 Detecting papilloedema as a marker of raised intracranial pressure using artificial intelligence: A systematic reviewLekaashree Rambabu, Thomas Edmiston, Brandon G Smith, Katharina Kohler, Angelos G Kolias, Richard A I Bethlehem, Pearse A Keane, Hani J Marcus, EyeVu Consortium, Peter J Hutchinson and Tom Bashford PLOS Digital Health, 2025, vol. 4, issue 9, 1-23 Abstract: Automated detection of papilloedema using artificial intelligence (AI) and retinal images acquired through an ophthalmoscope for triage of patients with potential intracranial pathology could prove to be beneficial, particularly in resource-limited settings where access to neuroimaging may be limited. However, a comprehensive overview of the current literature on this field is lacking. We conducted a systematic review on the use of AI for papilloedema detection by searching four databases: Ovid MEDLINE, Embase, Web of Science, and IEEE Xplore. Included studies were assessed for quality of reporting using the Checklist for AI in Medical Imaging and appraised using a novel 5-domain rubric, ‘SMART’, for the presence of bias. For a subset of studies, we also assessed the diagnostic test accuracy using the ‘Metadta’ command on Stata. Nineteen deep learning systems and eight non-deep learning systems were included. The median number of images of normal optic discs used in the training set was 2509 (IQR 580–9156) and in the testing set was 569 (IQR 119–1378). The number of papilloedema images in the training and testing sets was lower with a median of 1292 (IQR 201–2882) in training set and 201 (IQR 57–388) in the testing set. Age and gender were the two most frequently reported demographic data, included by one-third of the studies. Only ten studies performed external validation. The pooled sensitivity and specificity were calculated to be 0.87 [95% CI 0.76-0.93] and 0.90 [95% CI 0.74-0.97], respectively. Though AI model performance values are reported to be high, these results need to be interpreted with caution due highly biased data selection, poor quality of reporting, and limited evidence of reproducibility. Deep learning models show promise in retinal image analysis of papilloedema, however, external validation using large, diverse datasets in a variety of clinical settings is required before it can be considered a tool for triage of intracranial pathologies in resource-limited areas.Author summary: Papilloedema is a condition characterised by the swelling of the optic disc in the eye. It can be caused by increased intracranial pressure. It can be caused by an increase in pressure within the cranial cavity, which may be due to traumatic brain injuries, tumours, or infections. In low-resource settings where access to specialist imaging is limited, identifying papilloedema with accuracy by the bedside using retinal images and artificial intelligence could potentially serve as a tool for triaging when raised intracranial pressure is suspected and urgent surgical or medical intervention may be required. Our systematic review has critically appraised the primary studies which use any form of artificial intelligence to detect papilloedema from images of the retina. We provide a comprehensive overview and an in-depth discussion on the quality of reporting, areas of bias in model design, common limitations, and key findings from the primary literature. Date: 2025 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:plo:pdig00:0000783 DOI: 10.1371/journal.pdig.0000783 Access Statistics for this article More articles in PLOS Digital Health from Public Library of Science |
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