dilated ocular fundal photography examinations using an ophthalmoscope adapter mounted on a smartphone were done on 82 patients with clinical suspicion of raised ICP. Fundal photography findings were recorded as pictures/videos for disc analysis. Patients subsequently had neuroimaging with results analyzed for radiological features of raised ICP. These were correlated with fundal photography findings. Results A total of 82 adult patients participated in this study. Chi-square analysis showed a relationship between radiological signs of raised ICP and the absence of spontaneous retinal venous pulsation (SRVP) (p=0.001). There was no relationship observed between papilledema and radiological signs of raised ICP. However, when the fundal photography signs were aggregated, there was a significant relationship between the fundal signs of raised ICP and radiological signs of raised ICP (p=0.004). The sensitivity and specificity of smartphone-fundoscopy-detected papilledema in predicting radiological signs of raised ICP were 43.2% and 100%, respectively, while those of absent SRVP were 100% and 92.6%, respectively. Conclusion Smartphone ophthalmoscopy is a reliable screening tool for evaluating ICP in neurosurgical patients. It should be introduced into the neurosurgeon's tools for prompt evaluation of raised ICP, especially in developing/resource-poor settings where CT or magnetic resonance imaging is not readily available.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".