Papilledema is a pathology delineated by the swelling of the optic disc secondary to raised intracranial pressure (ICP). Diagnosis by ophthalmoscopy can be useful in the timely stratification of further investigations, such as magnetic resonance imaging or computed tomography to rule out pathologies associated with raised ICP. In resource-limited settings, in particular, access to trained specialists or radiological imaging may not always be readily available, and accurate fundoscopy-based identification of papilledema could be a useful tool for triage and escalation to tertiary care centres. Artificial intelligence (AI) has seen a rise in neuro-ophthalmology research in recent years, but there are many barriers to the translation of AI to clinical practice. The objective of this systematic review is to garner and present a comprehensive overview of the existing evidence on the application of AI in ophthalmoscopy for papilledema, and to provide a valuable perspective on this emerging field that sits at the intersection of clinical medicine and computer science, highlighting possible avenues for future research in this domain.
Keyphrases
- artificial intelligence
- deep learning
- computed tomography
- magnetic resonance imaging
- systematic review
- machine learning
- big data
- tertiary care
- clinical practice
- high resolution
- emergency department
- public health
- meta analyses
- positron emission tomography
- optic nerve
- contrast enhanced
- open label
- current status
- randomized controlled trial
- clinical trial
- resistance training
- body composition
- diffusion weighted imaging
- image quality
- dual energy
- photodynamic therapy