Detecting papilloedema as a marker of raised intracranial pressure using artificial intelligence: a systematic review

Introduction 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.

We included nineteen deep learning systems and eight non-deep learning systems. The median number of images of normal optic discs used in the training set was 2509 (IQR 580 to 9156) and in the testing set was 569 (IQR 119 to 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, 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 Papilledema 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 pressures 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 papilledema 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. Competing Interest Statement PK has acted as a consultant for Retina Consultants of America, Topcon, Roche, Boehringer-Ingleheim, and Bitfount and is an equity owner in Big Picture Medical. He has received speaker fees from Zeiss, Novartis, Gyroscope, Boehringer-Ingleheim, Apellis, Roche, Abbvie, Topcon, and Hakim Group. He has received travel support from Bayer, Topcon, and Roche. He has attended advisory boards for Topcon, Bayer, Boehringer-Ingleheim, RetinAI, and Novartis. RB holds shares and is a director of centile Bioscience Inc. LR, TE, BGS, KK, AGK, HJM, PJH, and TB don't have any competing interests to declare. Funding Statement Yes Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: N/A I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Footnotes ↵* denotes joint first authorship The members of the EyeVu Consortium include Abdulhakeem Abubakar Tunde, Abdur Raafay Iqbal, Alex Lawrence, Andrea Cuschieri, Antonia Vogt, Anyela Flor Bruno Peña, Ayda Lazemi, Blendi Bylygbashi, Charles Britton, Chiara Spezzani, Christos Antonopoulos, Daniel Black Boada, Daniel Shao, Dipanshu Gandhi, Ekwegbara Somtochukwu Mitchel, Elena Maerz Engstler, Emmanuel Chileshe Phiri, Geneviève Endalle, Ghina Hussain, Kassim Omar Kassim, Kehinde Alare, Kübra Tamer, Leona Takeuchi, Makinah Haq, Marwa SaedAli Emhemed, Mubarak Mustapha Jolayemi, Muhammad Iqbal Aniq, Nagheli Fernanda Borjas-Calderón, Ngepgou Beckline Tazoah, Nneka Lilian Amakom, O. Joshua Sokan, Olaoluwa Ezekiel Dada, Olobatoke Tunde Ayomide, Oloruntoba Ogunfolaji, Phupha Amornkijja, Razan Eid, Roshen Sidhu, Rushi Patel, Shodip Shrestha, Sruthi Ranganathan, Tangmi Djabo Eric Adrien, Temitayo Ayantayo, Tom Wilkins, Weng Tong Wu, Wesley Barrett, Zafer Utku Ulker Data Availability N/A