Anterior uveitis can be an early indicator of Axial SpA, a systemic rheumatic disease. Timely diagnosis is key to managing Axial SpA and preventing long-term complications.The key acronym is SPINE:Symptoms starting slowlyPain in the lower backImproves with movementNight time wakingEarly onset (under 40)This video accompanied the fantastic audit by @livuni medical student Timothy Boey. We were incredibly proud to see Timothy present his findings at the prestigious Royal College of Ophthalmologists Annual Congress in Liverpool!Myself and other Ophthalmology colleagues in the country are working to raise awareness and enhance patient care pathways. Early detection can make a world of difference #shorts #ophthalmology #rheumatology

🔎Eye Spy: Unveiling Systemic Secrets

The eyes aren't just windows to the soul, they're a window to our overall health! Thanks to #Optometry Today for inviting me as a Medical Ophthalmologist to talk about #Oculomics - the revolutionary field using eye exams to detect systemic diseases decades early.

Optometrists are key to this, with their unique skills and patient access.

Learn how eye tests can reveal signs of conditions like cardiovascular disease, and how AI is accelerating research in this exciting new era of healthcare:

https://www.aop.org.uk/ot/videos/2024/12/10/oculomics-a-window-to-overall-health

Up, up and away – clearing the clouds…

Bringing clarity to Uveitis Diagnosis with AI:

It’s a pleasure to share this joint Nature EYE editorial with Peter Thomas in which we survey how artificial neural networks (ANNs) might be used to enhance the diagnosis of uveitis, a significant cause of vision loss worldwide:

https://www.nature.com/articles/s41433-024-03588-y

The Challenge: Uveitis, an intraocular inflammatory condition, presents a complex diagnostic puzzle due to its myriad causes and varied clinical presentations. Traditional diagnostic methods, often reliant on subjective clinical judgment, can lead to delays and inaccuracies.

Collaboration: The partnership of data scientists and clinicians holds the key to unlocking the full potential of AI and machine learning in ophthalmology. We review the state of play to accompany this paper from Jacquot et al (https://www.nature.com/articles/s41433-024-03530-2) who have used a multilayer perceptron (MLP) model, to predict the underlying causes of uveitis. Their model, trained on a dataset of 375 patients, demonstrated impressive accuracy, matching expert opinion in 77.8% of cases and achieving 93% accuracy within the top two probable diagnoses.

We’re striving towards a more objective and standardised approach to uveitis diagnosis, and these models can help – particularly with diagnostic decision support systems (DDSS) which can complement (but not replace) clinicians – hopefully streamlining the clinical workflow and reducing diagnostic errors. The DDSS in the paper we feature focuses on diagnostically challenging cases (ideally providing valuable support where it's most needed…)

While challenges remain in terms of geographic transferability, under-representation of rare aetiologies, and integration into electronic medical records, the potential of this technology is undeniable. Future research will focus on validating these tools across diverse populations and refining them for seamless integration into clinical practice.